JT.test {SAGx}R Documentation

Jonckheere-Terpstra trend test

Description

The test is testing for a monotone trend in terms of the class parameter. The number of times that an individual of a higher class has a higher gene expression forms a basis for the inference.

Usage

trendA <- JT.test(data, class, labs = c("NS", "HS", "COPD0", "COPD1", "COPD2"), alternative = c("increasing", "decreasing", "two-sided"))

Arguments

data A matrix with genes in rows and subjects in columns
class the column labels, if not an ordered fctor it will be redefined to be one.
labs the labels of the categories coded by class

Details

Assumes that groups are given in increasing order, if the class variable is not an ordered factor, it will be redefined to be one. The p-value is calculated through a normal approximation.

The implementation owes to suggestions posted to R list.

The definition of predictive strength appears in Flandre and O'Quigley.

Value

an object of class JT-test, which extends the class htest, and includes the following slots

statistic the observed JT statistic
parameter the null hypothesis parameter, if other value than 0.
p.value the p-value for the two-sided test of no trend.
method Jonckheere-Terpstra
alternative The relations between the levels: decreasing, increasing or two-sided
data.name the name of the input data
median1 ... mediann the medians for the n groups
trend the rank correlation with category
S1 Predictive strength

Author(s)

Per Broberg, acknowledging input from Christopher Andrews at SUNY Buffalo

References

Lehmann, EH (1975) Nonparametrics: Statistical Methods Based on Ranks p. 233. Holden Day
Flandre, Philippe and O'Quigley, John, Predictive strength of Jonckheere's test for trend: an application to genotypic scores in HIV infection, Statistics in Medicine, 2007, 26, 24, 4441-4454

Examples

# Enter the data as a vector
A <- as.matrix(c(99,114,116,127,146,111, 125,143,148,157,133,139, 149, 160, 184))
# create the class labels
g <- c(rep(1,5),rep(2,5),rep(3,5))
# The groups have the medians
tapply(A, g, median)
# JT.test indicates that this trend is significant at the 5
JT.test(data = A, class = g, labs = c("GRP 1", "GRP 2", "GRP 3"), alternative = "two-sided")

[Package SAGx version 1.16.0 Index]