The demo codes for the Quant_13B_problem data and the SDS data have been removed. They are extensive analyses rather than the single topic examples that demos need to be. But the examples in the .Rd files have been extended, revised and supplied with more comments so that they can serve the demo process.
Likewise the vignette for the Quant_13B_problem data has been renewed in order to speed up the checking and installing processes. These vignette RMarkdown codes will be provided instead in gitHub and possibly CRAN along with other sample analyses in RMarkdown format.
We made the code more readable and interpretable by changing object names derived from mathematics to names that describe what the purpose of the object is. For example, "theta" was changed to "index" since these values are score index values, and "arclength" was changed to "infoSurp". Likewise, the many objects names beginning with capital "W" all pertain to surprisal values, matrices, lists and other structures. For example, a functional data basis object is names "Sbasis", a functional data curve is "Sfd" and a functional fdPar object to "SfdPar".
Extensive work on the test vignette Quant_13B_problem.Rmd and on the clinical rating scale vignette hads_dep_3.Rmd (not included in vignette folder, but in inst/HADS) led to detecting multiple problems with the plotting codes using ggplot2 and plotly packages. The argument lists in ICC.plot.R and Wpca.plot.R have been changed. Hopefully these are now safe to use.
TestGardener has evolved substantially over 2023 and since the last news item. There are now 38 functions, only 9 of which have not been altered over this period. The major changes have been in the graphical displays because we have switched to ggplot2 and plotly to construct them. Juan Li has become an expert in these two packages, and demonstrated a real talent for statistical graphics. We transported all functions having to do with surprisal curves in the fda package to versions in TestGardener. Unfortunately, we have beenb reduced to havingonly only one vignette, Quant_13B_problem.Rmd, to CRAN restrictions on time taken to install TestGardener. We have two large papers under review, one for testing data, and the other for rating scales.
We observe that the function used to define the best estimate of the score index for an examinee about 10 to 15 percent of the time has large nearly flat intervals of low values. Moreover, it is often the case that there are multiple minimizing locations, and function thetafun does not always identify the lowest of these. New function thetasearch uses a fast grid search to identify the lowest point, and replaces the location identified by thetafun where required by the integer closest to the global minimum. This is usually done after the cycles are completed as a final step.
The test analyst no longer needs to construct a "grbg"" object in order handle missing of illegitimate responses by treating them as an additional option, the garbage option as we call it. This is now done automatically within function make.dataList. Function make.dataList no longer needs an argument "grbg"." In order to makethis work, the user does need to adhere to a discipline. For each item, the labels or indices of its options must be the first positive integers. For the quantitative SweSAT test, for example, 68 of the items have option indices 1,...,4, and 12 have indices 1,...,5. For this large test given to over 55,000 examinees, the garbage option is essential for all items. It is assigned within make.dataList to an option with the index one larger than required for the legitimate options, so that after make_dataList has done its work, the 68 items have indices 1,...,5 and the others indices 1,...,6. Any integer within the data matrix U that exceeds the highest legitimate index is re-assigned to the corresponding garbage index. Do be aware that this process can change the U matrix that you set up initially. Users often want to have non-integer labels for options. This is still available by setting these up in the "optList" list vector, and these will appear in the most of the plots.
This version of TestGardener also has various code improvements that speed up computation and improve the information in plots.
Plots using the score index as the abscissa now use arc length along along the test information curve as the abscissa, since this interval is invariant under warpings of the index and therefore on a ratio scale. The codes in the demo and vignette folders have been modified accordingly.