plspm (version 0.4.9)

rescale: Rescale Latent Variable Scores

Description

Rescale standardized latent variable scores to original scale of manifest variables

Usage

rescale(pls, data = NULL)

Arguments

pls

object of class "plspm"

data

Optional dataset (matrix or data frame) used when argument dataset=NULL inside pls.

Value

A data frame with the rescaled latent variable scores

Details

rescale requires all outer weights to be positive

See Also

plspm

Examples

## Not run: ------------------------------------
#  ## example with customer satisfaction analysis
# 
#  # 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_modes = rep("A", 6)
# 
#  # apply plspm
#  my_pls = plspm(satisfaction, sat_path, sat_blocks, modes = sat_modes,
#               scaled=FALSE)
# 
#  # rescaling standardized scores of latent variables
#  new_scores = rescale(my_pls)
# 
#  # compare standardized LVs against rescaled LVs
#  summary(my_pls$scores)
#  summary(new_scores)
#  
## ---------------------------------------------