treemox.boot: Bootstrapping validation for PATHMOX or TECHMOX trees

In pathmox: Pathmox Approach of Segmentation Trees in Partial Least Squares Path Modeling

Description

Performs bootstrapping validation on path coefficients of terminal nodes from a PATHMOX or TECHMOX tree

Usage

  treemox.boot(pls, treemox, X = NULL, br = 100)

Arguments

pls	An object of class "plspm" returned by plspm.
treemox	An object of class "treemox" returned by either pathmox or techmox.
X	Optional dataset (matrix or data frame) used when argument dataset=NULL inside pls.
br	An integer indicating the number bootstrap resamples (br=100 by default).

Details

The default number of re-samples is 100. However, br can be specified in a range from 50 to 500.
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 X.

Value

An object of class "bootnodes". Basically a list with the following results:

PC	Matrix of original path coefficients for the root node and the terminal nodes.
PMB	Matrix of bootstrap path coefficients (mean value) for the root node and the terminal nodes.
PSB	Matrix of bootstrap standard errors of path coefficients for the root node and the terminal nodes.
PP05	Matrix of 0.05 bootstrap percentile of path coefficients for the root node and the terminal nodes.
PP95	Matrix of 0.95 bootstrap percentile of path coefficients for the root node and the terminal nodes.

Author(s)

Gaston Sanchez

See Also

pathmox, techmox, treemox.pls.

Examples

## Not run: 
 ## example of PLS-PM in customer satisfaction analysis
 ## model with seven LVs and reflective indicators
 data(csimobile)

 # select manifest variables
 data_mobile = csimobile[,8:33]

 # define path matrix (inner model)
 IMAG = c(0, 0, 0, 0, 0, 0, 0)
 EXPE = c(1, 0, 0, 0, 0, 0, 0)
 QUAL = c(0, 1, 0, 0, 0, 0, 0)
 VAL = c(0, 1, 1, 0, 0, 0, 0)
 SAT = c(1, 1, 1, 1, 0, 0, 0)
 COM = c(0, 0, 0, 0, 1, 0, 0)
 LOY = c(1, 0, 0, 0, 1, 1, 0)
 mob_path = rbind(IMAG, EXPE, QUAL, VAL, SAT, COM, LOY)

 # blocks of indicators (outer model)
 mob_blocks = list(1:5, 6:9, 10:15, 16:18, 19:21, 22:24, 25:26)
 mob_modes = rep("A", 7)

 # apply plspm
 mob_pls = plspm(data_mobile, mob_path, mob_blocks, modes = mob_modes,
                 scheme = "factor", scaled = FALSE)

 # re-ordering those segmentation variables with ordinal scale
 # (Age and Education)
 csimobile$Education = factor(csimobile$Education,
     levels=c("basic","highschool","university"),
     ordered=TRUE)

 # select the segmentation variables
 seg_vars = csimobile[,1:7]

 # Pathmox Analysis
 mob_pathmox = pathmox(mob_pls, seg_vars, signif=.10, size=.10, deep=2)

 # applying function treemox.pls
 mob_nodes_boot = treemox.boot(mob_pls, mob_pathmox)

 # plot of results for path coefficient number 12
 plot(mob_nodes_boot, pc=12)
 
## End(Not run)

pathmox documentation built on May 1, 2019, 11:31 p.m.