Lfun.G.upd

Loss function used for updating G. This is not equivalent to the original loss function, as 
only a part of the total loss depends on G.

multivariate

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

Constrained Dual Scaling for Detecting Response Styles

Lfun.G.upd function

<dl><dt>G</dt>
<dd>The current value for G.</dd>
<dt>a.cur</dt>
<dd>The current value for a.</dd>
<dt>bwts2</dt>
<dd>The current value of the squared b weights.</dd>
<dt>Fr.bk</dt>
<dd>Current product between Fr.cent and bk.</dd>
<dt>n</dt>
<dd>Number of respondents.</dd>
<dt>m</dt>
<dd>Number of items.</dd>
<dt>q</dt>
<dd>Number for rating scale categories so that the rating scale is <code>1:q</code>.</dd>
<dt>K</dt>
<dd>Number of response style groups.</dd></dl>

Arguments

Calculate Loss for G Update — Lfun.G.upd

<dl>

<dt>G</dt>
<dd>The current value for G.</dd>


<dt>a.cur</dt>
<dd>The current value for a.</dd>


<dt>bwts2</dt>
<dd>The current value of the squared b weights.</dd>


<dt>Fr.bk</dt>
<dd>Current product between Fr.cent and bk.</dd>


<dt>n</dt>
<dd>Number of respondents.</dd>


<dt>m</dt>
<dd>Number of items.</dd>


<dt>q</dt>
<dd>Number for rating scale categories so that the rating scale is <code>1:q</code>.</dd>


<dt>K</dt>
<dd>Number of response style groups.</dd>

</dl>

Lfun.G.upd: Calculate Loss for G Update

Description

Usage

Arguments