trRnorm

Random numbers from truncated univariate normal.

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

trRnorm function

<dl><dt>n</dt>
<dd>The number of points to sample.</dd>
<dt>mu</dt>
<dd>The mean of the distribution.</dd>
<dt>sd</dt>
<dd>The standard deviation.</dd>
<dt>a</dt>
<dd>The lower truncation point.</dd>
<dt>b</dt>
<dd>The upper truncation point.</dd></dl>

Arguments

Truncated Normal Sampling — trRnorm

<dl>

<dt>n</dt>
<dd>The number of points to sample.</dd>


<dt>mu</dt>
<dd>The mean of the distribution.</dd>


<dt>sd</dt>
<dd>The standard deviation.</dd>


<dt>a</dt>
<dd>The lower truncation point.</dd>


<dt>b</dt>
<dd>The upper truncation point.</dd>

</dl>

trRnorm: Truncated Normal Sampling

Description

Usage

Arguments