seminr: Building and Estimating Structural Equation Models

Description

A powerful, easy to use 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 (Ray, Danks, and Valdez 2021 doi:10.2139/ssrn.3900621).

Author(s)

Maintainer: Nicholas Patrick Danks nicholasdanks@hotmail.com

Authors:

• Soumya Ray soumya.ray@gmail.com [thesis advisor]

• André Calero Valdez andrecalerovaldez@gmail.com

Other contributors:

• Juan Manuel Velasquez Estrada [contributor]

• James Uanhoro James.uanhoro@gmail.com [contributor]

• Johannes Nakayama johannes.nakayama@rwth-aachen.de [contributor]

• Lilian Koyan lilian.kojan@rwth-aachen.de [contributor]

• Laura Burbach laura.burbach@rwth-aachen.de [contributor]

• Arturo Heynar Cano Bejar arturocano997@gmail.com [contributor]

• Susanne Adler susanne.adler@ovgu.de [contributor]

See Also

Useful links:

• https://github.com/sem-in-r/seminr

• Report bugs at https://github.com/sem-in-r/seminr/issues

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)

Arguments

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).

See Also

relationships constructs paths interaction_term bootstrap_model

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)

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, ...)

Arguments

Value

A list of reflective constructs.

See Also

as.reflective.measurement_model, as.reflective.construct

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)

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, ...)

Arguments

Value

A list of reflective constructs.

See Also

as.reflective.measurement_model

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)

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, ...)

Arguments

Value

A list of reflective constructs.

See Also

as.reflective.measurement_model

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)

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, ...)

Arguments

Value

A list of reflective constructs.

See Also

as.reflective.construct

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)

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(...)

Arguments

Value

A matrix of items that covary.

Examples

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)

Arguments

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)

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, ...)

Arguments

Value

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

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.

See Also

relationships constructs paths interaction_term

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)

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())

Arguments

Examples

## 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)

Arguments

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)

Arguments

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.

See Also

See constructs, reflective

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)
  )

Function to calculate Akaike weights for IT Criteria

Description

Function to calculate Akaike weights for IT Criteria

Usage

compute_itcriteria_weights(vector_of_itcriteria)

Arguments

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(...)

Arguments

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).

See Also

See composite, reflective

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))
  )

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)

Arguments

Measurement Instrument for the Corporate Reputation Model

Description

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

Usage

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")

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

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")

seminr csem2seminr() function

Description

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

Usage

csem2seminr(lav_syntax)

Arguments

Value

A SEMinR model.

See Also

estimate_pls

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))

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)

Arguments

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)

Arguments

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,
  ...
)

Arguments

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)

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
)

Arguments

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)

Arguments

The default template for labeling bootstrapped edges

Description

The default template for labeling bootstrapped edges

Usage

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()

Value

The template string

Wrap a text in single quotes

Description

Wrap a text in single quotes

Usage

esc_node(x)

Arguments

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", ...)

Arguments

Value

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

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.

See Also

as.reflective relationships constructs paths associations item_errors

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

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", ...)

Arguments

Value

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

References

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

See Also

constructs reflective associations item_errors as.reflective

#' @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)) )

#seminr syntax for freeing up item-item covariances mobi_am <- associations( item_errors(c("PERQ1", "PERQ2"), "CUEX3"), item_errors("IMAG1", "CUEX2") )

mobi_cfa <- estimate_cfa(mobi, mobi_mm, mobi_am)

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)

Arguments

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 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,
             assess_syntax = FALSE)

Arguments

Value

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

See Also

specify_model relationships constructs paths interaction_term bootstrap_model

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)

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, ...)

Arguments

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)

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)

Arguments

Helper function that applies formatting to each construct

Description

Helper function that applies formatting to each construct

Usage

extract_htmt_nodes(model, theme)

Arguments

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)

Arguments

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)

Arguments

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)

Arguments

gets the individual nodes and applies formatting

Description

gets the individual nodes and applies formatting

Usage

extract_mm_nodes(index, model, theme)

Arguments

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)

Arguments

Value

Returns a string of the structural model in dot notation.

seminr fSquared Function

Description

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

Usage

fSquared(seminr_model, iv, dv)

Arguments

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")

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)

Arguments

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)

Arguments

Value

Returns the formatted string

Gets the optimal size for construct elements in the plot

Description

Currently orients on reflective theme settings

Usage

get_construct_element_size(model, theme)

Arguments

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)

Arguments

Value

Returns a character string

Get dot string for global theme options

Description

Get dot string for global theme options

Usage

get_global_htmt_style(theme, layout = "dot")

Arguments

Get dot string for global theme options

Description

Get dot string for global theme options

Usage

get_global_style(theme, layout = "dot")

Arguments

Gets the optimal size for manifest elements in the plot

Description

Currently orients on reflective theme settings

Usage

get_manifest_element_size(model, theme)

Arguments

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)

Arguments

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)

Arguments

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)

Arguments

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)

Arguments

Value

Returns a string that determines the shape of a node

Get Documentation for theme options to use in Addin

Description

Get Documentation for theme options to use in Addin

Usage

get_theme_doc()

Value

Returns a data.frame with the documented arguments

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)

Arguments

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)

Arguments

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)

Arguments

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.

See Also

See constructs, reflective

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)
  )

higher_reflective

Description

higher_reflective creates a higher-order reflective construct

Usage

higher_reflective(construct_name, dimensions)

Arguments

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.

See Also

See constructs, reflective

Examples

  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

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")

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)

Arguments

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)

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)

Arguments

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)

Arguments

Value

A vector of items that covary.

Examples

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

Retrieve the last plot to be modified or created.

Description

Retrieve the last plot to be modified or created.

Usage

last_seminr_plot()

See Also

[save_plot()]

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)

Arguments

Details

mean_replacement provides the verb for replacing all omitted values (NA only) in the dataset with the mean of the variable. It gives a warning if more than 5

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

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")

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)

Arguments

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)

Arguments

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, ...)

Arguments

Value

A vector of numbered indicators.

See Also

See single_item

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)
  )

The default template for labeling endogenous construct nodes

Description

The default template for labeling endogenous construct nodes

Usage

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()

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)

Arguments

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)

Create a list of section content from every documentation page from a package

Description

Create a list of section content from every documentation page from a package

Usage

package_docs(pkg)

Arguments

Value

A list whose names are documentation page titles. Each element is itself a list of the details and sections for a page.

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)

Arguments

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)

Arguments

Value

A matrix of estimated structural relations.

References

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

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, ...)

Arguments

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)

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, ...)

Arguments

Value

Returns the plot.

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
)

Arguments

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)

Arguments

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")

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)

Arguments

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)

Arguments

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)

Arguments

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:

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)

Summary function for PLS-MGA

Description

Summary function for PLS-MGA

Usage

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

Arguments

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)

Arguments

Value

An un-evaluated function (promise) for estimating a product-indicator 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),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)

Format p values for the output and removes trailing numbers when p > .10

Description

Format p values for the output and removes trailing numbers when p > .10

Usage

psignr(pvals, sig.limit = c(0.05, 0.01, 0.001), html = FALSE)

Arguments

Value

A string of asterisks

pvalr(c(0.432, 0.05, 0.00001))

Format p values for the output and removes trailing numbers when p > .10

Description

Format p values for the output and removes trailing numbers when p > .10

Usage

pvalr(pvals, sig.limit = 0.001, digits = 3, html = FALSE)

Arguments

Value

A string formatted p-value including equal and less-than sign

pvalr(c(0.432, 0.05, 0.00001))

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)

Arguments

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.

See Also

See composite, constructs

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))
  )

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)

Arguments

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")
)

Function to report how missing data was handled and how much was missing.

Description

Function to report how missing data was handled and how much was missing.

Usage

report_missing(seminr_model)

Arguments

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)

Arguments

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)

Reruns a previously specified seminr model/analysis

Description

Reruns a previously specified seminr model/analysis

Usage

rerun(x, ...)

Arguments

Value

A re-estimated model of the same class

See Also

rerun.pls_model

Reruns a previously specified seminr PLS model

Description

Reruns a previously specified seminr PLS model

Usage

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

Arguments

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))

seminr rhoC_AVE() function

Description

Get rhoC and AVE for a CFA model estimated with estimate_pls, estimate_cbsem or estimate_cfa. Dillon-Goldstein's Rho as per: Dillon, W. R, and M. Goldstein. 1987. Multivariate Analysis: Methods and Applications. Biometrical Journal 29 (6). Average Variance Extracted as per: Fornell, C. and D. F. Larcker (February 1981). Evaluating structural equation models with unobservable variables and measurement error, Journal of Marketing Research, 18, pp. 39-5

Usage

rhoC_AVE(x, constructs = NULL)

Arguments

Value

A matrix containing the rhoC and AVE metrics for each construct.

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)

Arguments

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).

See Also

relationships constructs paths interaction_term bootstrap_model

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)

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
)

Arguments

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)

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",
  ...
)

Arguments

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
)

Arguments

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)

Arguments

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
)

Arguments

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",
  ...
)

Arguments

Value

a theme object

Set the last plot to be fetched by lastplot()

Description

Set the last plot to be fetched by lastplot()

Usage

set_last_seminr_plot(value)

See Also

[last_plot()]

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)

Arguments

Value

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

See Also

relationships constructs paths interaction_term estimate_pls bootstrap_model

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)

Single-item measurement model specification

Description

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

Usage

single_item(item)

Arguments

Value

A vector of a single indicator for a composite.

See Also

See multi_items

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"))
  )

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)

Arguments

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")

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)

Arguments

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.

See Also

bootstrap_model

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)

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
)

Arguments

Value

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

See Also

estimate_pls estimate_cbsem estimate_cfa

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

Description

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

Usage

standardize_safely(x)

Arguments

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)

Arguments

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.

See Also

bootstrap_model

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)

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)

Arguments

Value

An un-evaluated function (promise) for estimating a two-stage 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 = 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)

Arguments

Value

A matrix of estimated measurement model relations.

Should a construct type use weights or loadings

Description

Should a construct type use weights or loadings

Usage

use_construct_weights(theme, construct_type)

Arguments

Value

TRUE if weights should be used, FALSE if loadings