Description Usage Arguments Details Value Author(s) See Also Examples

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

1 | ```
treemox.boot(pls, treemox, X = NULL, br = 100)
``` |

`pls` |
An object of class |

`treemox` |
An object of class |

`X` |
Optional dataset (matrix or data frame) used
when argument |

`br` |
An integer indicating the number bootstrap
resamples ( |

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`

.

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. |

Gaston Sanchez

`pathmox`

, `techmox`

,
`treemox.pls`

.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 | ```
## 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)
``` |

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