hettmansperger_norton_test {pseudorank} | R Documentation |
Hettmansperger-Norton Trend Test for k-Samples
Description
This function calculates the Hettmansperger-Norton trend test using pseudo-ranks under the null hypothesis H0F: F_1 = ... F_k.
Usage
hettmansperger_norton_test(x, ...)
## S3 method for class 'numeric'
hettmansperger_norton_test(
x,
y,
na.rm = FALSE,
alternative = c("decreasing", "increasing", "custom"),
trend = NULL,
pseudoranks = TRUE,
...
)
## S3 method for class 'formula'
hettmansperger_norton_test(
formula,
data,
na.rm = FALSE,
alternative = c("decreasing", "increasing", "custom"),
trend = NULL,
pseudoranks = TRUE,
...
)
Arguments
x |
vector containing the observations |
... |
further arguments are ignored |
y |
vector specifiying the group to which the observations from the x vector belong to |
na.rm |
a logical value indicating if NA values should be removed |
alternative |
either decreasing (trend k, k-1, ..., 1) or increasing (1, 2, ..., k) or custom (then argument trend must be specified) |
trend |
custom numeric vector indicating the trend for the custom alternative, only used if alternative = "custom" |
pseudoranks |
logical value indicating if pseudo-ranks or ranks should be used |
formula |
formula object |
data |
data.frame containing the variables in the formula (observations and group) |
Value
Returns an object.
References
Brunner, E., Bathke, A.C., and Konietschke, F. (2018a). Rank- and Pseudo-Rank Procedures for Independent Observations in Factorial Designs - Using R and SAS. Springer Series in Statistics, Springer, Heidelberg. ISBN: 978-3-030-02912-8.
Happ M, Zimmermann G, Brunner E, Bathke AC (2020). Pseudo-Ranks: How to Calculate Them Efficiently in R. Journal of Statistical Software, Code Snippets, *95*(1), 1-22. doi: 10.18637/jss.v095.c01 (URL:https://doi.org/10.18637/jss.v095.c01).
Hettmansperger, T. P., & Norton, R. M. (1987). Tests for patterned alternatives in k-sample problems. Journal of the American Statistical Association, 82(397), 292-299
Examples
# create some data, please note that the group factor needs to be ordered
df <- data.frame(data = c(rnorm(40, 3, 1), rnorm(40, 2, 1), rnorm(20, 1, 1)),
group = c(rep(1,40),rep(2,40),rep(3,20)))
df$group <- factor(df$group, ordered = TRUE)
# you can either test for a decreasing, increasing or custom trend
hettmansperger_norton_test(df$data, df$group, alternative="decreasing")
hettmansperger_norton_test(df$data, df$group, alternative="increasing")
hettmansperger_norton_test(df$data, df$group, alternative="custom", trend = c(1, 3, 2))