View source: R/estimate_pls_mga.R
estimate_pls_mga | R Documentation |
Performs PLS-MGA to report significance of path differences between two subgroups of data
estimate_pls_mga(pls_model, condition, nboot = 2000, ...)
pls_model |
SEMinR PLS model estimated on the full sample |
condition |
logical vector of TRUE/FALSE indicating which rows of sample data are in group 1 |
nboot |
number of bootstrap resamples to use in PLS-MGA |
... |
any further parameters for bootstrapping (e.g., cores) |
Henseler, J., Ringle, C. M. & Sinkovics, R. R. New Challenges to International Marketing. Adv Int Marketing 277–319 (2009) doi:10.1108/s1474-7979(2009)0000020014
mobi <- 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("Quality", multi_items("PERQ", 1:7)), composite("Value", multi_items("PERV", 1:2)), composite("Satisfaction", multi_items("CUSA", 1:3)), composite("Complaints", single_item("CUSCO")), composite("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) # Should usually use nboot ~2000 and don't specify cores for full parallel processing mobi_mga <- estimate_pls_mga(mobi_pls, mobi$CUEX1 < 8, nboot=50, cores = 2)
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