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.

AuthorPieter Schoonees [aut, cre]
Date of publication2016-01-05 14:29:39
MaintainerPieter Schoonees <schoonees@gmail.com>
LicenseGPL (>= 2)
Version1.0.3

View on CRAN

Man pages

addbounds: Augment with Boundaries Between Rating Scale Categories and...

approxloads: Low Rank Approximation LL' of a Square Symmetrix Matrix R

calc.wt.bubbles: Calculate the Weights for Bubble Plots

cds: Constrained Dual Scaling for Successive Categories with...

cds-package: Constrained Dual Scaling for Successive Categories

cds.sim: Grouped Simulation with Response Styles

cdstoclue: S3 Methods for Integration into 'clue' Framework

clean.scales: Impute Optimal Scores for Rating Categories

createcdsdata: Create a cdsdata Object

create.ind: Create Indicator Matrix

create.rs: Create a response style

datsim: Simulate Data for a Single Response Style

gen.cop: Generate a Copula

genPCA: Generate PCA data and Calculates Correlation Matrices

group.ALS: Alternating Least Squares with Groups for Constrained Dual...

G.start: Constrained Dual Scaling for a Single Random G Start

indmat: Create an Indicator Matrix

ispline: Quadratic monotone spline basis function for given knots.

Lfun: Calculate Constrained Dual Scaling Loss

Lfun.G.upd: Calculate Loss for G Update

orthprocr: Orthogonal Procrustes Analysis

plot.cds: Plot cds Objects

plot.cdslist: Plot a 'cdslist' Object

print.cds: Print cds Object

print.cdsdata: Print dsdata Objects

rcormat: Randomly Generate Low-Rank Correlation Matrix

rcovmat: Construct a Structured Covariance Matrix for Simulations

sensory: sensory Data

sensory.aux: Auxiliary Information for 'sensory' Data

simpca: Simulate Data with a Specific Principal Components Structure...

trQnorm: Truncated Normal Quantiles

trRnorm: Truncated Normal Sampling

updateG: Update the Grouping Matrix

Questions? Problems? Suggestions? or email at ian@mutexlabs.com.

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