factorLoadingDiamondCIplot
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 thepsych
package. It is important that then.iter
argument offa
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 tocolorRamp
(and therefore, tocol2rgb
).  …

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 ggDiamondLayer
s is returned.
See Also
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
# factorLoadingDiamondCIplot(fa(Thurstone.33, 2,
# n.iter=100, n.obs=100));
#
# ### And using a lower alpha value for the diamonds to
# ### make them more transparent
# factorLoadingDiamondCIplot(fa(Thurstone.33, 2,
# n.iter=100, n.obs=100),
# alpha = .5);
## 