Assessing whether sigma for a random effects parameter is equal to 0
On 10/13/20 4:56 PM, Sammie Haskin wrote:
Hello! Given the pistonrings data set from the qcc package in R, I produced the following code to assess whether the standard deviation of random effect of the model was equal to 0. Here is my code: library(lme4) library(qcc) library(RLRsim) library(nlme) library(data.table) fit.pistons <- lmer(formula=diameter ~ sample + (1 | sample), data = pistonrings,REML=T) fit.pistons0 <- lm(diameter ~ sample, data = pistonrings) exactLRT(fit.pistons,fit.pistons0) Here is the output: LRT = 8.1423, p-value = 0.0014 Is this result implying that the standard deviation for the random effect is significant such that we reject the null hypothesis
Yes, if we're using a standard alpha-level cutoff of 0.05 (or 0.01). and that H0: sigma = 0 is false? For what it's worth I would argue that the null hypothesis is (almost??) *always* false, whatever the results of null-hypothesis testing are.
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