[R-sig-ME] Question regarding updates or changes to lme4 package

[Allen, Frederick]

Hello,

I hope this email finds you well! I have a question with regard to the lme4 package (which I was told to redirect to this address). What, if any updates have been made to the package in the past six months to one year? Were any changes made to the package (or, if you know of any, to the imported packages) that would affect how p-values are calculated? (I looked at lme4 news but didn't find anything that I thought would explain my issue below.)

The reason I ask is this: about 9 months ago, I constructed linear effects models (plural, because we do this over a thousand times to analyze individual entities for -omics data) with and without a continuous independent (fixed) variable and with and without a categorical independent (fixed) variable to determine their contribution to a model for the dependent variable. At the time, we extracted coefficients from the model, as well as calculated p-values for contribution of each variable to the model.

When I repeat this analysis today with the same code and the same data, the coefficients and p-values for the categorical variable are exactly the same to the sixth decimal point (with occasional differences beyond the sixth decimal point). However, for the continuous variable, the coefficients are exactly the same, but the p-values are entirely different.

Of note, our workflow to extract p-values is as follows:

models.with <- dlply(data, "groups", function(df) lmer(Independent ~ categorical + continous + (1|random), data=df, REML = FALSE))
models.without <- dlply(data, "groups", function(df) lmer(Independent ~ categorical + (1|random), data=df, REML = FALSE))
object1 <- mapply(x=model.without, y=model.with, function(x,y) anova(x,y))

I would send an example with data, but I have no way of reproducing the old p-values I got.

Thank you for your help!
-Fred


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