predictCoefficients_shash

Predict parameters for a shash regression model

internal

A comprehensive toolkit for generating continuous test norms in
psychometrics and biometrics, and analyzing model fit. The package offers
both distribution-free modeling using Taylor polynomials and parametric
modeling using the beta-binomial and the 'Sinh-Arcsinh' distribution.
Originally developed for achievement tests, it is applicable to a wide
range of mental, physical, or other test scores dependent on continuous or
discrete explanatory variables. The package provides several advantages:
It minimizes deviations from representativeness in subsamples, interpolates
between discrete levels of explanatory variables, and significantly reduces
the required sample size compared to conventional norming per age group.
cNORM enables graphical and analytical evaluation of model fit,
accommodates a wide range of scales including those with negative and
descending values, and even supports conventional norming. It generates
norm tables including confidence intervals. It also includes methods for
addressing representativeness issues through Iterative Proportional Fitting.
Based on Lenhard et al. (2016)
<doi:10.1177/1073191116656437>, Lenhard et al. (2019)
<doi:10.1371/journal.pone.0222279>, Lenhard and Lenhard (2021)
<doi:10.1177/0013164420928457> and Gary et al. (2023)
<doi:10.1007/s00181-023-02456-0>.

Wolfgang Lenhard

cNORM

Continuous Norming

Alexandra Lenhard

Sebastian Gary

WPS Publisher

predictCoefficients_shash function

<dl><dt>model</dt>
<dd>An object of class "cnormShash"</dd>
<dt>ages</dt>
<dd>A numeric vector of age points for prediction</dd></dl>

Arguments

Predict parameters for a shash regression model — predictCoefficients_shash

<dl>

<dt>model</dt>
<dd>An object of class "cnormShash"</dd>


<dt>ages</dt>
<dd>A numeric vector of age points for prediction</dd>

</dl>

predictCoefficients_shash: Predict parameters for a shash regression model

Description

Usage

Value

Arguments