fa, irt.fa, VSS, ICLUST, omega, factor.pa, or principal.## S3 method for class 'psych':
plot(x,labels=NULL,...)
## S3 method for class 'irt':
plot(x,xlab,ylab,main,D,type=c("ICC","IIC","test"),cut=.3,labels,keys=NULL,...)
## S3 method for class 'poly':
plot(x,D,xlab,ylab,ylim,main,type=c("ICC","IIC","test"),cut=.3,labels,keys=NULL,...)irt.fa, there are three options: type = "IIC" (default) will plot the item characteristic respone function. type = "IIC" will plot the item information function, and type= "test" will plot the test information function.These are calls to the generic plot function that are intercepted for objects of type "psych". More precise plotting control is available in the separate plot functions. plot may be used for psych objects returned from fa, irt.fa, ICLUST, omega, as well as principal
A "jiggle" parameter is available in the factor.plot function (called from plot.psych when the type is a factor or cluster. If jiggle=TRUE, then the points are jittered slightly (controlled by amount) before plotting. This option is useful when plotting items with identical factor loadings (e.g., when comparing hypothetical models).
Objects from irt.fa are plotted according to "type" (Item informations, item characteristics, or test information). In addition, plots for selected items may be done if using the keys matrix. Plots of irt information return three invisible objects, a summary of information for each item at levels of the trait, the average area under the curve (the average information) for each item as well as where the item is most informative.
It is also possible to create irt like plots based upon just a scoring key and item difficulties, or from a factor analysis and item difficulties. These are not true IRT type analyses, in that the parameters are not estimated from the data, but are rather indications of item location and discrimination for arbitrary sets of items. To do this, find irt.stats.like and then plot the results.
VSS.plot and factor.plot, cluster.plot, fa, irt.fa, VSS, ICLUST, omega, factor.pa, or principaltest.data <- Harman74.cor$cov
f4 <- fa(test.data,4)
plot(f4)
#not run
#data(bfi)
#e.irt <- irt.fa(bfi[11:15]) #just the extraversion items
#plot(e.irt) #the information curves
#
#ic <- iclust(test.data,3) #shows hierarchical structure
#plot(ic) #plots loadings
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