pathmox (version 0.2.0)

treemox.boot: Bootstrapping validation for PATHMOX or TECHMOX trees

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).

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.

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.

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)