qgraph (version 1.9.8)

qgraph.loadings: qgraph.loadings

Description

This function is a wrapper function for qgraph designed to visualize factor loadings.

Usage

qgraph.loadings( fact, ...)

Arguments

fact

A matrix containing factor loadings (items per row, factors per column) or an "loadings" object

...

Additional optional arguments passed to qgraph and special arguments used in this function (described below).

Additional optional arguments

layout

If "default" a standard layout for factor models will be made. If this is "circle" the default layout is circled (factors in the centre, items at the edge). No other layouts are currently supported.

vsize

A vector where the first value indicates the size of manifest variables and the second value indicates the size of latent variables.

model

"reflective" to have arrows go to manifest variables, "formative" to have arrows go to latent variables or "none" (default) for no arrows

crossloadings

Logical, if TRUE then for each manifest variable the strongest loading is omitted (default to FALSE).

groups

An optional list containing the measurement model, see qgraph

Fname

When there is only one factor, this is it's name. If there are more factors, the names in the groups list are used only if the factors can be identified.

resid

Values for the residuals

residSize

Size of the residuals, defaults to 0.1

factorCors

Correlation matrix of the factors

Author

Sacha Epskamp ([e-mail])

References

Sacha Epskamp, Angelique O. J. Cramer, Lourens J. Waldorp, Verena D. Schmittmann, Denny Borsboom (2012). qgraph: Network Visualizations of Relationships in Psychometric Data. Journal of Statistical Software, 48(4), 1-18. URL http://www.jstatsoft.org/v48/i04/.

See Also

qgraph

Examples

if (FALSE) {
# Load big5 dataset:
data(big5)
data(big5groups)

big5efa <- factanal(big5,factors=5,rotation="promax",scores="regression")
big5loadings <- loadings(big5efa)
qgraph.loadings(big5loadings,groups=big5groups,minimum=0.2,
				cut=0.4,vsize=c(1.5,15),borders=FALSE,vTrans=200,
				model = "reflective", resid = big5efa$uniquenesses)

# Tree layout:
qgraph.loadings(big5loadings,groups=big5groups,minimum=0.2,
				cut=0.4,vsize=c(1.5,15),borders=FALSE,vTrans=200,
				layout="tree",width=20,model = "reflective", 
				resid = big5efa$uniquenesses)
}