semplsGOF: Quality Indices and Goodness of fit measures for pls path...

Description Usage Arguments Value References See Also Examples

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

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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 dth , 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.

See Also

sempls, plsLoadings

Examples

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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)

Example output

Loading required package: lattice
All 250 observations are valid.
Converged after 6 iterations.
Tolerance: 1e-07
Scheme: centroid
             R-squared
Image                .
Expectation       0.25
Quality           0.31
Value             0.34
Satisfaction      0.68
Complaints        0.28
Loyalty           0.46
             Q-Squared
Image                .
Expectation       0.11
Quality           0.17
Value             0.28
Satisfaction      0.46
Complaints        0.26
Loyalty           0.21
             Dillon-Goldstein's rho reflective MVs
Image                          0.82              5
Expectation                    0.73              3
Quality                        0.90              7
Value                          0.92              2
Satisfaction                   0.87              3
Complaints                        .              1
Loyalty                        0.72              3
             communality reflective MVs
Image               0.48              5
Expectation         0.48              3
Quality             0.58              7
Value               0.85              2
Satisfaction        0.69              3
Complaints             .              1
Loyalty             0.52              3

	Average communality: 0.57 
             redundancy
Image                 .
Expectation        0.12
Quality            0.18
Value              0.29
Satisfaction       0.47
Complaints            .
Loyalty            0.24

	Average redundancy: 0.26 
             R-squared R-squared-corrected predecessors
Image                .                   .            0
Expectation       0.25                0.25            1
Quality           0.31                0.31            1
Value             0.34                0.34            2
Satisfaction      0.68                0.68            4
Complaints        0.28                0.27            1
Loyalty           0.46                0.45            3

	Average R-squared: 0.39 
                    Value
Average R-squared    0.39
Average Communality  0.57
GoF                  0.47
      Image Expectation Quality Value Satisfaction Complaints Loyalty
IMAG1  0.74           .       .     .            .          .       .
IMAG2  0.60           .       .     .            .          .       .
IMAG3  0.58           .       .     .            .          .       .
IMAG4  0.77           .       .     .            .          .       .
IMAG5  0.74           .       .     .            .          .       .
CUEX1     .        0.77       .     .            .          .       .
CUEX2     .        0.69       .     .            .          .       .
CUEX3     .        0.61       .     .            .          .       .
PERQ1     .           .    0.80     .         0.68          .       .
PERQ2     .           .    0.64     .            .          .       .
PERQ3  0.63           .    0.78     .         0.64          .       .
PERQ4     .           .    0.77     .            .          .       .
PERQ5  0.61           .    0.76     .            .          .       .
PERQ6     .           .    0.78     .            .          .       .
PERQ7     .           .    0.78     .         0.70          .       .
PERV1     .           .       .  0.90            .          .       .
PERV2     .           .       .  0.94            .          .       .
CUSA1     .           .    0.64     .         0.80          .       .
CUSA2     .           .       .     .         0.85          .       .
CUSA3     .           .       .     .         0.85          .       .
CUSCO     .           .       .     .            .       1.00       .
CUSL1     .           .       .     .            .          .    0.81
CUSL2     .           .       .     .            .          .       .
CUSL3     .           .       .     .            .          .    0.92

semPLS documentation built on May 2, 2019, 9:34 a.m.