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sempls
. The
structural and the measurement must be specified in .csv files. The
paths must be entered in the form of a from-to matrix. The variable in
the first column represents the source and the second column
represents the target of each path.plsm(data, strucmod, measuremod, order=c("generic",
"alphabetical"), interactive=FALSE)mvplot(model, ...)
## S3 method for class 'plsm':
mvplot(model, data, LVs, ask=TRUE, \ldots)
mvpairs(model, ...)
## S3 method for class 'plsm':
mvpairs(model, data, LVs, ask=TRUE, \ldots)
data.frame
intendend to use for the fitting method, sempls
.character
describing how to order the latent variables
(LVs). If
"generic"
the LVs will be ordered according to their
appearance in the causal chain of the structural model (default)."alphab
edit
. The default is FALSE
plsm
.character
vector naming the blocks of LVs for which to
create the plots.?par
par
.plsm
with the elements:character
vector naming the latent variables.character
vector naming the manifest variables.path
representing the structural
model.path
representing the measurement
model.list
naming the MVs belonging to each LV's block
and telling their measurement mode.sempls
, read.splsm
# getting the path to the .csv file representing the inner Model
ptf_Struc <- system.file("ECSIstrucmod.csv", package="semPLS")
# getting the path to the .csv file representing the outer Models
ptf_Meas <- system.file("ECSImeasuremod.csv", package="semPLS")
sm <- as.matrix(read.csv(ptf_Struc))
mm <- as.matrix(read.csv(ptf_Meas))
data(mobi)
ECSI <- plsm(data=mobi, strucmod=sm, measuremod=mm)
# Adjacency matrix of the structural model
ECSI$D
# Adjacency matrix of the measurement model
ECSI$M
# return all elements
ECSI
### Interactive mode
# specify model in a spreadsheets
ECSI <- plsm(data=mobi, interactive=TRUE)
ECSI
### explore blocks of MVs
mvplot(model=ECSI, data=mobi, LVs="Expectation")
mvpairs(model=ECSI, data=mobi, LVs="Expectation")
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