seminr (version 0.7.0)

estimate_pls: seminr estimate_pls() function

Description

The seminr package provides a natural syntax for researchers to describe PLS structural equation models.

Usage

estimate_pls(data, measurement_model, interactions=NULL, structural_model,
             inner_weights = path_weighting)

Arguments

data

A dataframe containing the indicator measurement data.

measurement_model

A source-to-target matrix representing the outer/measurement model, generated by constructs.

interactions

An object of type interactions as generated by the interactions method. Default setting is NULL and can be excluded for models with no interactions.

structural_model

A source-to-target matrix representing the inner/structural model, generated by relationships.

inner_weights

A parameter declaring which inner weighting scheme should be used path_weighting is default, alternately path_factorial can be used.

See Also

relationships constructs paths interactions bootstrap_model

Examples

# NOT RUN {
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)

summary(mobi_pls)
plot_scores(mobi_pls)
# }