weighted.rank

Conducts weighted ranking on the basis of sums of weights per unique raw score.
Please provide a vector with raw values and an additional vector with the weight
of each observation. In case the weight vector is NULL, a normal ranking is done.
The vectors may not include NAs and the weights should be positive non-zero
values. With equal weights, the result reproduces the mid-ranks of base R's
<code><a href="/link/rank()?package=cNORM&version=3.6.1" data-mini-rdoc="cNORM::rank()">rank()</a></code> (<code>ties.method = "average"</code>).

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

weighted.rank function

<dl><dt>x</dt>
<dd>A numerical vector</dd>
<dt>weights</dt>
<dd>A numerical vector with weights; should have the same length as x</dd></dl>

Arguments

Conducts weighted ranking on the basis of sums of weights per unique raw score.
Please provide a vector with raw values and an additional vector with the weight
of each observation. In case the weight vector is NULL, a normal ranking is done.
The vectors may not include NAs and the weights should be positive non-zero
values. With equal weights, the result reproduces the mid-ranks of base R's
<code><a href='https://rdrr.io/r/base/rank.html'>rank()</a></code> (<code>ties.method = "average"</code>).

Weighted rank estimation — weighted.rank

<dl>

<dt>x</dt>
<dd>A numerical vector</dd>


<dt>weights</dt>
<dd>A numerical vector with weights; should have the same length as x</dd>

</dl>

weighted.rank: Weighted rank estimation

Description

Usage

Value

Arguments