ispline

Calculate basis functions for monotone quadratic splines.

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

ispline function

<dl><dt>xvec</dt>
<dd>Vector at which to evaluate the basis functions.</dd>
<dt>tvec</dt>
<dd>Vector of spline knots: lower endpoint, interior knot, upper endpoint.</dd>
<dt>intercept</dt>
<dd>Logical; should an intercept be included or not?</dd></dl>

Arguments

Quadratic monotone spline basis function for given knots. — ispline

<dl>

<dt>xvec</dt>
<dd>Vector at which to evaluate the basis functions.</dd>


<dt>tvec</dt>
<dd>Vector of spline knots: lower endpoint, interior knot, upper endpoint.</dd>


<dt>intercept</dt>
<dd>Logical; should an intercept be included or not?</dd>

</dl>

ispline: Quadratic monotone spline basis function for given knots.

Description

Usage

Arguments