plspm.groups | R Documentation |
Performs a group comparison test for comparing path coefficients between two groups. The null and alternative hypotheses to be tested are: H0: path coefficients are not significantly different; H1: path coefficients are significantly different
plspm.groups(pls, group, Y = NULL, method = "bootstrap", reps = NULL)
pls |
object of class |
group |
factor with 2 levels indicating the groups to be compared |
Y |
optional dataset (matrix or data frame) used
when argument |
method |
method to be used in the test. Possible
values are |
reps |
integer indicating the number of either
bootstrap resamples or number of permutations. If
|
plspm.groups
performs a two groups comparison test
in PLS-PM for comparing path coefficients between two
groups. Only two methods are available: 1) bootstrap, and
2) permutation. The bootstrap test is an adapted t-test
based on bootstrap standard errors. The permutation test
is a randomization test which provides a non-parametric
option.
When the object pls
does not contain a data matrix
(i.e. pls$data=NULL
), the user must provide the
data matrix or data frame in Y
.
An object of class "plspm.groups"
test |
Table with the results of the applied test. Includes: path coefficients of the global model, path coeffs of group1, path coeffs of group2, (absolute) difference of path coeffs between groups, and the test results with the p-value. |
global |
List with inner model results for the global model |
group1 |
List with inner model results for group1 |
group2 |
List with inner model results for group2 |
Gaston Sanchez
Chin, W.W. (2003) A permutation procedure for multi-group comparison of PLS models. In: Vilares M., Tenenhaus M., Coelho P., Esposito Vinzi V., Morineau A. (Eds.) PLS and Related Methods - Proceedings of the International Symposium PLS03. Decisia, pp. 33-43.
Chin, W.W. (2000) Frequently Asked Questions, Partial Least Squares PLS-Graph. Available from: http://disc-nt.cba.uh.edu/chin/plsfaq/multigroup.htm
plspm
## Not run: ## example with customer satisfaction analysis ## group comparison based on the segmentation variable "gender" # load data satisfaction data(satisfaction) # define inner model matrix IMAG = c(0,0,0,0,0,0) EXPE = c(1,0,0,0,0,0) QUAL = c(0,1,0,0,0,0) VAL = c(0,1,1,0,0,0) SAT = c(1,1,1,1,0,0) LOY = c(1,0,0,0,1,0) sat_path = rbind(IMAG, EXPE, QUAL, VAL, SAT, LOY) # define outer model list sat_blocks = list(1:5, 6:10, 11:15, 16:19, 20:23, 24:27) # define vector of reflective modes sat_mod = rep("A", 6) # apply plspm satpls = plspm(satisfaction, sat_path, sat_blocks, modes = sat_mod, scaled = FALSE) # permutation test with 100 permutations group_perm = plspm.groups(satpls, satisfaction$gender, method="permutation", reps=100) group_perm ## End(Not run)
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