trim.ranef.test: Robust test for random factors using trimmed means.
Description
Provides a robust hypothesis test for the null: Var(X) = 0, for a population of random factor levels.
Usage
trim.ranef.test(Y, X, tr = 0.2)
Arguments
Y
Vector of response data. A quantitative vector.
X
Vector of factor levels
tr
Amount of trimming. A number from 0-0.5.
Value
Returns a list with three components dataframe describing numerator and denominator degrees of freedom, the F test statistic and the p-value.
Details
Robust analyses for random effect designs are particularly important since standard random effects models provide poor control over type I error when assumptions of normality and homoscedasticity are violated. Specifically, Wilcox (1994) showed that even with equal sample sizes, and moderately large samples, actual probability of type I error can exceed 0.3 if normality and homoscedasticity are violated.
References
Wilcox, R. R. (2005) Introduction to Robust Estimation and Hypothesis Testing, Second
Edition. Elsevier, Burlington, MA.
# NOT RUN {rye<-c(50,49.8,52.3,44.5,62.3,74.8,72.5,80.2,47.6,39.5,47.7,50.7)
nutrient<-factor(c(rep(1,4),rep(2,4),rep(3,4)))
trim.ranef.test(rye,nutrient,tr=.2)
# }