studySim
(Package: nFactors) :
Simulation Study from Given Factor Structure Matrices and Conditions
The structureSim function returns statistical results from simulations from predefined congeneric factor structures. The main ideas come from the methodology applied by Zwick and Velicer (1986).
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: studySim
●
0 images
|
Utility functions for structureSim class objects. Note that with the plot.structureSim a dotted black vertical line shows the median number of factors retained by all the different indices.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: boxplot.structureSim, is.structureSim, plot.structureSim, print.structureSim, summary.structureSim
●
0 images
|
structureSim
(Package: nFactors) :
Population or Simulated Sample Correlation Matrix from a Given Factor Structure Matrix
The structureSim function returns a population and a sample correlation matrices from a predefined congeneric factor structure.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: structureSim
●
0 images
|
rRecovery
(Package: nFactors) :
Test of Recovery of a Correlation or a Covariance matrix from a Factor Analysis Solution
The rRecovery function returns a verification of the quality of the recovery of the initial correlation or covariance matrix by the factor solution.
● Data Source:
CranContrib
● Keywords: utilities
● Alias: rRecovery
●
0 images
|
The principalComponents function returns a principal component analysis. Other R functions give the same results, but principalComponents is customized mainly for the other factor analysis functions available in the nfactors package. In order to retain only a small number of components the componentAxis function has to be used.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: principalComponents
●
0 images
|
The PrincipalAxis function returns a principal axis analysis without iterated communalities estimates. Three different choices of communalities estimates are given: maximum corelation, multiple correlation or estimates based on the sum of the squared principal component analysis loadings. Generally statistical packages initialize the the communalities at the multiple correlation value (usual inverse or generalized inverse). Unfortunately, this strategy cannot deal with singular correlation or covariance matrices. If a generalized inverse, the maximum correlation or the estimated communalities based on the sum of loading are used instead, then a solution can be computed.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: principalAxis
●
0 images
|
plotuScree
(Package: nFactors) :
Plot of the Usual Cattell's Scree Test
uScree plot a usual scree test of the eigenvalues of a correlation matrix.
● Data Source:
CranContrib
● Keywords: Graphics
● Alias: plotuScree
●
0 images
|
plotParallel
(Package: nFactors) :
Plot a Parallel Analysis Class Object
Plot a scree plot adding information about a parallel analysis.
● Data Source:
CranContrib
● Keywords: Graphics
● Alias: plotParallel
●
0 images
|
plotnScree
(Package: nFactors) :
Scree Plot According to a nScree Object Class
Plot a scree plot adding information about a non graphical nScree analysis.
● Data Source:
CranContrib
● Keywords: Graphics
● Alias: plotnScree
●
0 images
|
parallel
(Package: nFactors) :
Parallel Analysis of a Correlation or Covariance Matrix
This function gives the distribution of the eigenvalues of correlation or a covariance matrices of random uncorrelated standardized normal variables. The mean and a selected quantile of this distribution are returned.
● Data Source:
CranContrib
● Keywords: multivariate
● Alias: parallel
●
0 images
|
providing 
.