Package 'seminr' reference manual

Title:	Building and Estimating Structural Equation Models
Description:	A powerful, easy to syntax for specifying and estimating complex Structural Equation Models. Models can be estimated using Partial Least Squares Path Modeling or Covariance-Based Structural Equation Modeling or covariance based Confirmatory Factor Analysis. Methods described in Ray, Danks, and Valdez (2021).
Authors:	Soumya Ray [aut, ths], Nicholas Patrick Danks [aut, cre], André Calero Valdez [aut], Juan Manuel Velasquez Estrada [ctb], James Uanhoro [ctb], Johannes Nakayama [ctb], Lilian Koyan [ctb], Laura Burbach [ctb], Arturo Heynar Cano Bejar [ctb], Susanne Adler [ctb]
Maintainer:	Nicholas Patrick Danks <[email protected]>
License:	GPL-3
Version:	2.3.1
Built:	2025-02-10 04:45:25 UTC
Source:	https://github.com/sem-in-r/seminr

Converts all contructs of a measurement model, or just a single construct into reflective factors.

Description

Converts all contructs of a measurement model, or just a single construct into reflective factors.

Usage

as.reflective(x, ...)
as.reflective(x, ...)

Arguments

`x`	A measurement model defined by `constructs` or a single composite construct defined by `composite`
`...`	Any further parameters for the specific construct.

Value

A list of reflective constructs.

Examples


mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2))
)

new_mm <- as.reflective(mobi_mm)

mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2))
)

new_mm <- as.reflective(mobi_mm)

Converts a contruct of a measurement model into a reflective factor.

Description

Converts a contruct of a measurement model into a reflective factor.

Usage

## S3 method for class 'construct'
as.reflective(x, ...)
## S3 method for class 'construct'
as.reflective(x, ...)

Arguments

`x`	A measurement model defined by `constructs` or a single composite construct defined by `composite`
`...`	Any further parameters for the specific construct.

Value

A list of reflective constructs.

Examples


mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2))
)

new_mm <- as.reflective(mobi_mm)

mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2))
)

new_mm <- as.reflective(mobi_mm)

Converts interaction of a measurement model into a reflective factors.

Description

Converts interaction of a measurement model into a reflective factors.

Usage

## S3 method for class 'interaction'
as.reflective(x, ...)
## S3 method for class 'interaction'
as.reflective(x, ...)

Arguments

`x`	A measurement model defined by `constructs` or a single composite construct defined by `composite`
`...`	Any further parameters for the specific construct.

Value

A list of reflective constructs.

Examples


mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2))
)

new_mm <- as.reflective(mobi_mm)

mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2))
)

new_mm <- as.reflective(mobi_mm)

Converts all contructs of a measurement model, or just a single construct into reflective factors.

Description

Converts all contructs of a measurement model, or just a single construct into reflective factors.

Usage

## S3 method for class 'measurement_model'
as.reflective(x, ...)
## S3 method for class 'measurement_model'
as.reflective(x, ...)

Arguments

`x`	A measurement model defined by `constructs` or a single composite construct defined by `composite`
`...`	Any further parameters for the specific construct.

Value

A list of reflective constructs.

Examples


mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2))
)

new_mm <- as.reflective(mobi_mm)

mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2))
)

new_mm <- as.reflective(mobi_mm)

Specifies inter-item covariances that should be supplied to CBSEM estimation (`estimate_cbsem`) or CFA estimation (`estimate_cfa`)

Description

Specifies inter-item covariances that should be supplied to CBSEM estimation (estimate_cbsem) or CFA estimation (estimate_cfa)

Usage

associations(...)
associations(...)

Arguments

...

One or more associations defined by item_errors

Value

A matrix of items that covary.

Examples

covaries <- associations(
  item_errors(c("a1", "a2"), c("b1", "b2")),
  item_errors("a3", "c3")
)

covaries <- associations(
  item_errors(c("a1", "a2"), c("b1", "b2")),
  item_errors("a3", "c3")
)

Return all path bootstraps as a long dataframe. Columns of the dataframes are specified paths and rows are the estimated coefficients for the paths at each bootstrap iteration.

Description

Return all path bootstraps as a long dataframe. Columns of the dataframes are specified paths and rows are the estimated coefficients for the paths at each bootstrap iteration.

Usage

boot_paths_df(pls_boot)
boot_paths_df(pls_boot)

Arguments

pls_boot

bootstrapped PLS model

Examples

data(mobi)

mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Satisfaction", multi_items("CUSA", 1:3))
)

mobi_sm <- relationships(
  paths(from = c("Image", "Expectation"), to = "Satisfaction")
)

pls_model <- estimate_pls(data = mobi,
                          measurement_model = mobi_mm,
                          structural_model = mobi_sm)

pls_boot <- bootstrap_model(seminr_model = pls_model,
                            nboot = 50, cores = 2, seed = NULL)

boot_paths_df(pls_boot)

data(mobi)

mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Satisfaction", multi_items("CUSA", 1:3))
)

mobi_sm <- relationships(
  paths(from = c("Image", "Expectation"), to = "Satisfaction")
)

pls_model <- estimate_pls(data = mobi,
                          measurement_model = mobi_mm,
                          structural_model = mobi_sm)

pls_boot <- bootstrap_model(seminr_model = pls_model,
                            nboot = 50, cores = 2, seed = NULL)

boot_paths_df(pls_boot)

seminr bootstrap_model Function

Description

The seminr package provides a natural syntax for researchers to describe PLS structural equation models. bootstrap_model provides the verb for bootstrapping a pls model from the model parameters and data.

Usage

bootstrap_model(seminr_model, nboot = 500, cores = NULL, seed = NULL, ...)
bootstrap_model(seminr_model, nboot = 500, cores = NULL, seed = NULL, ...)

Arguments

`seminr_model`	A fully estimated model with associated data, measurement model and structural model
`nboot`	A parameter specifying the number of bootstrap iterations to perform, default value is 500. If 0 then no bootstrapping is performed.
`cores`	A parameter specifying the maximum number of cores to use in the parallelization.
`seed`	A parameter to specify the seed for reproducibility of results. Default is NULL.
`...`	A list of parameters passed on to the estimation method.

Value

A list of the estimated parameters for the bootstrapped model including:

`boot_paths`	An array of the 'nboot' estimated bootstrap sample path coefficient matrices.
`boot_loadings`	An array of the 'nboot' estimated bootstrap sample item loadings matrices.
`boot_weights`	An array of the 'nboot' estimated bootstrap sample item weights matrices.
`boot_HTMT`	An array of the 'nboot' estimated bootstrap sample model HTMT matrices.
`boot_total_paths`	An array of the 'nboot' estimated bootstrap sample model total paths matrices.
`paths_descriptives`	A matrix of the bootstrap path coefficients and standard deviations.
`loadings_descriptives`	A matrix of the bootstrap item loadings and standard deviations.
`weights_descriptives`	A matrix of the bootstrap item weights and standard deviations.
`HTMT_descriptives`	A matrix of the bootstrap model HTMT and standard deviations.
`total_paths_descriptives`	A matrix of the bootstrap model total paths and standard deviations.

References

Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd Ed., Sage: Thousand Oaks.

Examples

data(mobi)
# seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3)),
  interaction_term(iv = "Image", moderator = "Expectation", method = orthogonal),
  interaction_term(iv = "Image", moderator = "Value", method = orthogonal)
)

# structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                 "Image*Expectation", "Image*Value"))
)

seminr_model <- estimate_pls(data = mobi,
                             measurement_model = mobi_mm,
                             structural_model = mobi_sm)

# Load data, assemble model, and bootstrap
boot_seminr_model <- bootstrap_model(seminr_model = seminr_model,
                                     nboot = 50, cores = 2, seed = NULL)

summary(boot_seminr_model)
data(mobi)
# seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3)),
  interaction_term(iv = "Image", moderator = "Expectation", method = orthogonal),
  interaction_term(iv = "Image", moderator = "Value", method = orthogonal)
)

# structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                 "Image*Expectation", "Image*Value"))
)

seminr_model <- estimate_pls(data = mobi,
                             measurement_model = mobi_mm,
                             structural_model = mobi_sm)

# Load data, assemble model, and bootstrap
boot_seminr_model <- bootstrap_model(seminr_model = seminr_model,
                                     nboot = 50, cores = 2, seed = NULL)

summary(boot_seminr_model)

Open Edotor graphViz Website with the preloaded in the Browser

Description

Open Edotor graphViz Website with the preloaded in the Browser

Usage

browse_plot(model, theme = seminr_theme_get())
browse_plot(model, theme = seminr_theme_get())

Arguments

`model`	A SEMinR Model
`theme`	An optional SEMinR theme

Examples

## Not run: 
browse_plot(model)

## End(Not run)
## Not run: 
browse_plot(model)

## End(Not run)

A function to create regression plots (maybe not needed?)

Description

A function to create regression plots (maybe not needed?)

Usage

check_test_plot(plot, title, plot_dir = "regression_plots", refresh = FALSE)
check_test_plot(plot, title, plot_dir = "regression_plots", refresh = FALSE)

Arguments

`plot`	the plot
`title`	a unique title
`plot_dir`	optional directory name
`refresh`	whether to refresh all test cases

Value

TRUE if plots were the same, FALSE if they did not exist, or failed

Composite construct measurement model specification

Description

composite creates the composite measurement model matrix for a specific construct, specifying the relevant items of the construct and assigning the relationship of either correlation weights (Mode A) or regression weights (Mode B).

Usage

composite(construct_name, item_names,weights = correlation_weights)
composite(construct_name, item_names,weights = correlation_weights)

Arguments

`construct_name`	of construct
`item_names`	returned by the `multi_items` or `single_item` functions
`weights`	is the relationship between the construct and its items. This can be specified as `correlation_weights` or `mode_A` for correlation weights (Mode A) or as `regression_weights` or `mode_B` for regression weights (Mode B). Default is correlation weights.

Details

This function conveniently maps composite defined measurement items to a construct and is estimated using PLS.

Value

A vector of the indicators for a composite.

Examples

  mobi_mm <- constructs(
    composite("Image",        multi_items("IMAG", 1:5), weights = correlation_weights),
    composite("Expectation",  multi_items("CUEX", 1:3), weights = mode_A),
    composite("Quality",      multi_items("PERQ", 1:7), weights = regression_weights),
    composite("Value",        multi_items("PERV", 1:2), weights = mode_B)
  )
mobi_mm <- constructs(
    composite("Image",        multi_items("IMAG", 1:5), weights = correlation_weights),
    composite("Expectation",  multi_items("CUEX", 1:3), weights = mode_A),
    composite("Quality",      multi_items("PERQ", 1:7), weights = regression_weights),
    composite("Value",        multi_items("PERV", 1:2), weights = mode_B)
  )

Function to calculate Akaike weights for IT Criteria

Description

Function to calculate Akaike weights for IT Criteria

Usage

compute_itcriteria_weights(vector_of_itcriteria)
compute_itcriteria_weights(vector_of_itcriteria)

Arguments

vector_of_itcriteria

This argument is a vector consisting of the IT criterion estimated value for each model.

Measurement functions

Description

constructs creates the constructs from measurement items by assigning the relevant items to each construct and specifying reflective or formative (composite/causal) measurement models

Usage

constructs(...)
constructs(...)

Arguments

...

Comma separated list of the construct variable measurement specifications, as generated by the reflective(), or composite() methods.

Details

This function conveniently maps measurement items to constructs using root name, numbers, and affixes with explicit definition of formative or reflective relationships

Value

A list of constructs, their indicators and estimation technique (SEMinR measurement model).

Examples

  mobi_mm <- constructs(
    reflective("Image",        multi_items("IMAG", 1:5)),
    reflective("Expectation",  multi_items("CUEX", 1:3)),
    reflective("Quality",      multi_items("PERQ", 1:7)),
    reflective("Value",        multi_items("PERV", 1:2)),
    reflective("Satisfaction", multi_items("CUSA", 1:3)),
    reflective("Complaints",   single_item("CUSCO")),
    reflective("Loyalty",      multi_items("CUSL", 1:3))
  )
mobi_mm <- constructs(
    reflective("Image",        multi_items("IMAG", 1:5)),
    reflective("Expectation",  multi_items("CUEX", 1:3)),
    reflective("Quality",      multi_items("PERQ", 1:7)),
    reflective("Value",        multi_items("PERV", 1:2)),
    reflective("Satisfaction", multi_items("CUSA", 1:3)),
    reflective("Complaints",   single_item("CUSCO")),
    reflective("Loyalty",      multi_items("CUSL", 1:3))
  )

Returns R-sq of a dv given correlation matrix of ivs, dv cors <- cbsem_summary$descriptives$correlations$constructs cor_rsq(cors, dv_name = "Value", iv_names = c("Image", "Quality"))

Description

Returns R-sq of a dv given correlation matrix of ivs, dv cors <- cbsem_summary$descriptives$correlations$constructs cor_rsq(cors, dv_name = "Value", iv_names = c("Image", "Quality"))

Usage

cor_rsq(cor_matrix, dv_name, iv_names)
cor_rsq(cor_matrix, dv_name, iv_names)

Arguments

`cor_matrix`	A correlation matrix that includes ivs and dv
`dv_name`	Character string of dependent variable
`iv_names`	Vector of character strings for independent variables

Measurement Instrument for the Corporate Reputation Model

Description

The data set is used as measurement instrument for corporate reputation.

Usage

corp_rep_data
corp_rep_data

Format

A data frame with 344 rows and 46 variables:

serviceprovider: A categorical variable for the service provider: 1, 2, 3, or 4.
servicetype: A categorical variable for the service type: 1=Prepaid plan (n=125); 2=Contract plan (n=219).
csor_1: The company behaves in a socially conscious way.
csor_2: The company is forthright in giving information to the public.
csor_3: The company has a fair attitude toward competitors.
csor_4: The company is concerned about the preservation of the environment.
csor_5: The company is not only concerned about the profits.
csor_global: Please assess the extent to which the company acts in socially conscious ways 0 (not at all) to 7 (definitely).
attr_1: The company is succesful in attracting high-quality employees.
attr_2: I could see myself working at the company.
attr_3: I like the physical appearance of the company/buildings/shops, etc.
attr_global: Please assess the company’s overall attractiveness; 0=very low; 7=very high.
perf_1: The company is a very well managed company.
perf_2: The company is an economically stable company.
perf_3: The business risk for the company is modest compared to its competitors.
perf_4: The company has growth potential.
perf_5: The company has a clear vision about the future of the company.
perf_global: Please assess the company’s overall performance; 0=very low; 7=very high.
qual_1: The products/services offered by the company are of high quality.
qual_2: The company is an innovator, rather than an imitator with respect to industry.
qual_3: The company's services/products offer good quality for money.
qual_4: The services the company offered are good.
qual_5: Customer concerns are held in high regard at the company.
qual_6: The company is a reliable partner for customers.
qual_7: The company is a trustworthy company.
qual_8: I have a lot of respect for the company.
qual_global: Please assess the overall quality of the company’s activities; 0=very low; 7=very high.
like_1: The company is a copany that I can better identify with than other companies.
like_2: The company is a company that I would regret more not having if it no longer existed than other companies.
like_3: I regard the company as a likeable company.
comp_1: The company is a top competitor in its market.
comp_2: As far as I know, the company is recognized worldwide.
comp_3: I believe that the company performs at a premium level.
cusl_1: I would recommend the company to friends and relatives.
cusl_2: If I had to choose again, I would choose the company as my mobile phone services provider.
cusl_3: I will remain a customer of the company in the future.
cusa: I am satisfied with company.
age: ?
education: Categorical for education.
occupation: Categorical for type of occupation.
nphh: ?
sample_type: ?
mga_1: Multi Group Analysis 1.
mga_2: Multi Group Analysis 2.
mga_3: Multi Group Analysis 3.
mga_4: Multi Group Analysis 4.
switch_1: It takes me a great deal of time to switch to another company.
switch_2: It costs me too much to switch to another company.
switch_3: It takes a lot of effort to get used to a new company with its specific "rules" and practices.
switch_4: In general, it would be a hassle switching to another company.

Details

The data frame mobi contains the observed data for the model specified by Corporate Reputation.

References

Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (2nd ed.). Thousand Oakes, CA: Sage.

Examples

data("corp_rep_data")

data("corp_rep_data")

A Second Measurement Instrument for the Corporate Reputation Model

Description

The data set is used as measurement instrument for corporate reputation.

Usage

corp_rep_data2
corp_rep_data2

Format

A data frame with 347 rows and 49 variables:

servicetype: A categorical variable for the service type: 1=Postpaid plan; 2=Prepaid plan.
serviceprovider: A categorical variable for the service provider: 1, 2, 3, or 4.
cusa: If you consider your experiences with "company", how satisfied are you with "company"?
cusl_1: I would recommend "the company" to friends and relatives.
cusl_2: If I had to choose again, I would choose "the company" as my mobile phone services provider.
cusl_3: I will remain a customer of "the company" in the future.
qual_1: The products/services offered by "the company" are of high quality.
qual_2: "The company" is an innovator, rather than an imitator with respect to the mobile phone service industry.
qual_3: "The company's" services/products offer good quality for money.
qual_4: The services "the company" offers are good.
qual_5: Customer concerns are held in high regard at "the company".
qual_6: "The company" is a reliable partner for customers.
qual_7: "The company" is a trustworthy company.
qual_8: I have a lot of respect for "the company".
perf_1: "The company" is a very well managed company.
perf_2: "The company" is an economically stable company.
perf_3: The business risk of "the company" is reasonable compared to its competitors.
perf_4: The growth of "the company" is promising.
perf_5: "The company" has a clear vision about the future of the company.
csor_1: "The company" behaves in a socially conscious way.
csor_2: "The company" is honest in giving information to the public.
csor_3: "The company" competes fairly in the indsutry.
csor_4: "The company" cares for the preservation of the environment.
csor_5: "The company" is doing more than just making profits.
attr_1: "The company" is succesful in attracting high-quality employees.
attr_2: I could see myself working at "the company".
attr_3: I like the physical appearance of "the company" (company/buildings/shops, etc.).
comp_1: "The company" is a top competitor in its market.
comp_2: As far as I know, "the company" is recognized worldwide.
comp_3: I believe that "the company" performs at a premium level.
like_1: "The company" is a company that I can better identify with than other companies.
like_2: When comparing with other companies, "The company" is the company I would regret more if it no longer existed.
like_3: I regard "the company" as a likeable company.
qual_global: Please assess the general quality of "the company".
perf_global: Please assess the general performance of "the company".
csor_global: Please assess the extent to which "the company" acts in socially conscious ways.
attr_global: Please assess the attractiveness of "the company".
switch_1: It takes me a great deal of time to switch to another mobile phone services provider.
switch_2: It costs me too much to switch to another mobile phone services provider.
switch_3: It takes a lot of effort to get used to a new mobile phone services provider with its specific "rules" and practices.
switch_4: In general, it would be a hassle switching to another mobile phone services provider.

Details

The data frame mobi contains the observed data for the model specified by Corporate Reputation.

References

Sarstedt, M., Hair Jr, J. F., Cheah, J. H., Becker, J. M., & Ringle, C. M. (2019). How to specify, estimate, and validate higher-order constructs in PLS-SEM. Australasian Marketing Journal (AMJ), 27(3), 197-211.

Examples

data("corp_rep_data2")

data("corp_rep_data2")

seminr csem2seminr() function

Description

Converts lavaan syntax for composite models used by cSEM package to SEMinR model specifications

Usage

csem2seminr(lav_syntax)
csem2seminr(lav_syntax)

Arguments

lav_syntax

A string specifying the composite model measurement and structure using lavaan syntax

Value

A SEMinR model.

Examples

lav_syntax <- '
  Composite model
  Image <~ IMAG1 + IMAG2 + IMAG3 + IMAG4 + IMAG5
  Expectation <~ CUEX1 + CUEX2 + CUEX3
  Value  <~ PERV1  + PERV2
  Satisfaction <~ CUSA1 + CUSA2 + CUSA3

  # Structural model
  Satisfaction ~ Image + Expectation + Value
'

csem_model <- estimate_pls(mobi, model = csem2seminr(lav_syntax))

lav_syntax <- '
  Composite model
  Image <~ IMAG1 + IMAG2 + IMAG3 + IMAG4 + IMAG5
  Expectation <~ CUEX1 + CUEX2 + CUEX3
  Value  <~ PERV1  + PERV2
  Satisfaction <~ CUSA1 + CUSA2 + CUSA3

  # Structural model
  Satisfaction ~ Image + Expectation + Value
'

csem_model <- estimate_pls(mobi, model = csem2seminr(lav_syntax))

Cross-tabulates columns of a dataframe into a matrix with NAs for unspecified pairs

Description

Cross-tabulates columns of a dataframe into a matrix with NAs for unspecified pairs

Usage

df_xtab_matrix(model, df, rows, columns)
df_xtab_matrix(model, df, rows, columns)

Arguments

`model`	A `formula` indicating relevant columns from data frame
`df`	A `data.frame` of columns to cross-tabulate
`rows`	A `vector` of row names for the matrix to sort by
`columns`	A `vector` of column names for the matrix to sort by

Value

A cross-tabulated matrix matrix with NAs for unspecified pairs.

Generates the dot code for the measurement model

Description

Generates the dot code for the measurement model

Usage

dot_component_mm(model, theme)
dot_component_mm(model, theme)

Arguments

`model`	the model to use
`theme`	the theme to use

Generate a dot graph from various SEMinR models

Description

With the help of the DiagrammeR package this dot graph can then be plotted in various in RMarkdown, shiny, and other contexts. Depending on the type of model, different parameters can be used.

For a full description of parameters for lavaan models see semPaths method in the semPlot package.

Usage

dot_graph(model, title = "", theme = NULL, ...)

## S3 method for class 'cfa_model'
dot_graph(
  model,
  title = "",
  theme = NULL,
  what = "std",
  whatLabels = "std",
  ...
)

## S3 method for class 'cbsem_model'
dot_graph(
  model,
  title = "",
  theme = NULL,
  what = "std",
  whatLabels = "std",
  ...
)

## S3 method for class 'measurement_model'
dot_graph(model, title = "", theme = NULL, ...)

## S3 method for class 'structural_model'
dot_graph(model, title = "", theme = NULL, ...)

## S3 method for class 'specified_model'
dot_graph(
  model,
  title = "",
  theme = NULL,
  measurement_only = FALSE,
  structure_only = FALSE,
  ...
)

## S3 method for class 'boot_seminr_model'
dot_graph(
  model,
  title = "",
  theme = NULL,
  measurement_only = FALSE,
  structure_only = FALSE,
  ...
)

## S3 method for class 'pls_model'
dot_graph(
  model,
  title = "",
  theme = NULL,
  measurement_only = FALSE,
  structure_only = FALSE,
  ...
)
dot_graph(model, title = "", theme = NULL, ...)

## S3 method for class 'cfa_model'
dot_graph(
  model,
  title = "",
  theme = NULL,
  what = "std",
  whatLabels = "std",
  ...
)

## S3 method for class 'cbsem_model'
dot_graph(
  model,
  title = "",
  theme = NULL,
  what = "std",
  whatLabels = "std",
  ...
)

## S3 method for class 'measurement_model'
dot_graph(model, title = "", theme = NULL, ...)

## S3 method for class 'structural_model'
dot_graph(model, title = "", theme = NULL, ...)

## S3 method for class 'specified_model'
dot_graph(
  model,
  title = "",
  theme = NULL,
  measurement_only = FALSE,
  structure_only = FALSE,
  ...
)

## S3 method for class 'boot_seminr_model'
dot_graph(
  model,
  title = "",
  theme = NULL,
  measurement_only = FALSE,
  structure_only = FALSE,
  ...
)

## S3 method for class 'pls_model'
dot_graph(
  model,
  title = "",
  theme = NULL,
  measurement_only = FALSE,
  structure_only = FALSE,
  ...
)

Arguments

`model`	Model created with `seminr`.
`title`	An optional title for the plot
`theme`	Theme created with `seminr_theme_create`.
`...`	Unused
`what`	The metric to use for edges ("path", "est", "std", "eq", "col")
`whatLabels`	The metric to use for edge labels
`measurement_only`	Plot only measurement part
`structure_only`	Plot only structure part

Details

Current limitations: - Only plots PLS Models - no higher order constructs

Value

The path model as a formatted string in dot language.

Examples

mobi <- mobi

#seminr syntax for creating measurement model
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )
#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm)

# adapt nboot for better results
mobi_boot <- bootstrap_model(mobi_pls, nboot = 20, cores = 1)
# generate dot-Notation
res <- dot_graph(mobi_pls, title = "PLS-Model plot")

## Not run: 
DiagrammeR::grViz(res)
## End(Not run)

# generate dot-Notation
res <- dot_graph(mobi_boot, title = "Bootstrapped PLS-Model plot")

## Not run: 
DiagrammeR::grViz(res)
## End(Not run)

# - - - - - - - - - - - - - - - -
# Example for plotting a measurement model
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )
dot_graph(mobi_mm, title = "Preview measurement model")
# - - - - - - - - - - - - - - - -
# Example for plotting a structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)
res <- dot_graph(mobi_sm, title = "Preview structural model")

## Not run: 
DiagrammeR::grViz(res)

## End(Not run)
mobi <- mobi

#seminr syntax for creating measurement model
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )
#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm)

# adapt nboot for better results
mobi_boot <- bootstrap_model(mobi_pls, nboot = 20, cores = 1)
# generate dot-Notation
res <- dot_graph(mobi_pls, title = "PLS-Model plot")

## Not run: 
DiagrammeR::grViz(res)
## End(Not run)

# generate dot-Notation
res <- dot_graph(mobi_boot, title = "Bootstrapped PLS-Model plot")

## Not run: 
DiagrammeR::grViz(res)
## End(Not run)

# - - - - - - - - - - - - - - - -
# Example for plotting a measurement model
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )
dot_graph(mobi_mm, title = "Preview measurement model")
# - - - - - - - - - - - - - - - -
# Example for plotting a structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)
res <- dot_graph(mobi_sm, title = "Preview structural model")

## Not run: 
DiagrammeR::grViz(res)

## End(Not run)

Creates a dot string with a network graph of constructs based on HTMT measures

Description

Using a bootstrapped model this functions shows which constructs show insufficient discriminant validity.

Usage

dot_graph_htmt(
  model,
  title = "HTMT Plot",
  theme = seminr::seminr_theme_get(),
  htmt_threshold = 1,
  omit_threshold_edges = TRUE,
  use_ci = FALSE
)
dot_graph_htmt(
  model,
  title = "HTMT Plot",
  theme = seminr::seminr_theme_get(),
  htmt_threshold = 1,
  omit_threshold_edges = TRUE,
  use_ci = FALSE
)

Arguments

`model`	A bootsrapped PLS-Model
`title`	Optional title over the plot.
`theme`	Optional theme to use for plotting
`htmt_threshold`	The threshold to use under which constructs are highlighted (default = 1.0)
`omit_threshold_edges`	Whether or not to omit constructs that have low HTMT values (default = TRUE)
`use_ci`	Whether or not to rely on the upper threshold of the CI instead of the bootstrapped mean (default = FALSE)

Value

Returs a dot string of the plot

generates the dot code for a subgraph (per construct)

Description

generates the dot code for a subgraph (per construct)

Usage

dot_subcomponent_mm(index, model, theme)
dot_subcomponent_mm(index, model, theme)

Arguments

`index`	the index of the construct
`model`	the model to use
`theme`	the theme to use

The default template for labeling bootstrapped edges

Description

The default template for labeling bootstrapped edges

Usage

edge_template_default()
edge_template_default()

Value

The template string

A minimal template for labeling bootstrapped edges that only shows the bootstrapped mean value

Description

A minimal template for labeling bootstrapped edges that only shows the bootstrapped mean value

Usage

edge_template_minimal()
edge_template_minimal()

Value

The template string

Wrap a text in single quotes

Description

Wrap a text in single quotes

Usage

esc_node(x)
esc_node(x)

Arguments

`x`	a character string

seminr estimate_cbsem() function

Description

The seminr package provides a natural syntax for researchers to describe structural equation models.

Usage

estimate_cbsem(data, measurement_model = NULL,
               structural_model = NULL, item_associations = NULL,
               model = NULL, lavaan_model = NULL, estimator = "MLR", ...)
estimate_cbsem(data, measurement_model = NULL,
               structural_model = NULL, item_associations = NULL,
               model = NULL, lavaan_model = NULL, estimator = "MLR", ...)

Arguments

`data`	A `dataframe` containing the indicator measurement data. The entire CBSEM model can be specified in one of three ways: The pair of measurement and structural models, along associated items, can optionally be specified as separate model components
`measurement_model`	An optional `measurement_model` object representing the outer/measurement model, as generated by `constructs`. Note that only reflective constructs are supported for CBSEM models, though a composite measurement model can be converted into a reflective one using `as.reflective`.
`structural_model`	An optional `smMatrix` object representing the inner/structural model, as generated by `relationships`.
`item_associations`	An item-to-item matrix representing error covariances that are freed for estimation. This matrix is created by `associations()`, or defaults to NULL (no inter-item associations). The combination of measurement and structural models and inter-item associations can also be specified as a single `specified_model` object Note that any given model components (measurement_model, structural_model, item_associations) will override components in the fully specified model
`model`	An optional `specified_model` object containing both the the outer/measurement and inner/structural models, along with any inter-item associations, as generated by `specify_model`. The entire model can also be specified in Lavaan syntax (this overrides any other specifications)
`lavaan_model`	Optionally, a single character string containing the relevant model specification in `lavaan` syntax. Any further optional parameters to alter the estimation method:
`estimator`	A character string indicating which estimation method to use in Lavaan. It defaults to "MLR" for robust estimation. See the Lavaan documentation for other supported estimators.
`...`	Any other parameters to pass to `lavaan::sem` during estimation.

Value

A list of the estimated parameters for the CB-SEM model including:

`data`	A matrix of the data upon which the model was estimated.
`measurement_model`	The SEMinR measurement model specification.
`factor_loadings`	The matrix of estimated factor loadings.
`associations`	A matrix of model variable associations.
`mmMatrix`	A Matrix of the measurement model relations.
`smMatrix`	A Matrix of the structural model relations.
`constructs`	A vector of the construct names.
`construct scores`	A matrix of the estimated construct scores for the CB-SEM model.
`item_weights`	A matrix of the estimated CFA item weights.
`lavaan_model`	The lavaan model syntax equivalent of the SEMinR model.
`lavaan_output`	The raw lavaan output generated after model estimation.

References

Joreskog, K. G. (1973). A general method for estimating a linear structural equation system In: Goldberger AS, Duncan OD, editors. Structural Equation Models in the Social Sciences. New York: Seminar Press.

Examples

mobi <- mobi

#seminr syntax for creating measurement model
mobi_mm <- constructs(
  reflective("Image",        multi_items("IMAG", 1:5)),
  reflective("Quality",      multi_items("PERQ", 1:7)),
  reflective("Value",        multi_items("PERV", 1:2)),
  reflective("Satisfaction", multi_items("CUSA", 1:3)),
  reflective("Complaints",   single_item("CUSCO")),
  reflective("Loyalty",      multi_items("CUSL", 1:3))
)

#seminr syntax for freeing up item-item covariances
mobi_am <- associations(
  item_errors(c("PERQ1", "PERQ2"), "IMAG1")
)

#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = c("Image", "Quality"), to = c("Value", "Satisfaction")),
  paths(from = c("Value", "Satisfaction"), to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

# Estimate model and get results
mobi_cbsem <- estimate_cbsem(mobi, mobi_mm, mobi_sm, mobi_am)

# Use or capture the summary object for more results and metrics
summary(mobi_cbsem)

cbsem_summary <- summary(mobi_cbsem)
cbsem_summary$descriptives$correlations$constructs

mobi <- mobi

#seminr syntax for creating measurement model
mobi_mm <- constructs(
  reflective("Image",        multi_items("IMAG", 1:5)),
  reflective("Quality",      multi_items("PERQ", 1:7)),
  reflective("Value",        multi_items("PERV", 1:2)),
  reflective("Satisfaction", multi_items("CUSA", 1:3)),
  reflective("Complaints",   single_item("CUSCO")),
  reflective("Loyalty",      multi_items("CUSL", 1:3))
)

#seminr syntax for freeing up item-item covariances
mobi_am <- associations(
  item_errors(c("PERQ1", "PERQ2"), "IMAG1")
)

#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = c("Image", "Quality"), to = c("Value", "Satisfaction")),
  paths(from = c("Value", "Satisfaction"), to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

# Estimate model and get results
mobi_cbsem <- estimate_cbsem(mobi, mobi_mm, mobi_sm, mobi_am)

# Use or capture the summary object for more results and metrics
summary(mobi_cbsem)

cbsem_summary <- summary(mobi_cbsem)
cbsem_summary$descriptives$correlations$constructs

seminr estimate_cfa() function

Description

Estimates a Confirmatory Factor Analysis (CFA) model

Usage

estimate_cfa(data, measurement_model = NULL, item_associations=NULL,
             model = NULL, lavaan_model = NULL, estimator="MLR", ...)
estimate_cfa(data, measurement_model = NULL, item_associations=NULL,
             model = NULL, lavaan_model = NULL, estimator="MLR", ...)

Arguments

`data`	A `dataframe` containing the indicator measurement data. The entire CBSEM model can be specified in one of three ways: The pair of measurement and structural models, along associated items, can optionally be specified as separate model components
`measurement_model`	An optional `measurement_model` object representing the outer/measurement model, as generated by `constructs`. Note that only reflective constructs are supported for CBSEM models, though a composite measurement model can be converted into a reflective one using `as.reflective`.
`item_associations`	An item-to-item matrix representing error covariances that are freed for estimation. This matrix is created by `associations()`, or defaults to NULL (no inter-item associations). The combination of measurement and structural models and inter-item associations can also be specified as a single `specified_model` object Note that any given model components (measurement_model, structural_model, item_associations) will override components in the fully specified model
`model`	An optional `specified_model` object containing both the the outer/measurement and inner/structural models, along with any inter-item associations, as generated by `specify_model`. The entire model can also be specified in Lavaan syntax (this overrides any other specifications)
`lavaan_model`	Optionally, a single character string containing the relevant model specification in `lavaan` syntax. Any further optional parameters to alter the estimation method:
`estimator`	A character string indicating which estimation method to use in Lavaan. It defaults to "MLR" for robust estimation. See the Lavaan documentation for other supported estimators.
`...`	Any other parameters to pass to `lavaan::sem` during estimation.

Value

A list of the estimated parameters for the CFA model including:

`data`	A matrix of the data upon which the model was estimated.
`measurement_model`	The SEMinR measurement model specification.
`construct scores`	A matrix of the estimated construct scores for the CB-SEM model.
`item_weights`	A matrix of the estimated CFA item weights.
`lavaan_model`	The lavaan model syntax equivalent of the SEMinR model.
`lavaan_output`	The raw lavaan output generated after model estimation.

References

Jöreskog, K.G. (1969) A general approach to confirmatory maximum likelihood factor analysis. Psychometrika, 34, 183-202.

seminr estimate_lavaan_ten_berge() function

Description

Estimates factor scores using ten Berge method for a fitted Lavaan model

Usage

estimate_lavaan_ten_berge(fit)
estimate_lavaan_ten_berge(fit)

Arguments

fit

A fitted lavaan object – can be extracted from cbesem estimation or from using Lavaan directly.

Value

A list with two elements: ten berge scores; weights for calculating scores

Examples

#' #seminr syntax for creating measurement model
mobi_mm <- constructs(
  reflective("Image",        multi_items("IMAG", 1:5)),
  reflective("Quality",      multi_items("PERQ", 1:7)),
  reflective("Value",        multi_items("PERV", 1:2)),
  reflective("Satisfaction", multi_items("CUSA", 1:3)),
  reflective("Complaints",   single_item("CUSCO")),
  reflective("Loyalty",      multi_items("CUSL", 1:3))
)

#seminr syntax for freeing up item-item covariances
mobi_am <- associations(
  item_errors(c("PERQ1", "PERQ2"), "IMAG1")
)

#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = c("Image", "Quality"), to = c("Value", "Satisfaction")),
  paths(from = c("Value", "Satisfaction"), to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

# Estimate model and get results
cbsem <- estimate_cbsem(mobi, mobi_mm, mobi_sm, mobi_am)
tb <- estimate_lavaan_ten_berge(cbsem$lavaan_output)
tb$scores
tb$weights

#' #seminr syntax for creating measurement model
mobi_mm <- constructs(
  reflective("Image",        multi_items("IMAG", 1:5)),
  reflective("Quality",      multi_items("PERQ", 1:7)),
  reflective("Value",        multi_items("PERV", 1:2)),
  reflective("Satisfaction", multi_items("CUSA", 1:3)),
  reflective("Complaints",   single_item("CUSCO")),
  reflective("Loyalty",      multi_items("CUSL", 1:3))
)

#seminr syntax for freeing up item-item covariances
mobi_am <- associations(
  item_errors(c("PERQ1", "PERQ2"), "IMAG1")
)

#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = c("Image", "Quality"), to = c("Value", "Satisfaction")),
  paths(from = c("Value", "Satisfaction"), to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

# Estimate model and get results
cbsem <- estimate_cbsem(mobi, mobi_mm, mobi_sm, mobi_am)
tb <- estimate_lavaan_ten_berge(cbsem$lavaan_output)
tb$scores
tb$weights

seminr estimate_pls() function

Description

Estimates a pair of measurement and structural models using PLS-SEM, with optional estimation methods

Usage

estimate_pls(data,
             measurement_model = NULL, structural_model = NULL, model = NULL,
             inner_weights = path_weighting,
             missing = mean_replacement,
             missing_value = NA,
             maxIt = 300,
             stopCriterion = 7)
estimate_pls(data,
             measurement_model = NULL, structural_model = NULL, model = NULL,
             inner_weights = path_weighting,
             missing = mean_replacement,
             missing_value = NA,
             maxIt = 300,
             stopCriterion = 7)

Arguments

`data`	A `dataframe` containing the manifest measurement items in named columns. The pair of measurement and structural models can optionally be specified as separate model objects
`measurement_model`	An optional `measurement_model` object representing the outer/measurement model, as generated by `constructs`.
`structural_model`	An optional `smMatrix` object representing the inner/structural model, as generated by `relationships`. The pair of measurement and structural models can also be specified as a single `specified_model` object
`model`	An optional `specified_model` object containing both the the outer/measurement and inner/structural models, as generated by `specify_model`.
`inner_weights`	Function that implements inner weighting scheme: `path_weighting` (default) or `path_factorial` can be used.
`missing`	Function that replaces missing values. `mean_replacement` is default.
`missing_value`	Value in dataset that indicates missing values. NA is used by default.
`maxIt`	A parameter that specifies that maximum number of iterations when estimating the PLS model. Default value is 300.
`stopCriterion`	A parameter specifying the stop criterion for estimating the PLS model. Default value is 7.

Value

A list of the estimated parameters for the SEMinR model including:

`meanData`	A vector of the indicator means.
`sdData`	A vector of the indicator standard deviations
`mmMatrix`	A Matrix of the measurement model relations.
`smMatrix`	A Matrix of the structural model relations.
`constructs`	A vector of the construct names.
`mmVariables`	A vector of the indicator names.
`outer_loadings`	The matrix of estimated indicator loadings.
`outer_weights`	The matrix of estimated indicator weights.
`path_coef`	The matrix of estimated structural model relationships.
`iterations`	A numeric indicating the number of iterations required before the algorithm converged.
`weightDiff`	A numeric indicating the minimum weight difference between iterations of the algorithm.
`construct_scores`	A matrix of the estimated construct scores for the PLS model.
`rSquared`	A matrix of the estimated R Squared for each construct.
`inner_weights`	The inner weight estimation function.
`data`	A matrix of the data upon which the model was estimated (INcluding interactions.
`rawdata`	A matrix of the data upon which the model was estimated (EXcluding interactions.
`measurement_model`	The SEMinR measurement model specification.

Examples

mobi <- mobi

#seminr syntax for creating measurement model
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )
#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm,
                         missing = mean_replacement,
                         missing_value = NA)

summary(mobi_pls)
plot_scores(mobi_pls)
mobi <- mobi

#seminr syntax for creating measurement model
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )
#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm,
                         missing = mean_replacement,
                         missing_value = NA)

summary(mobi_pls)
plot_scores(mobi_pls)

Performs PLS-MGA to report significance of path differences between two subgroups of data

Description

Performs PLS-MGA to report significance of path differences between two subgroups of data

Usage

estimate_pls_mga(pls_model, condition, nboot = 2000, ...)
estimate_pls_mga(pls_model, condition, nboot = 2000, ...)

Arguments

`pls_model`	SEMinR PLS model estimated on the full sample
`condition`	logical vector of TRUE/FALSE indicating which rows of sample data are in group 1
`nboot`	number of bootstrap resamples to use in PLS-MGA
`...`	any further parameters for bootstrapping (e.g., cores)

References

Henseler, J., Ringle, C. M. & Sinkovics, R. R. New Challenges to International Marketing. Adv Int Marketing 277–319 (2009) doi:10.1108/s1474-7979(2009)0000020014

Examples

mobi <- mobi

#seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Quality",      multi_items("PERQ", 1:7)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3)),
  composite("Complaints",   single_item("CUSCO")),
  composite("Loyalty",      multi_items("CUSL", 1:3))
)

#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm,
                         missing = mean_replacement,
                         missing_value = NA)

# Should usually use nboot ~2000 and don't specify cores for full parallel processing

mobi_mga <- estimate_pls_mga(mobi_pls, mobi$CUEX1 < 8, nboot=50, cores = 2)

mobi <- mobi

#seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Quality",      multi_items("PERQ", 1:7)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3)),
  composite("Complaints",   single_item("CUSCO")),
  composite("Loyalty",      multi_items("CUSL", 1:3))
)

#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm,
                         missing = mean_replacement,
                         missing_value = NA)

# Should usually use nboot ~2000 and don't specify cores for full parallel processing

mobi_mga <- estimate_pls_mga(mobi_pls, mobi$CUEX1 < 8, nboot=50, cores = 2)

extract bootstrapped statistics from an edge using a row_index

Description

extract bootstrapped statistics from an edge using a row_index

Usage

extract_bootstrapped_values(ltbl, row_index, model, theme)
extract_bootstrapped_values(ltbl, row_index, model, theme)

Arguments

`ltbl`	a table of bootstrapped values (weights, loadings, path coefficients)
`row_index`	the index for the specific edge to extract
`model`	the model to use
`theme`	the theme to use

Helper function that applies formatting to each construct

Description

Helper function that applies formatting to each construct

Usage

extract_htmt_nodes(model, theme)
extract_htmt_nodes(model, theme)

Arguments

`model`	the model to use
`theme`	the theme to use

Value

Returns a string of the structural model in dot notation.

extracts the constructs and their types from the model

Description

extracts the constructs and their types from the model

Usage

extract_mm_coding(model)
extract_mm_coding(model)

Arguments

model

the model to use

gets the mm_edge value (loading, weight) for bootstrapped and regular models

Description

gets the mm_edge value (loading, weight) for bootstrapped and regular models

Usage

extract_mm_edge_value(model, theme, indicator, construct)
extract_mm_edge_value(model, theme, indicator, construct)

Arguments

`model`	the model to use
`theme`	the theme to use
`indicator`	the indicator to use
`construct`	the construct to use

extract mm edges from model for a given index of all constructs

Description

extract mm edges from model for a given index of all constructs

Usage

extract_mm_edges(index, model, theme, weights = 1000)
extract_mm_edges(index, model, theme, weights = 1000)

Arguments

`index`	the index of the construct
`model`	the model to use
`theme`	the theme to use
`weights`	a default weight for measurment models (high values suggested)

gets the individual nodes and applies formatting

Description

gets the individual nodes and applies formatting

Usage

extract_mm_nodes(index, model, theme)
extract_mm_nodes(index, model, theme)

Arguments

`index`	the index of the construct
`model`	the model to use
`theme`	the theme to use

Helper function that applies formatting to each construct

Description

Helper function that applies formatting to each construct

Usage

extract_sm_nodes(model, theme, structure_only = FALSE)
extract_sm_nodes(model, theme, structure_only = FALSE)

Arguments

`model`	the model to use
`theme`	the theme to use
`structure_only`	is this called in a structure_only model

Value

Returns a string of the structural model in dot notation.

Helps to render a node label for endogenous variables

Description

Helps to render a node label for endogenous variables

Usage

format_endo_node_label(theme, name, rstring)
format_endo_node_label(theme, name, rstring)

Arguments

`theme`	the theme to use
`name`	the content of the name placeholder
`rstring`	the content of the rstring placeholder

Value

Returns the formatted string

Helps to render a node label for exogenous variables

Description

Helps to render a node label for exogenous variables

Usage

format_exo_node_label(theme, name)
format_exo_node_label(theme, name)

Arguments

`theme`	the theme to use
`name`	the content of the name placeholder

Value

Returns the formatted string

seminr fSquared Function

Description

The fSquared function calculates f^2 effect size for a given IV and DV

Usage

fSquared(seminr_model, iv, dv)
fSquared(seminr_model, iv, dv)

Arguments

`seminr_model`	A `seminr_model` containing the estimated seminr model.
`iv`	An independent variable in the model.
`dv`	A dependent variable in the model.

Value

A matrix of the estimated F Square metric for each construct.

References

Cohen, J. (2013). Statistical power analysis for the behavioral sciences. Routledge.

Examples

mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )

mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm)

fSquared(mobi_pls, "Image", "Satisfaction")
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )

mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm)

fSquared(mobi_pls, "Image", "Satisfaction")

Gets the optimal size for construct elements in the plot

Description

Currently orients on reflective theme settings

Usage

get_construct_element_size(model, theme)
get_construct_element_size(model, theme)

Arguments

`model`	the model to use
`theme`	the theme to use

Value

Returns a two-element vector with c(width, height)

Returns the type of a construct from a model

Description

Returns the type of a construct from a model

Usage

get_construct_type(model, construct)
get_construct_type(model, construct)

Arguments

`model`	the model to get the type from
`construct`	the character string name of the construct

Value

Returns a character string

Gets the optimal size for manifest elements in the plot

Description

Currently orients on reflective theme settings

Usage

get_manifest_element_size(model, theme)
get_manifest_element_size(model, theme)

Arguments

`model`	the model to use
`theme`	the theme to use

Value

Returns a two-element vector with c(width, height)

individual styles for measurement model edges

Description

individual styles for measurement model edges

Usage

get_mm_edge_style(theme, construct_type, flip = FALSE)
get_mm_edge_style(theme, construct_type, flip = FALSE)

Arguments

`theme`	the theme to use
`construct_type`	Forward direction?
`flip`	invert the arrow direction because of sink?

Get a string to insert into a node specification using the themed shape

Description

Get a string to insert into a node specification using the themed shape

Usage

get_mm_node_shape(model, construct, theme)
get_mm_node_shape(model, construct, theme)

Arguments

`model`	the model to use
`construct`	the construct to use
`theme`	the theme to use

Value

Returns a string that determines the shape of a node

get global measurement model node style

Description

get global measurement model node style

Usage

get_mm_node_style(theme)
get_mm_node_style(theme)

Arguments

theme

the theme to use

Get a string to insert into a node specification using the themed shape

Description

Get a string to insert into a node specification using the themed shape

Usage

get_sm_node_shape(model, construct, theme)
get_sm_node_shape(model, construct, theme)

Arguments

`model`	the model to use
`construct`	the construct to use
`theme`	the theme to use

Value

Returns a string that determines the shape of a node

Formats the style of the structural model edges

Description

Formats the style of the structural model edges

Usage

get_value_dependent_mm_edge_style(value, theme)
get_value_dependent_mm_edge_style(value, theme)

Arguments

`value`	value to compare for negativity
`theme`	the theme to use

Value

Returns the style for the edge (both style and color)

Formats the style of the structural model edges

Description

Formats the style of the structural model edges

Usage

get_value_dependent_sm_edge_style(value, theme)
get_value_dependent_sm_edge_style(value, theme)

Arguments

`value`	value to compare for negativity
`theme`	the theme to use

Value

Returns the style for the edge (both style and color)

higher_composite

Description

higher_composite creates a higher order construct from first-order constructs using the two-stage method (Becker et al., 2012).

Usage

higher_composite(construct_name, dimensions, method, weights)
higher_composite(construct_name, dimensions, method, weights)

Arguments

`construct_name`	of second-order construct
`dimensions`	the first-order constructs
`method`	is the estimation method, default is two_stage
`weights`	is the relationship between the second-order construct and first-order constructs. This can be specified as `correlation_weights` or `mode_A` for correlation weights (Mode A) or as `regression_weights` or `mode_B` for regression weights (Mode B). Default is correlation weights.

Details

This function conveniently maps first-order constructs onto second-order constructs using construct names.

Value

A vector of the indicators for a higher-order-composite.

Examples

  mobi_mm <- constructs(
    composite("Image",        multi_items("IMAG", 1:5), weights = correlation_weights),
    composite("Expectation",  multi_items("CUEX", 1:3), weights = mode_A),
    higher_composite("Quality",  c("Image","Expectation"), method = two_stage),
    composite("Value",        multi_items("PERV", 1:2), weights = mode_B)
  )
mobi_mm <- constructs(
    composite("Image",        multi_items("IMAG", 1:5), weights = correlation_weights),
    composite("Expectation",  multi_items("CUEX", 1:3), weights = mode_A),
    higher_composite("Quality",  c("Image","Expectation"), method = two_stage),
    composite("Value",        multi_items("PERV", 1:2), weights = mode_B)
  )

higher_reflective

Description

higher_reflective creates a higher-order reflective construct

Usage

higher_reflective(construct_name, dimensions)
higher_reflective(construct_name, dimensions)

Arguments

`construct_name`	of second-order construct
`dimensions`	the first-order constructs

Details

This function maps first-order constructs onto second-order reflective constructs using construct names. It is currently only suitable for CB-SEM and not PLS

Value

A vector of the indicators for a higher-order-factor.

Examples

  mobi_mm <- constructs(
    reflective("Image",           multi_items("IMAG", 1:5)),
    reflective("Expectation",     multi_items("CUEX", 1:3)),
    higher_reflective("Quality",  c("Image", "Expectation"))
  )
mobi_mm <- constructs(
    reflective("Image",           multi_items("IMAG", 1:5)),
    reflective("Expectation",     multi_items("CUEX", 1:3)),
    higher_reflective("Quality",  c("Image", "Expectation"))
  )

Measurement Instrument for the Influencer Model

Description

The data set is used as measurement instrument for the Influencer Model which is used in Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R - A Workbook (2021) Hair, J.F. (Jr), Hult, T.M., Ringle, C.M., Sarstedt, M., Danks, N.P., and Ray, S.

Usage

influencer_data
influencer_data

Format

A data frame with 250 rows and 24 variables:

sic_1: The influencer reflects who I am.
sic_2: I can identify with the influencer.
sic_3: I feel a personal connection to the influencer.
sic_4: I (can) use the influencer to communicate who I am to other people.
sic_5: I think the influencer (could) help(s) me become the type of person I want to be.
sic_6: I consider the influencer to be "me".
sic_7: The influencer suits me well.
sic_global: My personality and the personality of the influencer relate accordingly to one another.
pq_1: The product has excellent quality.
pq_2: The product looks to be reliable and durable.
pq_3: The product will have fewer problems.
pq_4: The product has excellent quality features.
pl_1: I dislike the product (reverse coded).
pl_2: The product is appealing to me.
pl_3: The presented product raises a positive feeling in me.
pl_4: The product is interesting to me.
pi_1: It is very likely that I will purchase this product.
pi_2: I will purchase this product the next time I need it.
pi_3: I would definitely try the product out.
pi_4: I would recommend this product to my friends.
pi_5: I am willing to purchase this product.
pic_1: The influencer is qualified.
pic_2: The influencer is competent.
pic_3: The influencer is an expert.
pic_4: The influencer is experienced.
pic_5: The influencer is knowledgeable.
wtp: Please state your willingness to pay (in Euro) for the presented product.
influencer_group: A binary variable indicating which group the influencer belongs to.

Details

The data frame influencer_data contains the observed data for the model specified in the Influencer Model.

Examples

data("influencer_data")

data("influencer_data")

Interaction function

Description

interaction_term creates interaction measurement items by applying product indicator, two stage, or orthogonal approaches to creating new interaction constructs.

Usage

interaction_term(iv, moderator, method, weights)
interaction_term(iv, moderator, method, weights)

Arguments

`iv`	The independent variable that is subject to moderation.
`moderator`	The moderator variable.
`method`	The method to generate the estimated interaction term with a default of 'two_stage'.
`weights`	The weighting mode for interaction items in a PLS model (only) with default of 'modeA'.

Details

This function automatically generates interaction measurement items for a PLS or a CBSEM model.

Value

An un-evaluated function (promise) for generating a vector of interaction terms.

Interaction Combinations as generated by the interaction or interaction_term methods.

Examples

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3)),
  interaction_term(iv = "Image", moderator = "Expectation", method = orthogonal),
  interaction_term(iv = "Image", moderator = "Value", method = product_indicator)
)

#  structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                 "Image*Expectation", "Image*Value"))
)

mobi_pls <- estimate_pls(mobi, mobi_mm, mobi_sm)
summary(mobi_pls)

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3)),
  interaction_term(iv = "Image", moderator = "Expectation", method = orthogonal),
  interaction_term(iv = "Image", moderator = "Value", method = product_indicator)
)

#  structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                 "Image*Expectation", "Image*Value"))
)

mobi_pls <- estimate_pls(mobi, mobi_mm, mobi_sm)
summary(mobi_pls)

Tests whether the i_th construct is endogenous or not

Description

Tests whether the i_th construct is endogenous or not

Usage

is_sink(model, index)
is_sink(model, index)

Arguments

`model`	the model object
`index`	the index of the construct to test

Value

whether the construct is endogenous or not

Specifies pair of items, or sets of items, that should covary. Used to specify error covariances for `associations`.

Description

Specifies pair of items, or sets of items, that should covary. Used to specify error covariances for associations.

Usage

item_errors(items_a, items_b)
item_errors(items_a, items_b)

Arguments

`items_a`	One or more items that should covary
`items_b`	One or more items that should covary

Value

A vector of items that covary.

Examples

item_errors(c("a1", "a2"), c("b1", "b2"))
item_errors(c("a1", "a2"), c("b1", "b2"))

Function to clean data of omitted values by mean replacement

Description

The seminr package provides a natural syntax for researchers to describe PLS structural equation models.

Usage

mean_replacement(data)
mean_replacement(data)

Arguments

data

A dataset to be used for estimating a SEMinR model

Details

mean_replacement provides the verb for replacing all omitted values (NA only) in the dataset with the mean of the variable.

Value

A dataset with all missing values replaced with column means

References

Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd Ed., Sage: Thousand Oaks.

Measurement Instrument for the Mobile Phone Industry

Description

The data set is used as measurement instrument for the european customer satisfaction index (ECSI) adapted to the mobile phone market, see Tenenhaus et al. (2005).

Usage

mobi
mobi

Format

A data frame with 250 rows and 24 variables:

CUEX1: Expectations for the overall quality of "your mobile phone provider" at the moment you became customer of this provider
CUEX2: Expectations for "your mobile phone provider" to provide products and services to meet your personal need
CUEX3: How often did you expect that things could go wrong at "your mobile phone provider
CUSA1: Overall satisfaction
CUSA2: Fulfillment of expectations
CUSA3: How well do you think "your mobile phone provider" compares with your ideal mobile phone provider?
CUSCO: You complained about "your mobile phone provider" last year. How well, or poorly, was your most recent complaint handled or You did not complain about "your mobile phone provider" last year. Imagine you have to complain to "your mobile phone rovider" because of a bad quality of service or product. To what extent do you think that "your mobile phone provider" will care about your complaint?
CUSL1: If you would need to choose a new mobile phone provider how likely is it that you would choose "your provider" again?
CUSL2: Let us now suppose that other mobile phone providers decide to lower their fees and prices, but "your mobile phone provider" stays at the same level as today. At which level of difference (in percentage) would you choose another mobile phone provider?
CUSL3: If a friend or colleague asks you for advice, how likely is it that you would recommend "your mobile phone provider"?
IMAG1: It can be trusted what it says and does
IMAG2: It is stable and firmly established
IMAG3: It has a social contribution to society
IMAG4: It is concerned with customers
IMAG5: It is innovative and forward looking
PERQ1: Overall perceived quality
PERQ2: Technical quality of the network
PERQ3: Customer service and personal advice offered
PERQ4: Quality of the services you use
PERQ5: Range of services and products offered
PERQ6: Reliability and accuracy of the products and services provided
PERQ7: Clarity and transparency of information provided
PERV1: Given the quality of the products and services offered by "your mobile phone provider" how would you rate the fees and prices that you pay for them?
PERV2: Given the fees and prices that you pay for "your mobile phone provider" how would you rate the quality of the products and services offered by "your mobile phone provider"?

Details

The data frame mobi contains the observed data for the model specified by ECSImobi.

References

Tenenhaus, M., V. E. Vinzi, Y.-M. Chatelin, and C. Lauro (2005) PLS path modeling. Computational Statistics & Data Analysis 48, 159-205.

Examples

data("mobi")

data("mobi")

Outer weighting scheme functions to estimate construct weighting.

Description

mode_A, correlation_weights and mode_B, regression_weights specify the outer weighting scheme to be used in the estimation of the construct weights and score.

Usage

mode_A(mmMatrix, i, normData, construct_scores)
mode_A(mmMatrix, i, normData, construct_scores)

Arguments

`mmMatrix`	is the `measurement_model` - a source-to-target matrix representing the measurement model, generated by `constructs`.
`i`	is the name of the construct to be estimated.
`normData`	is the dataframe of the normalized item data.
`construct_scores`	is the matrix of construct scores generated by `estimate_model`.

Value

A matrix of estimated measurement model relations.

Outer weighting scheme functions to estimate construct weighting.

Description

mode_A, correlation_weights and mode_B, regression_weights specify the outer weighting scheme to be used in the estimation of the construct weights and score.

Usage

mode_B(mmMatrix, i, normData, construct_scores)
mode_B(mmMatrix, i, normData, construct_scores)

Arguments

`mmMatrix`	is the `measurement_model` - a source-to-target matrix representing the measurement model, generated by `constructs`.
`i`	is the name of the construct to be estimated.
`normData`	is the dataframe of the normalized item data.
`construct_scores`	is the matrix of construct scores generated by `estimate_model`.

Value

A matrix of estimated measurement model relations.

Multi-items measurement model specification

Description

multi_items creates a vector of measurment names given the item prefix and number range.

Usage

multi_items(item_name, item_numbers, ...)
multi_items(item_name, item_numbers, ...)

Arguments

`item_name`	Prefix name of items
`item_numbers`	The range of number suffixews for the items
`...`	Additional Item names and nubers

Value

A vector of numbered indicators.

Examples

  mobi_mm <- constructs(
    composite("Image",        multi_items("IMAG", 1:5), weights = correlation_weights),
    composite("Expectation",  multi_items("CUEX", 1:3), weights = mode_A),
    composite("Quality",      multi_items("PERQ", 1:7), weights = regression_weights),
    composite("Value",        multi_items("PERV", 1:2), weights = mode_B)
  )
mobi_mm <- constructs(
    composite("Image",        multi_items("IMAG", 1:5), weights = correlation_weights),
    composite("Expectation",  multi_items("CUEX", 1:3), weights = mode_A),
    composite("Quality",      multi_items("PERQ", 1:7), weights = regression_weights),
    composite("Value",        multi_items("PERV", 1:2), weights = mode_B)
  )

The default template for labeling endogenous construct nodes

Description

The default template for labeling endogenous construct nodes

Usage

node_endo_template_default()
node_endo_template_default()

Value

The template string

The default template for labeling exogenous construct nodes

Description

The default template for labeling exogenous construct nodes

Usage

node_exo_template_default()
node_exo_template_default()

Value

The template string

`orthogonal` creates interaction measurement items by using the orthogonalized approach wherein

Description

This function automatically generates interaction measurement items for a PLS SEM using the orthogonalized approach..

Usage

# orthogonalization approach as per Henseler & Chin (2010):
 orthogonal(iv, moderator, weights)
# orthogonalization approach as per Henseler & Chin (2010):
 orthogonal(iv, moderator, weights)

Arguments

`iv`	The independent variable that is subject to moderation.
`moderator`	The moderator variable.
`weights`	is the relationship between the items and the interaction terms. This can be specified as `correlation_weights` or `mode_A` for correlation weights (Mode A) or as `regression_weights` or `mode_B` for regression weights (Mode B). Default is correlation weights.

Value

An un-evaluated function (promise) for estimating an orthogonal interaction effect.

References

Henseler & Chin (2010), A comparison of approaches for the analysis of interaction effects between latent variables using partial least squares path modeling. Structural Equation Modeling, 17(1),82-109.

Examples

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3)),
  interaction_term(iv = "Image", moderator = "Expectation", method = orthogonal),
  interaction_term(iv = "Image", moderator = "Value", method = orthogonal)
)

#  structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                 "Image*Expectation", "Image*Value"))
)

mobi_pls <- estimate_pls(mobi, mobi_mm, mobi_sm)
summary(mobi_pls)

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3)),
  interaction_term(iv = "Image", moderator = "Expectation", method = orthogonal),
  interaction_term(iv = "Image", moderator = "Value", method = orthogonal)
)

#  structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                 "Image*Expectation", "Image*Value"))
)

mobi_pls <- estimate_pls(mobi, mobi_mm, mobi_sm)
summary(mobi_pls)

Inner weighting scheme functions to estimate inner paths matrix

Description

path_factorial and path_weighting specify the inner weighting scheme to be used in the estimation of the inner paths matrix

Usage

path_factorial(smMatrix,construct_scores, dependant, paths_matrix)
path_factorial(smMatrix,construct_scores, dependant, paths_matrix)

Arguments

`smMatrix`	is the `structural_model` - a source-to-target matrix representing the inner/structural model, generated by `relationships`.
`construct_scores`	is the matrix of construct scores generated by `estimate_model`.
`dependant`	is the vector of dependant constructs in the model.
`paths_matrix`	is the matrix of estimated path coefficients estimated by `estimate_model`.

Value

A matrix of estimated structural relations.

References

Lohmoller, J.-B. (1989). Latent variables path modeling with partial least squares. Heidelberg, Germany: Physica Verlag.

Inner weighting scheme functions to estimate inner paths matrix

Description

path_factorial and path_weighting specify the inner weighting scheme to be used in the estimation of the inner paths matrix

Usage

path_weighting(smMatrix,construct_scores, dependant, paths_matrix)
path_weighting(smMatrix,construct_scores, dependant, paths_matrix)

Arguments

`smMatrix`	is the `structural_model` - a source-to-target matrix representing the inner/structural model, generated by `relationships`.
`construct_scores`	is the matrix of construct scores generated by `estimate_model`.
`dependant`	is the vector of dependant constructs in the model.
`paths_matrix`	is the matrix of estimated path coefficients estimated by `estimate_model`.

Value

A matrix of estimated structural relations.

References

Lohmoller, J.B. (1989). Latent variables path modeling with partial least squares. Heidelberg, Germany: Physica-Verlag.

Plots a network graph of constructs based on HTMT measures

Description

Using a bootstrapped model this functions shows which constructs show insufficient discriminant validity.

Usage

plot_htmt(
  model,
  title = "HTMT Plot",
  theme = seminr::seminr_theme_get(),
  htmt_threshold = 1,
  omit_threshold_edges = TRUE,
  use_ci = FALSE
)
plot_htmt(
  model,
  title = "HTMT Plot",
  theme = seminr::seminr_theme_get(),
  htmt_threshold = 1,
  omit_threshold_edges = TRUE,
  use_ci = FALSE
)

Arguments

`model`	A bootsrapped PLS-Model
`title`	Optional title over the plot.
`theme`	Optional theme to use for plotting
`htmt_threshold`	The threshold to use under which constructs are highlighted (default = 1.0)
`omit_threshold_edges`	Whether or not to omit constructs that have low HTMT values (default = TRUE)
`use_ci`	Whether or not to rely on the upper threshold of the CI instead of the bootstrapped mean (default = FALSE)

Value

Returs a dot string of the plot

Function for plotting interaction plot for moderated PLS or CBSEM model

Description

plot_interaction generates an interaction plot for the effect of an antecedent on an outcome given a mediator variable.

Usage

plot_interaction(moderated_model, intxn, dv, legend)
plot_interaction(moderated_model, intxn, dv, legend)

Arguments

`moderated_model`	SEMinR model that contains an interaction.
`intxn`	Name (character) of the interaction term in the structural model. Must look like a product of independent variabel and moderator (e.g., "ABC*XYZ")
`dv`	Name (character) of the dependant consutruct affected by the moderator.
`legend`	Location (character) of the legend on the plot; must be a combination of bottom\|top and left\|right (e.g., "bottomright").

Examples

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
composite("Image",        multi_items("IMAG", 1:5)),
composite("Expectation",  multi_items("CUEX", 1:3)),
composite("Value",        multi_items("PERV", 1:2)),
composite("Satisfaction", multi_items("CUSA", 1:3)),
interaction_term(iv = "Image", moderator = c("Expectation"), method = orthogonal))

# Structural model
#  note: interactions should be the names of its main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                 "Image*Expectation")))

# Load data, assemble model, and estimate
mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm)

plot_interaction(mobi_pls, "Image*Expectation", "Satisfaction", "bottomright")

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
composite("Image",        multi_items("IMAG", 1:5)),
composite("Expectation",  multi_items("CUEX", 1:3)),
composite("Value",        multi_items("PERV", 1:2)),
composite("Satisfaction", multi_items("CUSA", 1:3)),
interaction_term(iv = "Image", moderator = c("Expectation"), method = orthogonal))

# Structural model
#  note: interactions should be the names of its main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                 "Image*Expectation")))

# Load data, assemble model, and estimate
mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm)

plot_interaction(mobi_pls, "Image*Expectation", "Satisfaction", "bottomright")

Function for plotting the measurement model reliability metrics of a PLS model

Description

plot.reliability_table generates an easy to read visualization of the rhoA, Cronbachs alpha, and Composite Reliability for all constructs. The plot visualizes the metrics in such a way as to draw meaning from not only the absolute values, but their relative values too.

Usage

## S3 method for class 'reliability_table'
plot(x, ...)
## S3 method for class 'reliability_table'
plot(x, ...)

Arguments

`x`	A `reliability_table` object from a SEMinR PLS model. This can be accessed as the `reliability` element of the PLS model summary object.
`...`	All other arguments inherited from `plot`.

Examples

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
 composite("Image",        multi_items("IMAG", 1:5)),
 composite("Expectation",  multi_items("CUEX", 1:3)),
 composite("Value",        multi_items("PERV", 1:2)),
 composite("Satisfaction", multi_items("CUSA", 1:3))
)

#  structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
 paths(to = "Satisfaction",
       from = c("Image", "Expectation", "Value"))
)

mobi_pls <- estimate_pls(mobi, measurement_model = mobi_mm, structural_model = mobi_sm)
plot(summary(mobi_pls)$reliability)

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
 composite("Image",        multi_items("IMAG", 1:5)),
 composite("Expectation",  multi_items("CUEX", 1:3)),
 composite("Value",        multi_items("PERV", 1:2)),
 composite("Satisfaction", multi_items("CUSA", 1:3))
)

#  structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
 paths(to = "Satisfaction",
       from = c("Image", "Expectation", "Value"))
)

mobi_pls <- estimate_pls(mobi, measurement_model = mobi_mm, structural_model = mobi_sm)
plot(summary(mobi_pls)$reliability)

Plot various SEMinR models

Description

With the help of the DiagrammeR package this dot graph can then be plotted in various in RMarkdown, shiny, and other contexts. Depending on the type of model, different parameters can be used. Please check the dot_graph function for additional parameters.

Usage

## S3 method for class 'seminr_model'
plot(x, title = "", theme = NULL, ...)
## S3 method for class 'seminr_model'
plot(x, title = "", theme = NULL, ...)

Arguments

`x`	The model description
`title`	An optional title for the plot
`theme`	Theme created with `seminr_theme_create`.
`...`	Please check the `dot_graph` for the additional parameters

Value

Returns the plot.

seminr PLSc Function

Description

The PLSc function calculates the consistent PLS path coefficients and loadings for a common-factor model. It returns a seminr_model containing the adjusted and consistent path coefficients and loadings for common-factor models and composite models.

Usage

PLSc(seminr_model)
PLSc(seminr_model)

Arguments

seminr_model

A seminr_model containing the estimated seminr model.

Value

A SEMinR model object which has been adjusted according to PLSc.

References

Dijkstra, T. K., & Henseler, J. (2015). Consistent Partial Least Squares Path Modeling, 39(X).

Examples

mobi <- mobi

#seminr syntax for creating measurement model
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )
#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

seminr_model <- estimate_pls(data = mobi,
                             measurement_model = mobi_mm,
                             structural_model = mobi_sm)

PLSc(seminr_model)
mobi <- mobi

#seminr syntax for creating measurement model
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )
#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

seminr_model <- estimate_pls(data = mobi,
                             measurement_model = mobi_mm,
                             structural_model = mobi_sm)

PLSc(seminr_model)

Predictive Scheme

Description

predict_EA and predict_DA specify the predictive scheme to be used in the generation of the predictions. EA refers to Earliest Antecedents nad DA to Direct Antecedents.

Usage

predict_DA(smMatrix, path_coef, construct_scores)
predict_DA(smMatrix, path_coef, construct_scores)

Arguments

`smMatrix`	is the `structural_model` - a source-to-target matrix representing the inner/structural model, generated by `relationships` generated by SEMinR.
`path_coef`	is the Path Coefficients matrix from a SEMinR model.
`construct_scores`	is the matrix of construct scores generated by SEMinR.

Predictive Scheme

Description

predict_EA and predict_DA specify the predictive scheme to be used in the generation of the predictions. EA refers to Earliest Antecedents nad DA to Direct Antecedents.

Usage

predict_EA(smMatrix, path_coef, construct_scores)
predict_EA(smMatrix, path_coef, construct_scores)

Arguments

`smMatrix`	is the `structural_model` - a source-to-target matrix representing the inner/structural model, generated by `relationships` generated by SEMinR.
`path_coef`	is the Path Coefficients matrix from a SEMinR model.
`construct_scores`	is the matrix of construct scores generated by SEMinR.

Predict_pls performs either k-fold or LOOCV on a SEMinR PLS model and generates predictions

Description

predict_pls uses cross-validation to generate in-sample and out-sample predictions for PLS models generated by SEMinR.

Usage

predict_pls(model, technique, noFolds, reps, cores)
predict_pls(model, technique, noFolds, reps, cores)

Arguments

`model`	A SEMinR model that has been estimated on the FULL dataset.
`technique`	The predictive technique to be employed, Earliest Antecedents (EA) `predict_EA` or Direct Antecedents (DA) `predict_DA`
`noFolds`	The required number of folds to use in k-fold cross validation. If NULL, then parallel LOOCV will be executed. Default is NULL.
`reps`	The number of times the cross-validation will be repeated. Default is NULL.
`cores`	The number of cores to use for parallel LOOCV processing. If k-fold is used, the process will not be parallelized.

Details

This function generates cross-validated in-sample and out-sample predictions for PLS models generated by SEMinR. The cross validation technique can be k-fold if a number of folds are specified, or leave-one-out-cross-validation (LOOCV) if no folds arew specified. LOOCV is recommended for small datasets.

Value

A list of the estimated PLS and LM prediction results:

`PLS_out_of_sample`	A matrix of the out-of-sample indicator predictions generated by the SEMinR model.
`PLS_in_sample`	A matrix of the in-sample indicator predictions generated by the SEMinR model.
`lm_out_of_sample`	A matrix of the out-of-sample indicator predictions generated by a linear regression model.
`lm_in_sample`	A matrix of the in-sample indicator predictions generated by a linear regression model.
`item_actuals`	A matrix of the actual indicator scores.
`PLS_out_of_sample_residuals`	A matrix of the out-of-sample indicator PLS prediction residuals.
`PLS_in_sample_residuals`	A matrix of the in-sample indicator PLS prediction residuals.
`lm_out_of_sample_residuals`	A matrix of the out-of-sample LM indicator prediction residuals.
`lm_in_sample_residuals`	A matrix of the in-sample LM indicator prediction residuals.
`mmMatrix`	A Matrix of the measurement model relations.
`smMatrix`	A Matrix of the structural model relations.
`constructs`	A vector of the construct names.
`mmVariables`	A vector of the indicator names.
`outer_loadings`	The matrix of estimated indicator loadings.
`outer_weights`	The matrix of estimated indicator weights.
`path_coef`	The matrix of estimated structural model relationships.
`iterations`	A numeric indicating the number of iterations required before the algorithm converged.
`weightDiff`	A numeric indicating the minimum weight difference between iterations of the algorithm.
`construct_scores`	A matrix of the estimated construct scores for the PLS model.
`rSquared`	A matrix of the estimated R Squared for each construct.
`inner_weights`	The inner weight estimation function.
`data`	A matrix of the data upon which the model was estimated (INcluding interactions.
`rawdata`	A matrix of the data upon which the model was estimated (EXcluding interactions.
`measurement_model`	The SEMinR measurement model specification.

Examples

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3))
)

mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value"))
)

mobi_pls <- estimate_pls(mobi, mobi_mm, mobi_sm)
cross_validated_predictions <- predict_pls(model = mobi_pls,
                                           technique = predict_DA,
                                           noFolds = 10,
                                           cores = NULL)

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3))
)

mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value"))
)

mobi_pls <- estimate_pls(mobi, mobi_mm, mobi_sm)
cross_validated_predictions <- predict_pls(model = mobi_pls,
                                           technique = predict_DA,
                                           noFolds = 10,
                                           cores = NULL)

Summary function for PLS-MGA

Description

Summary function for PLS-MGA

Usage

## S3 method for class 'seminr_pls_mga'
print(x, digits = 3, ...)
## S3 method for class 'seminr_pls_mga'
print(x, digits = 3, ...)

Arguments

`x`	estimated seminr_pls_mga object
`digits`	number of digits to print
`...`	any further parameters for printing

`product_indicator` creates interaction measurement items by scaled product indicator approach.

Description

This function automatically generates interaction measurement items for a PLS SEM using scaled product indicator approach.

Usage

# standardized product indicator approach as per Henseler & Chin (2010):
 product_indicator(iv, moderator, weights)
# standardized product indicator approach as per Henseler & Chin (2010):
 product_indicator(iv, moderator, weights)

Arguments

`iv`	The independent variable that is subject to moderation.
`moderator`	The moderator variable.
`weights`	is the relationship between the items and the interaction terms. This can be specified as `correlation_weights` or `mode_A` for correlation weights (Mode A) or as `regression_weights` or `mode_B` for regression weights (Mode B). Default is correlation weights.

Value

An un-evaluated function (promise) for estimating a product-indicator interaction effect.

References

Examples

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
 composite("Image",        multi_items("IMAG", 1:5),weights = mode_A),
 composite("Expectation",  multi_items("CUEX", 1:3),weights = mode_A),
 composite("Value",        multi_items("PERV", 1:2),weights = mode_A),
 composite("Satisfaction", multi_items("CUSA", 1:3),weights = mode_A),
 interaction_term(iv = "Image",
                  moderator = "Expectation",
                  method = product_indicator,
                  weights = mode_A),
 interaction_term(iv = "Image",
                  moderator = "Value",
                  method = product_indicator,
                  weights = mode_A)
)

# structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                "Image*Expectation", "Image*Value"))
)

# Load data, assemble model, and estimate using semPLS
mobi <- mobi
seminr_model <- estimate_pls(mobi, mobi_mm, mobi_sm, inner_weights = path_factorial)

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
 composite("Image",        multi_items("IMAG", 1:5),weights = mode_A),
 composite("Expectation",  multi_items("CUEX", 1:3),weights = mode_A),
 composite("Value",        multi_items("PERV", 1:2),weights = mode_A),
 composite("Satisfaction", multi_items("CUSA", 1:3),weights = mode_A),
 interaction_term(iv = "Image",
                  moderator = "Expectation",
                  method = product_indicator,
                  weights = mode_A),
 interaction_term(iv = "Image",
                  moderator = "Value",
                  method = product_indicator,
                  weights = mode_A)
)

# structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                "Image*Expectation", "Image*Value"))
)

# Load data, assemble model, and estimate using semPLS
mobi <- mobi
seminr_model <- estimate_pls(mobi, mobi_mm, mobi_sm, inner_weights = path_factorial)

Reflective construct measurement model specification

Description

reflective creates the reflective measurement model matrix for a specific common-factor, specifying the relevant items of the construct and assigning the relationship of reflective. By definition this construct will be estimated by PLS consistent.

Usage

reflective(construct_name, item_names)
reflective(construct_name, item_names)

Arguments

`construct_name`	of construct
`item_names`	returned by the `multi_items` or `single_item` functions

Details

This function conveniently maps reflectively defined measurement items to a construct and is estimated using PLS consistent.

Value

A vector of the indicators for a reflective construct.

Examples

  mobi_mm <- constructs(
    reflective("Image",        multi_items("IMAG", 1:5)),
    reflective("Expectation",  multi_items("CUEX", 1:3)),
    reflective("Quality",      multi_items("PERQ", 1:7)),
    reflective("Value",        multi_items("PERV", 1:2)),
    reflective("Satisfaction", multi_items("CUSA", 1:3)),
    reflective("Complaints",   single_item("CUSCO")),
    reflective("Loyalty",      multi_items("CUSL", 1:3))
  )
mobi_mm <- constructs(
    reflective("Image",        multi_items("IMAG", 1:5)),
    reflective("Expectation",  multi_items("CUEX", 1:3)),
    reflective("Quality",      multi_items("PERQ", 1:7)),
    reflective("Value",        multi_items("PERV", 1:2)),
    reflective("Satisfaction", multi_items("CUSA", 1:3)),
    reflective("Complaints",   single_item("CUSCO")),
    reflective("Loyalty",      multi_items("CUSL", 1:3))
  )

Structural specification functions for seminr package

Description

paths creates the structural paths of a PLS SEM model and relationships generates the matrix of paths.

Usage

relationships(...)

paths(from,to)
relationships(...)

paths(from,to)

Arguments

`...`	A comma separated list of all the structural relationships in the the model. These paths take the form (from = c(construct_name), to = c(construct_name)).
`to`	The destination construct of a structural path
`from`	The source construct of a structural path
`paths`	The function `paths` that specifies the source and destination constructs for each of the model's structural paths.

Value

A vector of construct names and structural relationships.

Examples

mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

Functions for reporting the Path Coefficients and R2 of endogenous constructs and for generating a scatterplot matrix of construct scores.

Description

report_paths generates an easy to read table reporting path coefficients and R2 values for endogenous constructs.plot_scores generates a scatterplot matrix of each construct's scores against every other construct's scores.

Usage

report_paths(seminr_model, digits=3)

plot_scores(seminr_model, constructs=NULL)
report_paths(seminr_model, digits=3)

plot_scores(seminr_model, constructs=NULL)

Arguments

`seminr_model`	The PLS model estimated by `seminr`. The estimated model returned by the `estimate_pls` or `bootstrap_model` methods.
`digits`	A `numeric` minimum number of significant digits. If not specified, default is "2".
`constructs`	a `list` indicating which constructs to report. If not specified, all constructs are graphed and returned.

Details

These functions generate an easy to read table reporting path coefficients and R2 values for endogenous constructs or a scatterplot matrix of construct scores.

Value

A matrix of structural paths.

Examples

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3))
)

#  structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value"))
)

mobi_pls <- estimate_pls(mobi, measurement_model = mobi_mm, structural_model = mobi_sm)
report_paths(mobi_pls)
plot_scores(mobi_pls)

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3))
)

#  structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value"))
)

mobi_pls <- estimate_pls(mobi, measurement_model = mobi_mm, structural_model = mobi_sm)
report_paths(mobi_pls)
plot_scores(mobi_pls)

Reruns a previously specified seminr model/analysis

Description

Reruns a previously specified seminr model/analysis

Usage

rerun(x, ...)
rerun(x, ...)

Arguments

`x`	An estimated seminr_model object - refer to specific rerun methods
`...`	Any parameters to change during the rerun.

Value

A re-estimated model of the same class

Reruns a previously specified seminr PLS model

Description

Reruns a previously specified seminr PLS model

Usage

## S3 method for class 'pls_model'
rerun(x, ...)
## S3 method for class 'pls_model'
rerun(x, ...)

Arguments

`x`	An estimated pls_model object produced by `estimate_pls`
`...`	Any parameters to change during the re-estimation (e.g., data, measurement_model, etc.)

Value

A re-estimated pls_model object

Examples


mobi <- mobi

mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Loyalty",      multi_items("CUSL", 1:3))
)

mobi_sm <- relationships(
  paths(from = "Image",        to = c("Loyalty"))
)

mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm,
                         missing = mean_replacement,
                         missing_value = NA)

# Re-estimate model faithfully
mobi_pls2 <- rerun(mobi_pls)

# Re-estimated model with altered measurement model
mobi_pls3 <- rerun(mobi_pls, measurement_model=as.reflective(mobi_mm))

mobi <- mobi

mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Loyalty",      multi_items("CUSL", 1:3))
)

mobi_sm <- relationships(
  paths(from = "Image",        to = c("Loyalty"))
)

mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm,
                         missing = mean_replacement,
                         missing_value = NA)

# Re-estimate model faithfully
mobi_pls2 <- rerun(mobi_pls)

# Re-estimated model with altered measurement model
mobi_pls3 <- rerun(mobi_pls, measurement_model=as.reflective(mobi_mm))

seminr rho_A Function

Description

The rho_A function calculates the rho_A reliability indices for each construct. For formative constructs, the index is set to 1.

Usage

rho_A(seminr_model, constructs)
rho_A(seminr_model, constructs)

Arguments

`seminr_model`	A `seminr_model` containing the estimated seminr model.
`constructs`	A vector containing the names of the constructs to calculate rhoA for.

Value

A matrix containing the rhoA metric for each construct.

References

Dijkstra, T. K., & Henseler, J. (2015). Consistent partial least squares path modeling. MIS quarterly, 39(2).

Examples

#seminr syntax for creating measurement model
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )
#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

mobi_pls <- estimate_pls(data = mobi,
                           measurement_model = mobi_mm,
                           structural_model = mobi_sm)

rho_A(mobi_pls, mobi_pls$constructs)
#seminr syntax for creating measurement model
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )
#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

mobi_pls <- estimate_pls(data = mobi,
                           measurement_model = mobi_mm,
                           structural_model = mobi_sm)

rho_A(mobi_pls, mobi_pls$constructs)

Saves a SEMinR model plot to file

Description

Saves a SEMinR model plot to a graphical file. Default output is RPlots.pdf.

Usage

save_plot(
  filename = "RPlot.pdf",
  plot = last_seminr_plot(),
  width = NULL,
  height = NULL
)
save_plot(
  filename = "RPlot.pdf",
  plot = last_seminr_plot(),
  width = NULL,
  height = NULL
)

Arguments

`filename`	The name of the file output (can be png, pdf, webp, ps, or svg.)
`plot`	A plot that is created from the `plot` function. By default it uses the last plot created.
`width`	An optional parameter for width in pixels.
`height`	An optional parameter for height in pixels.

Value

Does not return a value

Examples

mobi <- mobi

# seminr syntax for creating measurement model
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )
# seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

# estimate the model
mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm)
## Not run: 
# generate the plot
plot(mobi_pls)
# save to file
save_plot("myplot.pdf")

## End(Not run)

mobi <- mobi

# seminr syntax for creating measurement model
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )
# seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

# estimate the model
mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm)
## Not run: 
# generate the plot
plot(mobi_pls)
# save to file
save_plot("myplot.pdf")

## End(Not run)

Create a theme for a seminr graph visualization

Description

All customizable options are parameters of this function. See the details all the way down for more information.

Usage

seminr_theme_create(
  plot.title.fontsize = 24,
  plot.title.fontcolor = "black",
  plot.fontname = "helvetica",
  plot.splines = TRUE,
  plot.rounding = 3,
  plot.adj = FALSE,
  plot.specialcharacters = TRUE,
  plot.randomizedweights = FALSE,
  plot.bgcolor = "transparent",
  mm.node.color = "dimgrey",
  mm.node.fill = "white",
  mm.node.label.fontsize = 8,
  mm.node.label.fontcolor = "black",
  mm.edge.positive.color = "dimgrey",
  mm.edge.negative.color = "dimgrey",
  mm.edge.positive.style = "solid",
  mm.edge.negative.style = "dashed",
  mm.edge.label.fontsize = 7,
  mm.edge.label.fontcolor = "black",
  mm.edge.label.show = TRUE,
  mm.edge.minlen = 1,
  mm.edge.width_multiplier = 3,
  mm.edge.width_offset = 0.5,
  mm.edge.use_outer_weights = TRUE,
  mm.edge.boot.show_t_value = FALSE,
  mm.edge.boot.show_p_value = FALSE,
  mm.edge.boot.show_p_stars = TRUE,
  mm.edge.boot.show_ci = FALSE,
  mm.edge.boot.template = edge_template_minimal(),
  sm.node.color = "black",
  sm.node.fill = "white",
  sm.node.label.fontsize = 12,
  sm.node.label.fontcolor = "black",
  sm.node.endo.template = node_endo_template_default(),
  sm.node.exo.template = node_exo_template_default(),
  sm.edge.boot.show_t_value = FALSE,
  sm.edge.boot.show_p_value = FALSE,
  sm.edge.boot.show_p_stars = TRUE,
  sm.edge.boot.show_ci = TRUE,
  sm.edge.boot.template = edge_template_default(),
  sm.edge.positive.color = "black",
  sm.edge.negative.color = "black",
  sm.edge.positive.style = "solid",
  sm.edge.negative.style = "dashed",
  sm.edge.label.fontsize = 9,
  sm.edge.label.fontcolor = "black",
  sm.edge.label.show = TRUE,
  sm.edge.label.all_betas = TRUE,
  sm.edge.minlen = NA_integer_,
  sm.edge.width_offset = 0.5,
  sm.edge.width_multiplier = 5,
  construct.reflective.shape = "ellipse",
  construct.reflective.arrow = "backward",
  construct.reflective.use_weights = FALSE,
  construct.compositeA.shape = "hexagon",
  construct.compositeA.arrow = "backward",
  construct.compositeA.use_weights = FALSE,
  construct.compositeB.shape = "hexagon",
  construct.compositeB.arrow = "forward",
  construct.compositeB.use_weights = TRUE,
  manifest.reflective.shape = "box",
  manifest.compositeA.shape = "box",
  manifest.compositeB.shape = "box",
  ...
)
seminr_theme_create(
  plot.title.fontsize = 24,
  plot.title.fontcolor = "black",
  plot.fontname = "helvetica",
  plot.splines = TRUE,
  plot.rounding = 3,
  plot.adj = FALSE,
  plot.specialcharacters = TRUE,
  plot.randomizedweights = FALSE,
  plot.bgcolor = "transparent",
  mm.node.color = "dimgrey",
  mm.node.fill = "white",
  mm.node.label.fontsize = 8,
  mm.node.label.fontcolor = "black",
  mm.edge.positive.color = "dimgrey",
  mm.edge.negative.color = "dimgrey",
  mm.edge.positive.style = "solid",
  mm.edge.negative.style = "dashed",
  mm.edge.label.fontsize = 7,
  mm.edge.label.fontcolor = "black",
  mm.edge.label.show = TRUE,
  mm.edge.minlen = 1,
  mm.edge.width_multiplier = 3,
  mm.edge.width_offset = 0.5,
  mm.edge.use_outer_weights = TRUE,
  mm.edge.boot.show_t_value = FALSE,
  mm.edge.boot.show_p_value = FALSE,
  mm.edge.boot.show_p_stars = TRUE,
  mm.edge.boot.show_ci = FALSE,
  mm.edge.boot.template = edge_template_minimal(),
  sm.node.color = "black",
  sm.node.fill = "white",
  sm.node.label.fontsize = 12,
  sm.node.label.fontcolor = "black",
  sm.node.endo.template = node_endo_template_default(),
  sm.node.exo.template = node_exo_template_default(),
  sm.edge.boot.show_t_value = FALSE,
  sm.edge.boot.show_p_value = FALSE,
  sm.edge.boot.show_p_stars = TRUE,
  sm.edge.boot.show_ci = TRUE,
  sm.edge.boot.template = edge_template_default(),
  sm.edge.positive.color = "black",
  sm.edge.negative.color = "black",
  sm.edge.positive.style = "solid",
  sm.edge.negative.style = "dashed",
  sm.edge.label.fontsize = 9,
  sm.edge.label.fontcolor = "black",
  sm.edge.label.show = TRUE,
  sm.edge.label.all_betas = TRUE,
  sm.edge.minlen = NA_integer_,
  sm.edge.width_offset = 0.5,
  sm.edge.width_multiplier = 5,
  construct.reflective.shape = "ellipse",
  construct.reflective.arrow = "backward",
  construct.reflective.use_weights = FALSE,
  construct.compositeA.shape = "hexagon",
  construct.compositeA.arrow = "backward",
  construct.compositeA.use_weights = FALSE,
  construct.compositeB.shape = "hexagon",
  construct.compositeB.arrow = "forward",
  construct.compositeB.use_weights = TRUE,
  manifest.reflective.shape = "box",
  manifest.compositeA.shape = "box",
  manifest.compositeB.shape = "box",
  ...
)

Arguments

`plot.title.fontsize`	Font size of the title.
`plot.title.fontcolor`	Fontcolor of the title of the plot.
`plot.fontname`	Font to be used throughout the plot.
`plot.splines`	Whether or not to use splines as edges (default = TRUE).
`plot.rounding`	The amount of decimals to keep for rounding (default = 3).
`plot.adj`	TRUE or FALSE (default). Whether or not to use adjusted r^2 in constructs.
`plot.specialcharacters`	Whether or not to use greek UTF-8 symbols in plots.
`plot.randomizedweights`	TRUE or FALSE (default), decides whether to add. minimal random weights to the measurement model. Can help with determinism in plot outcomes.
`plot.bgcolor`	The background color of the plot (default = "transparent").
`mm.node.color`	Color of the measurement model nodes.
`mm.node.fill`	Fill of the measurement model nodes.
`mm.node.label.fontsize`	Font size of the measurement model node labels.
`mm.node.label.fontcolor`	Color of the measurement model node labels.
`mm.edge.positive.color`	Color of the measurement model edges, when values are positive.
`mm.edge.negative.color`	Color of the measurement model edges, when values are negative.
`mm.edge.positive.style`	Style of the measurement model edges, when values are positive.
`mm.edge.negative.style`	Style of the measurement model edges, when values are negative.
`mm.edge.label.fontsize`	Font size of the measurement model edge labels.
`mm.edge.label.fontcolor`	Font color of the measurement model edge labels.
`mm.edge.label.show`	Whether or not to show measurement model edge labels.
`mm.edge.minlen`	Minimum length of the measurement model edges.
`mm.edge.width_multiplier`	The multiplier for measurement model edge penwidth (default = 3).
`mm.edge.width_offset`	The minimal width of an edge of the measurement model (default = 0.5).
`mm.edge.use_outer_weights`	Whether or not to use outer weights as edge labels in the measurement model.
`mm.edge.boot.show_t_value`	Should boot-strapped loadings/weights show a t-value
`mm.edge.boot.show_p_value`	Should boot-strapped loadings/weights show a p-value
`mm.edge.boot.show_p_stars`	Should boot-strapped loadings/weights show significance stars
`mm.edge.boot.show_ci`	Should boot-strapped loadings/weights show a 95 percent confidence interval
`mm.edge.boot.template`	A template string for HTML formatting of edges for loadings/weights
`sm.node.color`	Color of the structural model nodes.
`sm.node.fill`	Fill of the structural model nodes.
`sm.node.label.fontsize`	Font size of the structural model node labels.
`sm.node.label.fontcolor`	Font color of the structural model node labels.
`sm.node.endo.template`	A template string for the nodes of endogenous constructs
`sm.node.exo.template`	A template string for the nodes of exogenous constructs
`sm.edge.boot.show_t_value`	Should boot-strapped path coefficients show a t-value
`sm.edge.boot.show_p_value`	Should boot-strapped path coefficients show a p-value
`sm.edge.boot.show_p_stars`	Should boot-strapped path coefficients show significance stars
`sm.edge.boot.show_ci`	Should boot-strapped path coefficients show a 95 percent confidence interval
`sm.edge.boot.template`	A template string for HTML formatting of edges
`sm.edge.positive.color`	Color of the structural model edges, when values are positive.
`sm.edge.negative.color`	Color of the structural model edges, when values are negative.
`sm.edge.positive.style`	Style of the structural model edges, when values are positive.
`sm.edge.negative.style`	Style of the structural model edges, when values are negative.
`sm.edge.label.fontsize`	Font size of the structural model edge labels.
`sm.edge.label.fontcolor`	Font color of the structural model edge labels.
`sm.edge.label.show`	Whether or not to show edge labels on structural model edges.
`sm.edge.label.all_betas`	Whether to label both endogenous and exogenous paths with a beta (default = TRUE).
`sm.edge.minlen`	Minimum length of the structural model edges.
`sm.edge.width_offset`	The minimal width of an edge of the structural model (default = 0.5).
`sm.edge.width_multiplier`	The multiplier for structural model edges (default = 5).
`construct.reflective.shape`	Dot shape of reflective constructs
`construct.reflective.arrow`	Direction of the arrow for reflective constructs. Can be forward, backward (default), or none.
`construct.reflective.use_weights`	Should measurements from reflective constructs show weights (TRUE) or loadings (FALSE: default).
`construct.compositeA.shape`	Dot shape of composite constructs using correlation weights
`construct.compositeA.arrow`	Direction of the arrow for constructs using correlation weight (default: backward)
`construct.compositeA.use_weights`	Should measurements from constructs using correlation weights show weights (TRUE) or loadings (FALSE: default).
`construct.compositeB.shape`	Dot shape of composite constructs using regression weights
`construct.compositeB.arrow`	Direction of the arrow for constructs using regression weights (default: forward)
`construct.compositeB.use_weights`	Should measurements from constructs using regression weights show weights (TRUE: default) or loadings (FALSE).
`manifest.reflective.shape`	Dot shape of manifest variables of reflective constructs
`manifest.compositeA.shape`	Dot shape of manifest variables of composite constructs using correlation weights
`manifest.compositeB.shape`	Dot shape of manifest variables of composite constructs using regression weights
`...`	additional parameters (unused)

Details

You can use the auto-complete feature of your editor to help you find the right parameter.

General settings start with plot.*

Measurement model settings start with mm.*

Structural model settings start with sm.*

Setting the shape of manifest or construct variables depending on their estimation type can be found under manifest.* and construct.*

Value

A seminr.theme object that can be supplied to dot_graph

The theme function for an inverted theme on black background.

Description

The theme function for an inverted theme on black background.

Usage

seminr_theme_dark(
  plot.title.fontsize = 24,
  mm.node.label.fontsize = 8,
  sm.node.label.fontsize = 12,
  mm.edge.label.fontsize = 7,
  sm.edge.label.fontsize = 9
)
seminr_theme_dark(
  plot.title.fontsize = 24,
  mm.node.label.fontsize = 8,
  sm.node.label.fontsize = 12,
  mm.edge.label.fontsize = 7,
  sm.edge.label.fontsize = 9
)

Arguments

`plot.title.fontsize`	Title font size
`mm.node.label.fontsize`	Font size for measurement variables
`sm.node.label.fontsize`	Font size for constructs
`mm.edge.label.fontsize`	Font size for measurement model edges
`sm.edge.label.fontsize`	Font size for path edges

Value

a theme object

Get and set the active theme

Description

The current/active theme (see [seminr_theme()]) is automatically applied to every graph you draw. Use 'seminr_theme_get()' to get the current theme, and 'seminr_theme_set()' to completely override it.

Usage

seminr_theme_get()

seminr_theme_set(new)
seminr_theme_get()

seminr_theme_set(new)

Arguments

new

new theme (a list of theme elements)

A theme function for a basic b/w theme

Description

A theme function for a basic b/w theme

Usage

seminr_theme_old(
  plot.title.fontsize = 24,
  mm.node.label.fontsize = 8,
  sm.node.label.fontsize = 12,
  mm.edge.label.fontsize = 7,
  sm.edge.label.fontsize = 9
)
seminr_theme_old(
  plot.title.fontsize = 24,
  mm.node.label.fontsize = 8,
  sm.node.label.fontsize = 12,
  mm.edge.label.fontsize = 7,
  sm.edge.label.fontsize = 9
)

Arguments

`plot.title.fontsize`	Title font size
`mm.node.label.fontsize`	Font size for measurement variables
`sm.node.label.fontsize`	Font size for constructs
`mm.edge.label.fontsize`	Font size for measurement model edges
`sm.edge.label.fontsize`	Font size for path edges

Value

a theme object

A colored theme

Description

A colored theme

A theme function for a modern approach of visualizing PLS models in b/w

Usage

seminr_theme_smart(
  plot.title.fontsize = 24,
  mm.node.label.fontsize = 8,
  sm.node.label.fontsize = 12,
  mm.edge.label.fontsize = 7,
  sm.edge.label.fontsize = 9
)

seminr_theme_default(
  plot.title.fontsize = 24,
  mm.node.label.fontsize = 8,
  sm.node.label.fontsize = 12,
  mm.edge.label.fontsize = 7,
  sm.edge.label.fontsize = 9,
  construct.reflective.shape = "ellipse",
  construct.compositeA.shape = "hexagon",
  construct.compositeB.shape = "hexagon",
  construct.reflective.arrow = "backward",
  construct.compositeA.arrow = "backward",
  construct.compositeB.arrow = "forward",
  ...
)
seminr_theme_smart(
  plot.title.fontsize = 24,
  mm.node.label.fontsize = 8,
  sm.node.label.fontsize = 12,
  mm.edge.label.fontsize = 7,
  sm.edge.label.fontsize = 9
)

seminr_theme_default(
  plot.title.fontsize = 24,
  mm.node.label.fontsize = 8,
  sm.node.label.fontsize = 12,
  mm.edge.label.fontsize = 7,
  sm.edge.label.fontsize = 9,
  construct.reflective.shape = "ellipse",
  construct.compositeA.shape = "hexagon",
  construct.compositeB.shape = "hexagon",
  construct.reflective.arrow = "backward",
  construct.compositeA.arrow = "backward",
  construct.compositeB.arrow = "forward",
  ...
)

Arguments

`plot.title.fontsize`	Title font size
`mm.node.label.fontsize`	Font size for measurement variables
`sm.node.label.fontsize`	Font size for constructs
`mm.edge.label.fontsize`	Font size for measurement model edges
`sm.edge.label.fontsize`	Font size for path edges
`construct.reflective.shape`	Shape of reflective constructs
`construct.compositeA.shape`	Shape of composite constructs mode A
`construct.compositeB.shape`	Shape of composite constructs mode B
`construct.reflective.arrow`	Direction of arrows of reflective constructs
`construct.compositeA.arrow`	Direction of arrows of composite constructs mode A
`construct.compositeB.arrow`	Direction of arrows of composite constructs mode B
`...`	Other parameters for the seminr_theme_create function

Value

a theme object

seminr simplePLS Function

Description

The seminr package provides a natural syntax for researchers to describe PLS structural equation models. seminr is compatible with simplePLS. simplePLS provides the verb for estimating a pls model.

Usage

simplePLS(obsData,smMatrix, mmMatrix,inner_weights = path_weighting,
          maxIt=300, stopCriterion=7,measurement_mode_scheme)
simplePLS(obsData,smMatrix, mmMatrix,inner_weights = path_weighting,
          maxIt=300, stopCriterion=7,measurement_mode_scheme)

Arguments

`obsData`	A `dataframe` containing the indicator measurement data.
`smMatrix`	A source-to-target matrix representing the inner/structural model, generated by `relationships`.
`mmMatrix`	A source-to-target matrix representing the outer/measurement model, generated by `constructs`.
`inner_weights`	A parameter declaring which inner weighting scheme should be used path_weighting is default, alternately path_factorial can be used.
`maxIt`	The maximum number of iterations to run (default is 300).
`stopCriterion`	The criterion to stop iterating (default is 7).
`measurement_mode_scheme`	A named list of constructs and measurement scheme functions

Value

A list of the estimated parameters for the SimplePLS model including:

`meanData`	A vector of the indicator means.
`sdData`	A vector of the indicator standard deviations
`mmMatrix`	A Matrix of the measurement model relations.
`smMatrix`	A Matrix of the structural model relations.
`constructs`	A vector of the construct names.
`mmVariables`	A vector of the indicator names.
`outer_loadings`	The matrix of estimated indicator loadings.
`outer_weights`	The matrix of estimated indicator weights.
`path_coef`	The matrix of estimated structural model relationships.
`iterations`	A numeric indicating the number of iterations required before the algorithm converged.
`weightDiff`	A numeric indicating the minimum weight difference between iterations of the algorithm.
`construct_scores`	A matrix of the estimated construct scores for the PLS model.
`rSquared`	A matrix of the estimated R Squared for each construct.
`inner_weights`	The inner weight estimation function.

Examples

#seminr syntax for creating measurement model
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )
#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

mobi_pls <- estimate_pls(data = mobi,
                           measurement_model = mobi_mm,
                           structural_model = mobi_sm)

#seminr syntax for creating measurement model
mobi_mm <- constructs(
             reflective("Image",        multi_items("IMAG", 1:5)),
             reflective("Expectation",  multi_items("CUEX", 1:3)),
             reflective("Quality",      multi_items("PERQ", 1:7)),
             reflective("Value",        multi_items("PERV", 1:2)),
             reflective("Satisfaction", multi_items("CUSA", 1:3)),
             reflective("Complaints",   single_item("CUSCO")),
             reflective("Loyalty",      multi_items("CUSL", 1:3))
           )
#seminr syntax for creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

mobi_pls <- estimate_pls(data = mobi,
                           measurement_model = mobi_mm,
                           structural_model = mobi_sm)

Single-item measurement model specification

Description

single_item specifies a single item name to be assigned to a construct.

Usage

single_item(item)
single_item(item)

Arguments

item

Name of item

Value

A vector of a single indicator for a composite.

Examples

  mobi_mm <- constructs(
    composite("Image",        multi_items("IMAG", 1:5), weights = correlation_weights),
    composite("Expectation",  multi_items("CUEX", 1:3), weights = mode_A),
    composite("Quality",      multi_items("PERQ", 1:7), weights = regression_weights),
    composite("Value",        single_item("PERV1"))
  )
mobi_mm <- constructs(
    composite("Image",        multi_items("IMAG", 1:5), weights = correlation_weights),
    composite("Expectation",  multi_items("CUEX", 1:3), weights = mode_A),
    composite("Quality",      multi_items("PERQ", 1:7), weights = regression_weights),
    composite("Value",        single_item("PERV1"))
  )

Function for plotting a slope analysis for an interaction in a PLS model

Description

slope_analysis generates an interaction plot for the effect of an antecedent on an outcome given a mediator variable.

Usage

slope_analysis(moderated_model, dv, moderator, iv,  leg_place)
slope_analysis(moderated_model, dv, moderator, iv,  leg_place)

Arguments

`moderated_model`	A SEMinR model that contains an interaction.
`dv`	The name of the dependant consutruct affected by the moderator (interaction term).
`moderator`	The name of the moderator construct.
`iv`	The name of the independant construct affected by the moderator.
`leg_place`	The location of the legend, in order to make sure the legend does not obscure the plot lines.

Examples

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
composite("Image",        multi_items("IMAG", 1:5)),
composite("Expectation",  multi_items("CUEX", 1:3)),
composite("Value",        multi_items("PERV", 1:2)),
composite("Satisfaction", multi_items("CUSA", 1:3)),
interaction_term(iv = "Image", moderator = c("Expectation"), method = orthogonal))

# Structural model
#  note: interactions should be the names of its main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                 "Image*Expectation")))

# Load data, assemble model, and estimate
mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm)

slope_analysis(mobi_pls, "Satisfaction", "Expectation", "Image", "bottomright")

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
composite("Image",        multi_items("IMAG", 1:5)),
composite("Expectation",  multi_items("CUEX", 1:3)),
composite("Value",        multi_items("PERV", 1:2)),
composite("Satisfaction", multi_items("CUSA", 1:3)),
interaction_term(iv = "Image", moderator = c("Expectation"), method = orthogonal))

# Structural model
#  note: interactions should be the names of its main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                 "Image*Expectation")))

# Load data, assemble model, and estimate
mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm)

slope_analysis(mobi_pls, "Satisfaction", "Expectation", "Image", "bottomright")

seminr specific effect significance function

Description

The seminr package provides a natural syntax for researchers to describe PLS structural equation models. specific_effect_significance provides the verb for calculating the bootstrap mean, standard deviation, T value, and confidence intervals for direct or mediated path in a bootstrapped SEMinR model.

Usage

specific_effect_significance(boot_seminr_model, from, to, through, alpha)
specific_effect_significance(boot_seminr_model, from, to, through, alpha)

Arguments

`boot_seminr_model`	A bootstrapped model returned by the `bootstrap_model` function.
`from`	A parameter specifying the antecedent composite for the path.
`to`	A parameter specifying the outcome composite for the path.
`through`	A parameter to specify a vector of mediators for the path. Default is NULL.
`alpha`	A parameter for specifying the alpha for the confidence interval. Default is 0.05.

Value

A vector of lower and upper confidence intervals for a path.

References

Zhao, X., Lynch Jr, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of consumer research, 37(2), 197-206.

Examples

mobi_mm <- constructs(
composite("Image",        multi_items("IMAG", 1:5)),
composite("Expectation",  multi_items("CUEX", 1:3)),
composite("Quality",      multi_items("PERQ", 1:7)),
composite("Value",        multi_items("PERV", 1:2)),
composite("Satisfaction", multi_items("CUSA", 1:3)),
composite("Complaints",   single_item("CUSCO")),
composite("Loyalty",      multi_items("CUSL", 1:3))
)

# Creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

# Estimating the model
mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm)

# Load data, assemble model, and bootstrap
boot_seminr_model <- bootstrap_model(seminr_model = mobi_pls,
                                     nboot = 50, cores = 2, seed = NULL)

specific_effect_significance(boot_seminr_model = boot_seminr_model,
                             from = "Image",
                             through = c("Expectation", "Satisfaction","Complaints"),
                             to = "Loyalty",
                             alpha = 0.05)
mobi_mm <- constructs(
composite("Image",        multi_items("IMAG", 1:5)),
composite("Expectation",  multi_items("CUEX", 1:3)),
composite("Quality",      multi_items("PERQ", 1:7)),
composite("Value",        multi_items("PERV", 1:2)),
composite("Satisfaction", multi_items("CUSA", 1:3)),
composite("Complaints",   single_item("CUSCO")),
composite("Loyalty",      multi_items("CUSL", 1:3))
)

# Creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

# Estimating the model
mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm)

# Load data, assemble model, and bootstrap
boot_seminr_model <- bootstrap_model(seminr_model = mobi_pls,
                                     nboot = 50, cores = 2, seed = NULL)

specific_effect_significance(boot_seminr_model = boot_seminr_model,
                             from = "Image",
                             through = c("Expectation", "Satisfaction","Complaints"),
                             to = "Loyalty",
                             alpha = 0.05)

seminr specify_model() function

Description

Combines model components together into a single specified_model object for estimation functions

Usage

specify_model(
  measurement_model,
  structural_model = NULL,
  item_associations = NULL
)
specify_model(
  measurement_model,
  structural_model = NULL,
  item_associations = NULL
)

Arguments

`measurement_model`	An optional `measurement_model` object representing the outer/measurement model, as generated by `constructs`.
`structural_model`	An optional `smMatrix` object representing the inner/structural model, as generated by `relationships`.
`item_associations`	An item-to-item matrix representing error covariances that are freed for estimation. This matrix is created by `associations()`, or defaults to NULL (no inter-item associations).

Value

A list containing a SEMinR measurement model, structural model, and item associations.

Standardize (scale) a matrix/df and report interpretable errors

Description

Standardize (scale) a matrix/df and report interpretable errors

Usage

standardize_safely(x)
standardize_safely(x)

Arguments

`x`	vector, data.frame, or matrix

Value

scaled object as returned by scale function

seminr total indirect confidence intervals function

Description

total_indirect_ci provides the verb for calculating the total indirect confidence intervals of a direct or mediated path in a bootstrapped SEMinR model.

Usage

total_indirect_ci(boot_seminr_model, from, to, alpha)
total_indirect_ci(boot_seminr_model, from, to, alpha)

Arguments

`boot_seminr_model`	A bootstrapped model returned by the `bootstrap_model` function.
`from`	A parameter specifying the antecedent composite for the path.
`to`	A parameter specifying the outcome composite for the path.
`alpha`	A parameter for specifying the alpha for the confidence interval. Default is 0.05.

Value

A vector of lower and upper confidence intervals for a path.

References

Zhao, X., Lynch Jr, J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of consumer research, 37(2), 197-206.

Examples

mobi_mm <- constructs(
composite("Image",        multi_items("IMAG", 1:5)),
composite("Expectation",  multi_items("CUEX", 1:3)),
composite("Quality",      multi_items("PERQ", 1:7)),
composite("Value",        multi_items("PERV", 1:2)),
composite("Satisfaction", multi_items("CUSA", 1:3)),
composite("Complaints",   single_item("CUSCO")),
composite("Loyalty",      multi_items("CUSL", 1:3))
)

# Creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

# Estimating the model
mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm)

# Load data, assemble model, and bootstrap
boot_seminr_model <- bootstrap_model(seminr_model = mobi_pls,
                                     nboot = 50, cores = 2, seed = NULL)

total_indirect_ci(boot_seminr_model = boot_seminr_model,
                  from = "Image",
                  to = "Loyalty",
                  alpha = 0.05)
mobi_mm <- constructs(
composite("Image",        multi_items("IMAG", 1:5)),
composite("Expectation",  multi_items("CUEX", 1:3)),
composite("Quality",      multi_items("PERQ", 1:7)),
composite("Value",        multi_items("PERV", 1:2)),
composite("Satisfaction", multi_items("CUSA", 1:3)),
composite("Complaints",   single_item("CUSCO")),
composite("Loyalty",      multi_items("CUSL", 1:3))
)

# Creating structural model
mobi_sm <- relationships(
  paths(from = "Image",        to = c("Expectation", "Satisfaction", "Loyalty")),
  paths(from = "Expectation",  to = c("Quality", "Value", "Satisfaction")),
  paths(from = "Quality",      to = c("Value", "Satisfaction")),
  paths(from = "Value",        to = c("Satisfaction")),
  paths(from = "Satisfaction", to = c("Complaints", "Loyalty")),
  paths(from = "Complaints",   to = "Loyalty")
)

# Estimating the model
mobi_pls <- estimate_pls(data = mobi,
                         measurement_model = mobi_mm,
                         structural_model = mobi_sm)

# Load data, assemble model, and bootstrap
boot_seminr_model <- bootstrap_model(seminr_model = mobi_pls,
                                     nboot = 50, cores = 2, seed = NULL)

total_indirect_ci(boot_seminr_model = boot_seminr_model,
                  from = "Image",
                  to = "Loyalty",
                  alpha = 0.05)

Creates an interaction measurement item using a two-stage approach. The two-stage procedure for both PLS and CBSEM models estimates construct scores in the first stage, and uses them to produce a single-item product item for the interaction term in the second stage. For a PLS model, the first stage uses PLS to compute construct scores. For a CBSEM model, the first stage uses a CFA to produce ten Berge construct scores.

Description

Creates an interaction measurement item using a two-stage approach. The two-stage procedure for both PLS and CBSEM models estimates construct scores in the first stage, and uses them to produce a single-item product item for the interaction term in the second stage. For a PLS model, the first stage uses PLS to compute construct scores. For a CBSEM model, the first stage uses a CFA to produce ten Berge construct scores.

Usage

# two stage approach as per Henseler & Chin (2010):
 two_stage(iv, moderator, weights)
# two stage approach as per Henseler & Chin (2010):
 two_stage(iv, moderator, weights)

Arguments

`iv`	The independent variable that is subject to moderation.
`moderator`	The moderator variable.
`weights`	is the relationship between the items and the interaction terms. This can be specified as `correlation_weights` or `mode_A` for correlation weights (Mode A) or as `regression_weights` or `mode_B` for regression weights (Mode B). Default is correlation weights.

Value

An un-evaluated function (promise) for estimating a two-stage interaction effect.

References

Examples

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3)),
  interaction_term(iv = "Image", moderator = "Expectation", method = two_stage)
)

#  structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                 "Image*Expectation"))
)

# PLS example:
mobi_pls <- estimate_pls(mobi, mobi_mm, mobi_sm)
summary(mobi_pls)

# CBSEM example:
mobi_cbsem <- estimate_cbsem(mobi, as.reflective(mobi_mm), mobi_sm)
summary(mobi_cbsem)

data(mobi)

# seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3)),
  interaction_term(iv = "Image", moderator = "Expectation", method = two_stage)
)

#  structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                 "Image*Expectation"))
)

# PLS example:
mobi_pls <- estimate_pls(mobi, mobi_mm, mobi_sm)
summary(mobi_pls)

# CBSEM example:
mobi_cbsem <- estimate_cbsem(mobi, as.reflective(mobi_mm), mobi_sm)
summary(mobi_cbsem)

Outer weighting scheme functions to estimate construct weighting.

Description

mode_A, correlation_weights and mode_B, regression_weights and unit_weights specify the outer weighting scheme to be used in the estimation of the construct weights and score.

Usage

unit_weights(mmMatrix, i, normData, construct_scores)
unit_weights(mmMatrix, i, normData, construct_scores)

Arguments

`mmMatrix`	is the `measurement_model` - a source-to-target matrix representing the measurement model, generated by `constructs`.
`i`	is the name of the construct to be estimated.
`normData`	is the dataframe of the normalized item data.
`construct_scores`	is the matrix of construct scores generated by `estimate_model`.

Value

A matrix of estimated measurement model relations.

Package 'seminr'

Help Index

Converts all contructs of a measurement model, or just a single construct into reflective factors.

Description

Usage

Arguments

Value

See Also

Examples

Converts a contruct of a measurement model into a reflective factor.

Description

Usage

Arguments

Value

See Also

Examples

Converts interaction of a measurement model into a reflective factors.

Description

Usage

Arguments

Value

See Also

Examples

Converts all contructs of a measurement model, or just a single construct into reflective factors.

Description

Usage

Arguments

Value

See Also

Examples

Specifies inter-item covariances that should be supplied to CBSEM estimation (estimate_cbsem) or CFA estimation (estimate_cfa)

Description

Usage

Arguments

Value

Examples

Return all path bootstraps as a long dataframe. Columns of the dataframes are specified paths and rows are the estimated coefficients for the paths at each bootstrap iteration.

Description

Usage

Arguments

Examples

seminr bootstrap_model Function

Description

Usage

Arguments

Value

References

See Also

Examples

Open Edotor graphViz Website with the preloaded in the Browser

Description

Usage

Arguments

Examples

A function to create regression plots (maybe not needed?)

Description

Usage

Arguments

Value

Composite construct measurement model specification

Description

Usage

Arguments

Details

Value

See Also

Examples

Function to calculate Akaike weights for IT Criteria

Description

Usage

Arguments

Measurement functions

Description

Usage

Arguments

Details

Value

See Also

Examples

Returns R-sq of a dv given correlation matrix of ivs, dv cors <- cbsem_summary$descriptives$correlations$constructs cor_rsq(cors, dv_name = "Value", iv_names = c("Image", "Quality"))

Specifies inter-item covariances that should be supplied to CBSEM estimation (`estimate_cbsem`) or CFA estimation (`estimate_cfa`)