seminr: bootstrap_model – R documentation

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bootstrap_model

seminr bootstrap_model Function

Description

The seminr package provides a natural syntax for researchers to describe PLS structural equation models. bootstrap_model provides the verb for bootstrapping a pls model from the model parameters and data.

Usage

bootstrap_model(seminr_model, nboot = 500, cores = NULL, seed = NULL, ...)

Arguments

`seminr_model`	A fully estimated model with associated data, measurement model and structural model
`nboot`	A parameter specifying the number of bootstrap iterations to perform, default value is 500. If 0 then no bootstrapping is performed.
`cores`	A parameter specifying the maximum number of cores to use in the parallelization.
`seed`	A parameter to specify the seed for reproducibility of results. Default is NULL.
`...`	A list of parameters passed on to the estimation method.

Value

A list of the estimated parameters for the bootstrapped model including:

`boot_paths`	An array of the 'nboot' estimated bootstrap sample path coefficient matrices.
`boot_loadings`	An array of the 'nboot' estimated bootstrap sample item loadings matrices.
`boot_weights`	An array of the 'nboot' estimated bootstrap sample item weights matrices.
`boot_HTMT`	An array of the 'nboot' estimated bootstrap sample model HTMT matrices.
`boot_total_paths`	An array of the 'nboot' estimated bootstrap sample model total paths matrices.
`paths_descriptives`	A matrix of the bootstrap path coefficients and standard deviations.
`loadings_descriptives`	A matrix of the bootstrap item loadings and standard deviations.
`weights_descriptives`	A matrix of the bootstrap item weights and standard deviations.
`HTMT_descriptives`	A matrix of the bootstrap model HTMT and standard deviations.
`total_paths_descriptives`	A matrix of the bootstrap model total paths and standard deviations.

References

Hair, J. F., Hult, G. T. M., Ringle, C. M., and Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM), 2nd Ed., Sage: Thousand Oaks.

Examples

data(mobi)
# seminr syntax for creating measurement model
mobi_mm <- constructs(
  composite("Image",        multi_items("IMAG", 1:5)),
  composite("Expectation",  multi_items("CUEX", 1:3)),
  composite("Value",        multi_items("PERV", 1:2)),
  composite("Satisfaction", multi_items("CUSA", 1:3)),
  interaction_term(iv = "Image", moderator = "Expectation", method = orthogonal),
  interaction_term(iv = "Image", moderator = "Value", method = orthogonal)
)

# structural model: note that name of the interactions construct should be
#  the names of its two main constructs joined by a '*' in between.
mobi_sm <- relationships(
  paths(to = "Satisfaction",
        from = c("Image", "Expectation", "Value",
                 "Image*Expectation", "Image*Value"))
)

seminr_model <- estimate_pls(data = mobi,
                             measurement_model = mobi_mm,
                             structural_model = mobi_sm)

# Load data, assemble model, and bootstrap
boot_seminr_model <- bootstrap_model(seminr_model = seminr_model,
                                     nboot = 50, cores = 2, seed = NULL)

summary(boot_seminr_model)

seminr

Building and Estimating Structural Equation Models

v2.0.2

GPL-3

Authors

Soumya Ray [aut, ths], Nicholas Patrick Danks [aut, cre], André Calero Valdez [aut], Juan Manuel Velasquez Estrada [ctb], James Uanhoro [ctb], Johannes Nakayama [ctb], Lilian Koyan [ctb], Laura Burbach [ctb], Arturo Heynar Cano Bejar [ctb], Susanne Adler [ctb]

Initial release

2021-04-01