# Plot the test information or scale curve in either two or three dimensions. A test or scale analysis produces a space curve that varies with in the space of possible option curves of dimension `Sdim`. 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. ```r Spca_plot(harmvarmxfd, nharm=2, titlestr=NULL) ``` ## Arguments - `harmvarmxfd`: Functional data objects for the principal components of the curve shape. - `nharm`: Number of principal components. - `titlestr`: A string for the title of the plot. Defaults to NULL. ## Returns Side effect is a two or three-dimensional plot of the principal component approximation of the information curve using the plotly package. Function `plot_ly` does not return a value, but does render the graphic. ## References Ramsay, J. O., Li J. and Wiberg, M. (2020) Full information optimal scoring. Journal of Educational and Behavioral Statistics, 45, 297-315. Ramsay, J. O., Li J. and Wiberg, M. (2020) Better rating scale scores with information-based psychometrics. Psych, 2, 347-360. ## Author(s) Juan Li and James Ramsay ## See Also `Spca` ## Examples ```r # Example 1. Display the test information curve for the # short SweSAT multiple choice test with 24 items and 1000 examinees # plot a two-dimension version of manifold curve SfdList <- Quant_13B_problem_parmList$SfdList index <- Quant_13B_problem_parmList$index arclength <- Quant_13B_problem_parmList$arclength Results <- Spca(SfdList, nharm=2, rotate=TRUE) varprop <- Results$varpropvarmx titlestr <- "SweSAT problem items" oldpar <- par(no.readonly=TRUE) on.exit(oldpar) Spca_plot(Results$harmvarmxfd, nharm=2, titlestr) print("Proportions of variance accounted for and their sum:") print(round(100*c(varprop,sum(varprop)),1)) # plot a three-dimension version of manifold curve SfdList <- Quant_13B_problem_parmList$SfdList index <- Quant_13B_problem_parmList$index arclength <- Quant_13B_problem_parmList$arclength Results <- Spca(SfdList, nharm=3, rotate=TRUE) varprop <- Results$varpropvarmx Spca_plot(Results$harmvarmxfd, nharm=3, titlestr) print("Proportions of variance accounted for and their sum:") print(round(100*c(varprop,sum(varprop)),1)) ```

Spca_plot function