subsample_lm

Deprecated. Averaging OLS coefficients over subsamples or folds cannot
improve upon the full-sample (weighted) least squares fit, which is already
the minimum-variance unbiased estimator for a fixed set of terms
(Gauss-Markov); it merely adds Monte-Carlo noise and small-sample bias. The
relevant source of variance is model *selection*, which is addressed by
consistency screening and by BIC-weighted model averaging
(<code>bestModel(..., averaging = TRUE)</code> or <code>weightedAverageModel</code>).
This function now simply returns the full-sample least squares fit.

deprecated

Generates 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 as well supports conventional norming. It generates
norm tables including confidence intervals. Methods for addressing
representativeness issues are available 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

subsample_lm function

<dl><dt>text</dt>
<dd>A character string or formula specifying the model to be fitted</dd>
<dt>data</dt>
<dd>A data frame containing the variables in the model</dd>
<dt>weights</dt>
<dd>Optional numeric vector of weights</dd>
<dt>k</dt>
<dd>Ignored (kept for backwards compatibility)</dd></dl>

Arguments

Deprecated: K-fold Resampled Coefficient Estimation — subsample_lm

<dl>

<dt>text</dt>
<dd>A character string or formula specifying the model to be fitted</dd>


<dt>data</dt>
<dd>A data frame containing the variables in the model</dd>


<dt>weights</dt>
<dd>Optional numeric vector of weights</dd>


<dt>k</dt>
<dd>Ignored (kept for backwards compatibility)</dd>

</dl>

subsample_lm: Deprecated: K-fold Resampled Coefficient Estimation

Description

Usage

Value

Arguments

See Also