sim.hierarchical {psych} | R Documentation

## Create a population or sample correlation matrix, perhaps with hierarchical structure.

### Description

Create a population orthogonal or hierarchical correlation matrix from a set of factor loadings and factor intercorrelations. Samples of size n may be then be drawn from this population. Return either the sample data, sample correlations, or population correlations. This is used to create sample data sets for instruction and demonstration.

### Usage

    sim.hierarchical(gload=NULL, fload=NULL, n = 0, raw = FALSE,mu = NULL)
    make.hierarchical(gload=NULL, fload=NULL, n = 0, raw = FALSE)  #deprecated

### Arguments

`gload` | Loadings of group factors on a general factor
`fload` | Loadings of items on the group factors
`n` | Number of subjects to generate: N=0 => population values
`raw` | raw=TRUE, report the raw data, raw=FALSE, report the sample correlation matrix.
`mu` | means for the individual variables

### Details

Many personality and cognitive tests have a hierarchical factor structure. For demonstration purposes, it is useful to be able to create such matrices, either with population values, or sample values.

Given a matrix of item factor loadings (fload) and of loadings of these factors on a general factor (gload), we create a population correlation matrix by using the general factor law (R = F' theta F where theta = g'g).

To create sample values, we use code adapted from the `mvrnorm` function in MASS.

The default is to return population correlation matrices. Sample correlation matrices are generated if n >0. Raw data are returned if raw = TRUE.

The default values for gload and fload create a data matrix discussed by Jensen and Weng, 1994.

Although written to create hierarchical structures, if the gload matrix is all 0, then a non-hierarchical structure will be generated.

### Value

a matrix of correlations or a data matrix

### Author(s)

William Revelle

### References

https://personality-project.org/r/r.omega.html
Jensen, A.R., Weng, L.J. (1994) What is a Good g? Intelligence, 18, 231-258.

### See Also

`omega`, `schmid`, `ICLUST`, `VSS` for ways of analyzing these data. Also see `sim.structure` to simulate a variety of structural models (e.g., multiple correlated factor models). The simulation uses the `mvrnorm` function from the MASS package.

### Examples

    gload <-  gload<-matrix(c(.9,.8,.7),nrow=3)    # a higher order factor matrix
    fload <-matrix(c(                    #a lower order (oblique) factor matrix
               .8,0,0,
               .7,0,.0,
               .6,0,.0,
                0,.7,.0,
                0,.6,.0,
                0,.5,0,
                0,0,.6,
                0,0,.5,
                0,0,.4),   ncol=3,byrow=TRUE)

    jensen <- sim.hierarchical(gload,fload)    #the test set used by omega
    round(jensen,2)

    #simulate a non-hierarchical structure
    fload <- matrix(c(c(c(.9,.8,.7,.6),rep(0,20)),c(c(.9,.8,.7,.6),rep(0,20)),
        c(c(.9,.8,.7,.6),rep(0,20)),c(c(c(.9,.8,.7,.6),rep(0,20)),c(.9,.8,.7,.6))),ncol=5)
    gload <- matrix(rep(0,5))
    five.factor <- sim.hierarchical(gload,fload,500,TRUE) #create sample data set
    #do it again with a hierachical structure
    gload <- matrix(rep(.7,5)  )
    five.factor.g <- sim.hierarchical(gload,fload,500,TRUE) #create sample data set
    #compare these two with omega
    #not run
    #om.5 <- omega(five.factor$observed,5)
    #om.5g <- omega(five.factor.g$observed,5)

[Package psych version 1.8.4 Index]