rcompanion (version 1.13.2)

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(formula = NULL, data = NULL, x = NULL,
  g = NULL, b = NULL, method = "fdr", ...)

Arguments

formula

A formula indicating the measurement variable and the grouping variable. e.g. y ~ group | block.

data

The data frame to use.

x

The response variable as a vector.

g

The grouping variable as a vector.

b

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

The input should include either formula and data; or x, g, and b.

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

See Also

pairwiseOrdinalPairedTest

Examples

# NOT RUN {
data(BobBelcher)
BobBelcher$Likert.f = factor(BobBelcher$Likert, ordered = TRUE)
BobBelcher$Instructor = factor( BobBelcher$Instructor, 
                  levels = c("Linda Belcher", "Louise Belcher",
                             "Tina Belcher", "Bob Belcher",
                             "Gene Belcher")) 
PT = pairwiseOrdinalPairedMatrix(Likert.f ~ Instructor | Rater,
                                 data      = BobBelcher,
                                 threshold ="equidistant",
                                 method    = "fdr")$Adjusted
PT
library(multcompView)
multcompLetters(PT,
                compare="<",
                threshold=0.05,
                Letters=letters)
                 
# }