Two-dimensional visualisation of factor analyses

This function uses the diamondPlot to visualise the results of a factor analyses. Because the factor loadings computed in factor analysis are point estimates, they may vary from sample to sample. The factor loadings for any given sample are usually not relevant; samples are but means to study populations, and so, researchers are usually interested in population values for the factor loadings. However, tables with lots of loadings can quickly become confusing and intimidating. This function aims to facilitate working with and interpreting factor analysis based on confidence intervals by visualising the factor loadings and their confidence intervals.

factorLoadingDiamondCIplot(fa,
                           xlab="Factor Loading",
                           colors =
                             viridis_pal()(max(2, fa$factors)),
                           labels=NULL,
                           theme=theme_bw(),
                           ...)

Arguments

fa	The object produced by the fa function from the psych package. It is important that the n.iter argument of fa was set to a realistic number, because otherwise, no confidence intervals will be available.
xlab	The label for the x axis.
colors	The colors used for the factors. The default uses the discrete viridis palette, which is optimized for perceptual uniformity, maintaining its properties when printed in grayscale, and designed for colourblind readers. A vector can also be supplied; the colors must be valid arguments to colorRamp (and therefore, to col2rgb).
labels	The labels to use for the items (on the Y axis).
theme	The ggplot2 theme to use.
…	Additional arguments will be passed to ggDiamondLayer. This can be used to set, for example, the transparency (alpha value) of the diamonds to a lower value using e.g. alpha=.5.

Value

A ggplot plot with several ggDiamondLayers is returned.

See also

fa, meansDiamondPlot, meanSDtoDiamondPlot, diamondPlot, ggDiamondLayer

Examples

# NOT RUN {
### (Not run during testing because it takes too long and
###  may generate warnings because of the bootstrapping of
###  the confidence intervals)

factorLoadingDiamondCIplot(fa(Bechtoldt,
                              nfactors=2,
                              n.iter=50,
                              n.obs=200));

### And using a lower alpha value for the diamonds to
### make them more transparent

factorLoadingDiamondCIplot(fa(Bechtoldt,
                              nfactors=2,
                              n.iter=50,
                              n.obs=200),
                           alpha=.5,
                           size=1);
# }