Package: cds 1.0.3

cds: Constrained Dual Scaling for Detecting Response Styles

This is an implementation of constrained dual scaling for detecting response styles in categorical data, including utility functions. The procedure involves adding additional columns to the data matrix representing the boundaries between the rating categories. The resulting matrix is then doubled and analyzed by dual scaling. One-dimensional solutions are sought which provide optimal scores for the rating categories. These optimal scores are constrained to follow monotone quadratic splines. Clusters are introduced within which the response styles can vary. The type of response style present in a cluster can be diagnosed from the optimal scores for said cluster, and this can be used to construct an imputed version of the data set which adjusts for response styles.

Authors:Pieter Schoonees [aut, cre]

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cds.pdf |cds.html
cds/json (API)

# Install 'cds' in R:
install.packages('cds', repos = c('https://schoonees.r-universe.dev', 'https://cloud.r-project.org'))

Peer review:

Datasets:

On CRAN:

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

2.65 score 1 packages 37 scripts 302 downloads 4 mentions 29 exports 17 dependencies

Last updated 9 years agofrom:4641f96422. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKOct 25 2024
R-4.5-winNOTEOct 25 2024
R-4.5-linuxNOTEOct 25 2024
R-4.4-winOKOct 25 2024
R-4.4-macOKOct 25 2024
R-4.3-winOKOct 25 2024
R-4.3-macOKOct 25 2024

Exports:addboundsapproxloadscalc.wt.bubblescdscds.simcl_class_ids.cdscl_class_ids.cdsdataclean.scalescreate.indcreate.rscreatecdsdatadatsimG.startgen.copgenPCAgroup.ALSindmatis.cl_hard_partition.cdsis.cl_hard_partition.cdsdatais.cl_partition.cdsis.cl_partition.cdsdataisplineorthprocrrcormatrcovmatsimpcatrQnormtrRnormupdateG

Dependencies:ADGofTestclueclustercolorspacecopulagsllatticelimSolvelpSolveMASSMatrixmvtnormnumDerivpcaPPpsplinequadprogstabledist

Readme and manuals

Help Manual

Help pageTopics
Constrained Dual Scaling for Successive Categoriescds-package
Augment with Boundaries Between Rating Scale Categories and Rankaddbounds
Low Rank Approximation LL' of a Square Symmetrix Matrix Rapproxloads
Calculate the Weights for Bubble Plotscalc.wt.bubbles
Constrained Dual Scaling for Successive Categories with Groupscds
Grouped Simulation with Response Stylescds.sim
S3 Methods for Integration into 'clue' Frameworkcl_class_ids.cds cl_class_ids.cdsdata is.cl_hard_partition.cds is.cl_hard_partition.cdsdata is.cl_partition.cds is.cl_partition.cdsdata
Impute Optimal Scores for Rating Categoriesclean.scales clean.scales.cds clean.scales.cdslist
Create Indicator Matrixcreate.ind
Create a response stylecreate.rs
Create a cdsdata Objectcreatecdsdata
Simulate Data for a Single Response Styledatsim
Constrained Dual Scaling for a Single Random G StartG.start
Generate a Copulagen.cop
Generate PCA data and Calculates Correlation MatricesgenPCA
Alternating Least Squares with Groups for Constrained Dual Scalinggroup.ALS
Create an Indicator Matrixindmat
Quadratic monotone spline basis function for given knots.ispline
Calculate Constrained Dual Scaling LossLfun
Calculate Loss for G UpdateLfun.G.upd
Orthogonal Procrustes Analysisorthprocr
Plot cds Objectsplot.cds
Plot a 'cdslist' Objectplot.cdslist
Print cds Objectprint.cds
Print dsdata Objectsprint.cdsdata
Randomly Generate Low-Rank Correlation Matrixrcormat
Construct a Structured Covariance Matrix for Simulationsrcovmat
sensory Datasensory
Auxiliary Information for 'sensory' Datasensory.aux
Simulate Data with a Specific Principal Components Structure and Response Style Contaminationsimpca
Truncated Normal QuantilestrQnorm
Truncated Normal SamplingtrRnorm
Update the Grouping MatrixupdateG