ncda: Nonparametric Clustered Data Analysis

In npclust: Nonparametric Tests for Incomplete Clustered Data

Nonparametric Clustered Data Analysis

Description

Main function to calculate nonparametric effect sizes and conduct hypothesis tests.

Usage

ncda(formula,data,period,subject,indicator=NULL,Contrast=NULL)

Arguments

formula	An object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under ‘Details’.
data	a data frame in the long format.
period	time indicator variable.
subject	subject or cluster ID
indicator	an optional vector of characters indicating the order of pre and post intervention period; must match the levels of period argument if specified; if not specified, the pre and post intervention period will be ordered in the alphabet order by default
Contrast	an optional contrast matrix for effect sizes.

Details

The model has the form response ~ tx where response is the (numeric) response variable and tx is the treatment variable.

Value

An object with effect sizes and other test details.

References

Cui, Yue, Frank Konietschke, and Solomon W. Harrar. "The nonparametric Behrens–Fisher problem in partially complete clustered data." Biometrical Journal 63.1 (2021): 148-167.

Harrar, Solomon W., and Yue Cui. "Nonparametric methods for clustered data in pre-post intervention design." Journal of Statistical Planning and Inference 222 (2023): 1-21.

Examples

ARTIS_analysis <- ncda(symptoms_pqol~tx, ARTIS, intervention, homeid,
                        indicator=c("0","1"),
                        Contrast=matrix(c(1,-1,1,-1,1,-1), nrow = 1))
names(ARTIS_analysis)
ARTIS_analysis$p.vector

skin_analysis <- ncda(score~tx, skin, intervention, subject,
                      indicator=c("control","treatment"),
                      Contrast=matrix(c(1,-1), nrow = 1))
skin_analysis$TotalSampleSize
skin_analysis$p.vector

npclust documentation built on April 3, 2025, 8:18 p.m.