seminr estimate_pls() function
Estimates a pair of measurement and structural models using PLS-SEM, 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)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. |
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. |
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)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.