weighted.rank

A numerical vector with weights; should have the same length as x

weights

Type of estimator, can either be "Harrell-Davis" using a beta function to
approximate the weighted percentiles (Harrell &amp; Davis, 1982) or "Type7" (default; Hyndman &amp; Fan, 1996), an adaption
of the generic quantile function in R, including weighting. All code based on the work of Akinshin (2020).

type

Conducts weighted ranking on the basis of either the weighted Harrell-Davis quantile estimator
or an adaption of the type 7 quantile estimator of the generic quantile function in the base
package. 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.

Conventional methods for producing standard scores in psychometrics or biometrics
are often plagued with "jumps" or "gaps" (i.e., discontinuities) in norm tables and low
confidence for assessing extreme scores. The continuous norming method introduced by A.
Lenhard et al. (2016, <doi:10.1177/1073191116656437>; 2019, <doi:10.1371/journal.pone.0222279>) and generates continuous test norm
scores on the basis of the raw data from standardization samples, without requiring
assumptions about the distribution of the raw data: Norm scores are directly established
from raw data by modeling the latter ones as a function of both percentile scores and an
explanatory variable (e.g., age). The method minimizes bias arising from sampling and
measurement error, while handling marked deviations from normality, addressing bottom
or ceiling effects and capturing almost all of the variance in the original norm data
sample. An online demonstration is available via <https://cnorm.shinyapps.io/cNORM/>.

weighted.rank: Weighted rank estimation

Description

Usage

Arguments

Value

References