estimate_pls: seminr estimate_pls() function
In seminr: Building and Estimating Structural Equation Models

estimate_pls

R Documentation

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)

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)

seminr documentation built on Oct. 14, 2024, 1:07 a.m.