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.
|Author||Pieter Schoonees [aut, cre]|
|Date of publication||2016-01-05 14:29:39|
|Maintainer||Pieter Schoonees <firstname.lastname@example.org>|
|License||GPL (>= 2)|
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