# snk.test

0th

Percentile

##### Student-Newman-Keuls (SNK) procedure

This function perforns a SNK post-hoc test of means on the factors of a chosen term of the model, comparing among levels of one factor within each level of other factor or combination of factors.

Keywords
htest
##### Usage
snk.test(object, term, among = NULL, within = NULL)
##### Arguments
object
An object of class lm, containing the specified design.
term
Term of the model to be analysed. Use estimates to see the right form to inform it.
among
Specifies the factor which levels will be compared among. Need to be specified if the term to be analysed envolves more than one factor.
within
Specifies the factor or combination of factors that will be compared within level among.
##### Details

SNK is a stepwise procedure for hypothesis testing. First the sample means are sorted, then the pairwise studentized range (q) is calculated by dividing the differences between means by the standard error, which is based upon the average variance of the two sample.

##### Value

A list containing the standard error, the degree of freedom and pairwise comparisons among levels of one factor within each level of other(s) factor(s).

##### References

Underwood, A.J. 1997. Experiments in Ecology: Their Logical Design and Interpretation Using Analysis of Variance. Cambridge University Press, Cambridge.

gad, estimates

• snk.test
##### Examples
library(GAD)
data(rohlf95)
CG <- as.fixed(rohlf95$cages) MQ <- as.random(rohlf95$mosquito)
model <- lm(wing ~ CG + CG%in%MQ, data = rohlf95)
##Check estimates to see model structure
estimates(model)
snk.test(model,term = 'CG:MQ', among = 'MQ', within = 'CG')
##
##
##Example using snails dataset
data(snails)
O <- as.random(snails$origin) S <- as.random(snails$shore)
B <- as.random(snails$boulder) C <- as.random(snails$cage)
model <- lm(growth ~ O + S + O*S + B%in%S + O*(B%in%S) + C%in%(O*(B%in%S)),
data = snails)