View source: R/estimate_simplePLS.R
simplePLS | R Documentation |
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.
simplePLS(obsData,smMatrix, mmMatrix,inner_weights = path_weighting,
maxIt=300, stopCriterion=7,measurement_mode_scheme)
obsData |
A |
smMatrix |
A source-to-target matrix representing the inner/structural model,
generated by |
mmMatrix |
A source-to-target matrix representing the outer/measurement model,
generated by |
inner_weights |
A parameter declaring which inner weighting scheme should be used path_weighting is default, alternately path_factorial can be used. |
maxIt |
The maximum number of iterations to run (default is 300). |
stopCriterion |
The criterion to stop iterating (default is 7). |
measurement_mode_scheme |
A named list of constructs and measurement scheme functions |
A list of the estimated parameters for the SimplePLS model including:
meanData |
A vector of the indicator means. |
sdData |
A vector of the indicator standard deviations |
mmMatrix |
A Matrix of the measurement model relations. |
smMatrix |
A Matrix of the structural model relations. |
constructs |
A vector of the construct names. |
mmVariables |
A vector of the indicator names. |
outer_loadings |
The matrix of estimated indicator loadings. |
outer_weights |
The matrix of estimated indicator weights. |
path_coef |
The matrix of estimated structural model relationships. |
iterations |
A numeric indicating the number of iterations required before the algorithm converged. |
weightDiff |
A numeric indicating the minimum weight difference between iterations of the algorithm. |
construct_scores |
A matrix of the estimated construct scores for the PLS model. |
rSquared |
A matrix of the estimated R Squared for each construct. |
inner_weights |
The inner weight estimation function. |
relationships
constructs
paths
interaction_term
estimate_pls
bootstrap_model
#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)
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