owl {peramo}

One-Way Layout Permutation Test

Description

owl performs the global test and multiple comparisons for single factor experiments.

Usage

owl(df, rand = 9999, alpha.post = 0.05, type.post = "control", seed = 1)

Arguments

df

a data frame with the name of experimental groups as the first column and the measurement of responses as the remaining columns.

rand

an integer, the number of randomization samples. The default value is 9999.

alpha.post

a numeric, the Type I error rate for multiple comparisons. The default value is 0.05.

type.post

the way of multiple comparisons, "all" for pairwise comparisons or "control" for only comparisons with the control group.

seed

an integer, the seed for random number generation. Setting a seed ensures the reproducibility of the result. See set.seed for more details.

Details

The first name appearing in the first column will determine the control group. The other names will be treatment groups.

Value

owl returns a list with 9 components:

n.obs

the sample sizes.

avg.obs

the mean responses.

T.obs

the T statistic for global test.

pval

the p-value for global test.

pval.round

the reported form of p-value.

main.test

the strength of evidence against the null hypothesis.

d.multi.obs

the differences in means for multiple comparisons.

mad.cric

the critical value of maximum absolute differences in means.

post.test

TRUE if the differences are significant.

References

Ernst, M. D. (2004). Permutation Methods: A Basis for Exact Inference. Statistical Science, 19(4), 676–685. doi:10.1214/088342304000000396.

Muff, S., Nilsen, E. B., O’Hara, R. B., & Nater, C. R. (2022). Rewriting results sections in the language of evidence. Trends in Ecology & Evolution, 37(3), 203–210. doi:10.1016/j.tree.2021.10.009.

Examples

ernst2004 <- data.frame(
group = factor(rep(c("style1", "style2", "style3"), each = 5 ),
levels = c("style1", "style2", "style3")),
speed = c( 135,91,111,87, 122, 175,130,514,283, NA,105,147,159,107,194))
owl(ernst2004, type.post = "all")