0th

Percentile

##### 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.

Keywords
hplot
##### Usage
factorLoadingDiamondCIplot(fa, xlab="Factor Loading", colors = brewer.pal(max(3, fa\$factors), "Set1"), ...)
##### 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 'Set1' palette from colorbrewer using the RColorBrewer package. A vector can also be supplied; the colors must be valid arguments to colorRamp (and therefore, to col2rgb).
...
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.

fa, meansDiamondPlot, meanSDtoDiamondPlot, diamondPlot, ggDiamondLayer

##### Examples
## Not run:
# ### Not run because it takes too long and may generate
# ### warnings because of the bootstrapping of the confidence
# ### intervals
#                               n.iter=100, n.obs=100));
#
# ### And using a lower alpha value for the diamonds to
# ### make them more transparent