estimate_pls  R Documentation 
Estimates a pair of measurement and structural models using PLSSEM, with optional estimation methods
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)
data 
A The pair of measurement and structural models can optionally be specified as separate model objects 
measurement_model 
An optional 
structural_model 
An optional The pair of measurement and structural models can also be specified as a single 
model 
An optional 
inner_weights 
Function that implements inner weighting scheme:

missing 
Function that replaces missing values.

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