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sempls
fits structural equation models by the patial least
squares (PLS) method. The estimation is based on the raw data and
requires no distributional assumptions.sempls(model, ...)## S3 method for class 'plsm':
sempls(model, data, maxit=20, tol=1e-7,
scaled=TRUE, sum1=FALSE, wscheme="centroid", pairwise=FALSE,
method=c("pearson", "kendall", "spearman"),
convCrit=c("relative", "square"),
verbose=TRUE, ...)
## S3 method for class 'sempls':
print(x, digits=2, \ldots)
## S3 method for class 'sempls':
plot(x, \ldots)
## S3 method for class 'sempls':
densityplot(x, data, use=c("fscores", "prediction",
"residuals"), ...)
pathCoeff(object, ...)
## S3 method for class 'sempls':
pathCoeff(object, \ldots)
## S3 method for class 'pathCoeff':
print(x, na.print=".", digits=2, abbreviate=FALSE, \ldots)
totalEffects(object)
## S3 method for class 'sempls':
totalEffects(object)
## S3 method for class 'totalEffects':
print(x, na.print=".", digits=2, abbreviate=FALSE,\ldots)
plsWeights(object)
## S3 method for class 'sempls':
plsWeights(object)
## S3 method for class 'plsWeights':
print(x, na.print=".", digits=2, abbreviate=FALSE, \ldots)
plsLoadings(object)
## S3 method for class 'sempls':
plsLoadings(object)
## S3 method for class 'plsLoadings':
print(x, type=c("discriminant", "outer", "cross"),
cutoff=NULL, reldiff=0.2, na.print=".", digits=2, abbreviate=FALSE, ...)
plsm
as returned from
plsm
or read.splsm
.data.frame
containing the observed variables
(MVs). The storage mode for all the MVs included in the model must
be numeric
.numeric
value, which determines the maximum number of
iterations performed by the PLS algorithm. The default is $20$
iterations.numeric
value, specifying the tolerance for the maximum relative
differences in the outer weights. The default value is
$10^{-7}$.logical
value indicating, whether the observed
data shall be scaled to zero mean and unit variance. The default is
TRUE
.logical
value indicating, whether the outer
weights foreach latent variable (LV) shall be standardized to sum up
to one. The default is FALSE
. Since the factor scores are
scaled in each step of the PLS algorithm, chcharacter
naming the weighting scheme to
use. Possible values are:
"A"
or"centroid"
for the centroid scheme, the default,"B"
or"factorial"
for the factorial scheme andlogical
value indicating, whether
correlations shall be calculated pairwise. If the observed data
does not contain missing values, the results are not affected.
The default is FALSE
. For more details the R help,
character
naming the method to calculate
the correlations. Possible values are:
"pearson"
, the default,"kendall"
,"spearman"
."relative"
, the default,"square"
.FALSE
no status messages are printed.sempls
.what="loadings"
, type
describes the
loadings to be extracted -- those are:
"discriminant"
, the default, contrasts outer against cross
loadings to check for discrimant validity oftype="discriminant"
. It is
a numeric
value, specifying the relative difference between
outer and cross loadings at which cross loadings will still be
printed.character
substituting values not to be printed.print.default
."fscores"
: the factor scores are used,"prediction"
: the estimated factor scores are used,"residuals"
: the residuals are used.abbreviate
. The default is FALSE
.sempls
returns an object of class sempls
, with the following elements:data.frame
containing the estimates for
all the arcs in the path model, those are the outer loadings for
mode matrix
of path coefficients.matrix
of outer loadings.matrix
of cross loadings.matrix
of total effects.matrix
of inner weights.matrix
of outer weights.data.frame
containing the estimated factor scores for the
LVs.data.frame
containing the preprocessed obseravtions of the
MVs.call
.plsm
, read.splsm
,
rSquared
, pathDiagram
,
bootsempls
, plsm2sem
,
sem
data(ECSImobi)
ecsi <- sempls(model=ECSImobi, data=mobi, wscheme="pathWeighting")
ecsi
## create plots
densityplot(ecsi)
densityplot(ecsi, use="prediction")
densityplot(ecsi, use="residuals")
## Values of 'sempls' objects
names(ecsi)
ecsi$outer_weights
ecsi$outer_loadings
ecsi$path_coefficients
ecsi$total_effects
### using convenience methods to sempls results
## path coefficients
pathCoeff(ecsi)
## total effects
totalEffects(ecsi)
## get loadings and check for discriminant validity
(l <- plsLoadings(ecsi))
# outer loadings
print(l, type="outer", digits=2)
# outer loadings greater than 0.5
print(l,type="outer", cutoff=0.5, digits=2)
# cross loadings greater than 0.5
print(ecsi, type="cross", cutoff=0.5, digits=2)
### R-squared
rSquared(ecsi)
### Create .dot representation of the path diagram and
### create .pdf file if graphviz is available.
pathDiagram(ecsi, file="ecsiPLS1", edge.labels="both",
output.type="graphics", digits=3, graphics.fmt = "pdf")
# include R-squared values
pathDiagram(ecsi, file="ecsiPLS2", edge.labels="both",
output.type="graphics", digits=3, graphics.fmt = "pdf",
rSquared=rSquared(ecsi))
# only the structural model
pathDiagram(ecsi, file="ecsiPLS3", edge.labels="both",
output.type="graphics", digits=3, graphics.fmt = "pdf",
rSquared=rSquared(ecsi), full=FALSE)
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