Wpca.plot  R Documentation 
A test or scale analysis produces a space curve that varies with in the space of
possible option curves of dimension Wdim
. Fortunately, it is usual that most
of the shape variation in the curve is within only two or three dimensions, and these
can be fixed by using functional principal components analysis.
Wpca.plot(arclength, WfdList, Wdim, nharm=2, rotate=TRUE, dodge = 1.003, titlestr = NULL)
arclength 
The total length of the test information or scale curve as computed by function

WfdList 
A numbered list object produced by a TestGardener analysis of a test. Its length
is equal to the number of items in the test or questions in the scale.
Each member of

Wdim 
The total number of options in the test or scale. 
nharm 
The number of principal components of the test information or scale curve to be used to display the curve. Must be either 2 or 3. 
rotate 
If true, rotate principal components of the test information or scale curve to be used to display the curve to VARIMAX orientation. 
dodge 
A constant greater than 1 required by ggplot2. Defaults to 1.003. 
titlestr 
A string for the title of the plot. Defaults to NULL. 
A named list with these members:
pcaplot 
If two dimensions or harmonics are specified, this is a

harmvarmxfd 
Functional data objects for the principal components of the curve shape. 
varpropvarmx 
Proportions of variance accounted for by the principal components 
The principal components are VARIMAX rotated by default. The plot is displayed as a side value even if no output object is specified in the call to the function.
Juan Li and James Ramsay
Ramsay, J. O., Li J. and Wiberg, M. (2020) Full information optimal scoring. Journal of Educational and Behavioral Statistics, 45, 297315.
Ramsay, J. O., Li J. and Wiberg, M. (2020) Better rating scale scores with informationbased psychometrics. Psych, 2, 347360.
http://testgardener.azurewebsites.net
# Example 1. Display the test information curve for the # short SweSAT multiple choice test with 24 items and 1000 examinees # plot a twodimension version of manifold curve WfdList < Quantshort_parList$WfdList theta < Quantshort_parList$theta arclength < Quantshort_parList$arclength Wpca.plotResults < Wpca.plot(arclength, WfdList, Quantshort_dataList$Wdim) varprop < Wpca.plotResults$varpropvarmx print("Proportions of variance accounted for and their sum:") print(round(c(varprop,sum(varprop)),3)) # plot a threedimension version of manifold curve WfdList < Quantshort_parList$WfdList theta < Quantshort_parList$theta arclength < Quantshort_parList$arclength Wpca.plotResults < Wpca.plot(arclength, WfdList, Quantshort_dataList$Wdim, nharm=3) varprop < Wpca.plotResults$varpropvarmx print("Proportions of variance accounted for and their sum:") print(round(c(varprop,sum(varprop)),3)) # Example 2. Display the test information curve for the # Symptom Distress Scale with 13 items and 473 respondents. # Proceed as above changing "Quant" to "SDS"
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