pseudorank (version 1.0.4)

kepner_robinson_test: Kepner-Robinson Test

Description

This function calculates the Kepner-Robinson test using ranks under the null hypothesis H0F: F_1 = ... F_k where F_i are the marginal distributions. Each subject needs to have k measurements. This test assumes that the covariance matrix of a subject has a compound symmetry structure.

Usage

kepner_robinson_test(x, ...)
# S3 method for numeric
kepner_robinson_test(
  x,
  time,
  subject,
  na.rm = FALSE,
  distribution = c("Chisq", "F"),
  ...
)
# S3 method for formula
kepner_robinson_test(
  formula,
  data,
  subject,
  na.rm = FALSE,
  distribution = c("Chisq", "F"),
  ...
)

Value

Returns an object of class 'pseudorank'

Arguments

x

numeric vector containing the data

...

further arguments are ignored

time

factor specifying the groups

subject

factor specifying the subjects or the name of the subject column if a data.frame is used

na.rm

a logical value indicating if NA values should be removed

distribution

either 'Chisq' or 'F' approximation

formula

optional formula object

data

optional data.frame of the data

References

James L. Kepner & David H. Robinson (1988) Nonparametric Methods for Detecting Treatment Effects in Repeated-Measures Designs, Journal of the American Statistical Association, 83:402, 456-461.

Examples

# create some artificial data with 20 subjects measured at two time points
data <- rnorm(40)
time <- rep(c(1,2),20)
subject <- gl(20,2)
df <- data.frame(data=data,time=time,subject=subject)

kepner_robinson_test(data,time,subject)
kepner_robinson_test(data~time,data=df,subject="subject")