semPLS: semplsGOF – R documentation

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semplsGOF

Quality Indices and Goodness of fit measures for pls path models

Description

A collection of method to validate the goodness of the model. Since there is no well identified global optimization criterion each part of the model needs to be validated.

Usage

rSquared(object, ...)
## S3 method for class 'sempls'
rSquared(object, na.rm=FALSE, ...)
## S3 method for class 'rSquared'
print(x, na.print=".", digits=2, ...)

qSquared(object, ...)
## S3 method for class 'sempls'
qSquared(object, d=NULL, impfun, dlines=TRUE,
         total=FALSE, ...)
## S3 method for class 'qSquared'
print(x, na.print=".", digits=2, ...)

dgrho(object, ...)
## S3 method for class 'sempls'
dgrho(object, ...)
## S3 method for class 'dgrho'
print(x, na.print=".", digits=2, ...)

communality(object, ...)
## S3 method for class 'sempls'
communality(object, ...)
## S3 method for class 'communality'
print(x, na.print=".", digits=2, ...)

redundancy(object, ...)
## S3 method for class 'sempls'
redundancy(object, ...)
## S3 method for class 'redundancy'
print(x, na.print=".", digits=2, ...)

rSquared2(object, ...)
## S3 method for class 'sempls'
rSquared2(object, na.rm=FALSE, ...)
## S3 method for class 'rSquared2'
print(x, na.print=".", digits=2, ...)

gof(object, ...)
## S3 method for class 'sempls'
gof(object, ...)
## S3 method for class 'gof'
print(x, na.print=".", digits=2, ...)

Arguments

`object`	An object of class `sempls`.
`d`	A `numeric` value for the omission distance. Thus choosing d=N, where N is the number of complete observations, is leaving one out cross validation. This is done when `d` takes its default value `NULL`.
`impfun`	An user specified function to impute missing values.
`dlines`	If `TRUE` the same observations are deleted for a whole block of MVs, else each `d`th , counting from top left to bottom right, observation is deleted.
`total`	If `total=TRUE` total effects are used instead of path coefficients to calculate the predictions.
`na.rm`	If `na.rm=TRUE` observation with missing values are discarded before analysis.
`x`	An object of the according class.
`na.print`	A `character` substituting values not to be printed.
`digits`	minimal number of _significant_ digits, see `print.default`.

...

Arguments to be passed down.

Value

Most GOF methods return a column vector with the names of the variables as rows and the respective measure as column.

References

Esposito Vinzi V., Trinchera L., Amato S. (2010). PLS Path Modeling: From Foundations to Recent Developments and Open Issues for Model Assessment and Improvement. In Esposito Vinzi V., Chin W.W., Henseler J., Wang H.F. (eds.), Handbook of Partial Least Squares: Concepts, Methods and Applications in Marketing and Related Fields, chapter 2. Springer-Verlag Berlin Heidelberg.

Examples

data(ECSImobi)
ecsi <- sempls(model=ECSImobi, data=mobi, E="C")

### R-squared
rSquared(ecsi)

### Q-squared with omission distance d=4
qSquared(ecsi, d=4)

### Dillon-Goldstein's rho (aka composite reliability)
dgrho(ecsi)

### Communalities
communality(ecsi)

### Redundancy
redundancy(ecsi)

### R-squared (normal + corrected)
rSquared2(ecsi)

### Goodness of fit
gof(ecsi)

### check for discriminant validity using loadings
l <-plsLoadings(ecsi)
print(l, type="discriminant", cutoff=0.5, reldiff=0.2)

semPLS

Structural Equation Modeling Using Partial Least Squares

v1.0-10

GPL-2

Authors

Armin Monecke <armin.monecke@stat.uni-muenchen.de>

Initial release

2013-01-21