estimate_pls: seminr estimate_pls() function

Description Usage Arguments Value See Also Examples

View source: R/estimate_pls.R

Description

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

Usage

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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.

See Also

specify_model relationships constructs paths interaction_term bootstrap_model

Examples

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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)

seminr documentation built on Sept. 5, 2021, 5:36 p.m.