pairwiseOrdinalPairedMatrix: Pairwise two-sample ordinal regression for blocked data with matrix output

Description

Performs pairwise two-sample ordinal regression across groups for paired or blocked data.

Usage

pairwiseOrdinalPairedMatrix(x, g, b, method = "fdr", ...)

Arguments

The response variable as a vector.

The grouping variable as a vector.

The blocking variable as a vector.

method

The p-value adjustment method to use for multiple tests. See p.adjust.

...

Additional arguments passed to clmm.

Value

A list consisting of: A matrix of p-values; The p-value adjustment method; A matrix of adjusted p-values.

Details

Ordinal regression is analogous to general linear regression or generalized linear regression for cases where the dependent variable is an ordinal variable. The ordinal package provides a flexible and powerful implementation of ordinal regression. The pairwiseOrdinalPairedTest function can be used as a post-hoc method following an omnibus ordinal regession whose form is analogous to a one-way analysis of variance with random blocks. The matrix output can be converted to a compact letter display. The blocking variable is treated as a random variable. The x variable must be an ordered factor.

References

http://rcompanion.org/handbook/G_08.html

Examples

Run this code

data(BobBelcher)
BobBelcher$Likert.f = factor(BobBelcher$Likert, ordered = TRUE)
BobBelcher = BobBelcher[order(factor(BobBelcher$Instructor, 
                        levels=c("Linda Belcher", "Louise Belcher",
                                 "Tina Belcher", "Bob Belcher",
                                 "Gene Belcher"))),]               
PT = pairwiseOrdinalPairedMatrix(x      = BobBelcher$Likert.f,
                                 g      = BobBelcher$Instructor,
                                 b      = BobBelcher$Rater,
                                 threshold="equidistant",
                                 method = "fdr")$Adjusted
PT
library(multcompView)
multcompLetters(PT,
                compare="<",
                threshold=0.05,
                Letters=letters)

Run the code above in your browser using DataLab

Data Engineering and BI courses are free this week!