Package: ccrs 0.1.0

Mariko Takagishi

ccrs: Correct and Cluster Response Style Biased Data

Functions for performing Correcting and Clustering response-style-biased preference data (CCRS). The main functions are correct.RS() for correcting for response styles, and ccrs() for simultaneously correcting and content-based clustering. The procedure begin with making rank-ordered boundary data from the given preference matrix using a function called create.ccrsdata(). Then in correct.RS(), the response style is corrected as follows: the rank-ordered boundary data are smoothed by I-spline functions, the given preference data are transformed by the smoothed functions. The resulting data matrix, which is considered as bias-corrected data, can be used for any data analysis methods. If one wants to cluster respondents based on their indicated preferences (content-based clustering), ccrs() can be applied to the given (response-style-biased) preference data, which simultaneously corrects for response styles and clusters respondents based on the contents. Also, the correction result can be checked by plot.crs() function.

Authors:Mariko Takagishi [aut, cre]

ccrs_0.1.0.tar.gz
ccrs_0.1.0.tar.gz(r-4.5-noble)ccrs_0.1.0.tar.gz(r-4.4-noble)
ccrs_0.1.0.tgz(r-4.4-emscripten)ccrs_0.1.0.tgz(r-4.3-emscripten)
ccrs.pdf |ccrs.html✨
ccrs/json (API)

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6 exports 0.00 score 59 dependencies 4 scripts 222 downloads

Last updated 6 years agofrom:3f94efe42a. Checks:OK: 1 NOTE: 1. Indexed: yes.

Exports:ccrsconvert.X2Fcorrect.rscreate.ccrsdatagenerate.rsdatatransformRSdata

Dependencies:ADGofTestcdscliclueclustercodetoolscolorspacecopuladoParalleldplyrexpmfansifarverforeachgenericsggplot2gluegridExtragslgtableisobanditeratorslabelinglatticelifecyclelimSolvelpSolvelsbclustmagrittrMASSMatrixmgcvmsmmunsellmvtnormnlmenumDerivpcaPPpillarpkgconfigplyrpsplinequadprogR6RColorBrewerRcppreshape2rlangscalesstablediststringistringrsurvivaltibbletidyselectutf8vctrsviridisLitewithr