log_likelihood

This function computes the negative log-likelihood for a beta binomial regression model
where both the mean and standard deviation are modeled as functions of predictors.

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 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.

Wolfgang Lenhard

cNORM

Continuous Norming

Alexandra Lenhard

Sebastian Gary

WPS publisher 

log_likelihood function

<dl><dt>params</dt>
<dd>A numeric vector containing all model parameters. The first n_beta elements
are coefficients for the mean model, and the remaining elements are
coefficients for the log-standard deviation model.</dd>
<dt>X</dt>
<dd>A matrix of predictors for the mean model.</dd>
<dt>Z</dt>
<dd>A matrix of predictors for the log-standard deviation model.</dd>
<dt>y</dt>
<dd>A numeric vector of response values.</dd>
<dt>weights</dt>
<dd>A numeric vector of weights for each observation.</dd></dl>

Arguments

Calculate the negative log-likelihood for a beta binomial regression model — log_likelihood

<dl>

<dt>params</dt>
<dd>A numeric vector containing all model parameters. The first n_beta elements
are coefficients for the mean model, and the remaining elements are
coefficients for the log-standard deviation model.</dd>


<dt>X</dt>
<dd>A matrix of predictors for the mean model.</dd>


<dt>Z</dt>
<dd>A matrix of predictors for the log-standard deviation model.</dd>


<dt>y</dt>
<dd>A numeric vector of response values.</dd>


<dt>weights</dt>
<dd>A numeric vector of weights for each observation.</dd>

</dl>

log_likelihood: Calculate the negative log-likelihood for a beta binomial regression model

Description

Usage

Value

Arguments