| qgraph.loadings {qgraph} | R Documentation |
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 |
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(s)
Sacha Epskamp (mail@sachaepskamp.com)
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
Examples
## Not run:
# 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)
## End(Not run)