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.
- Pieter Schoonees [aut, cre]
- Date of publication
- 2016-01-05 14:29:39
- Pieter Schoonees <firstname.lastname@example.org>
- GPL (>= 2)
- Augment with Boundaries Between Rating Scale Categories and...
- Low Rank Approximation LL' of a Square Symmetrix Matrix R
- Calculate the Weights for Bubble Plots
- Constrained Dual Scaling for Successive Categories with...
- Constrained Dual Scaling for Successive Categories
- Grouped Simulation with Response Styles
- S3 Methods for Integration into 'clue' Framework
- Impute Optimal Scores for Rating Categories
- Create a cdsdata Object
- Create Indicator Matrix
- Create a response style
- Simulate Data for a Single Response Style
- Generate a Copula
- Generate PCA data and Calculates Correlation Matrices
- Alternating Least Squares with Groups for Constrained Dual...
- Constrained Dual Scaling for a Single Random G Start
- Create an Indicator Matrix
- Quadratic monotone spline basis function for given knots.
- Calculate Constrained Dual Scaling Loss
- Calculate Loss for G Update
- Orthogonal Procrustes Analysis
- Plot cds Objects
- Plot a 'cdslist' Object
- Print cds Object
- Print dsdata Objects
- Randomly Generate Low-Rank Correlation Matrix
- Construct a Structured Covariance Matrix for Simulations
- sensory Data
- Auxiliary Information for 'sensory' Data
- Simulate Data with a Specific Principal Components Structure...
- Truncated Normal Quantiles
- Truncated Normal Sampling
- Update the Grouping Matrix
Files in this package