[R-sig-ME] adjusted values
Ben Bolker
bbolker at gmail.com
Thu Mar 22 19:08:38 CET 2018
summary() via lmerTest incorporates finite-size corrections, but not
multiple-comparisons corrections. glht does the opposite. In this case
your finite-size corrections are pretty much irrelevant though (in this
context 962 \approx infinity).
By convention, people don't usually bother with MC corrections when
they're testing pre-defined contrasts from a single model, but I don't
know that there's hard-and-fast rule (if I were testing the effects of a
large number of treatments within a single model I might indeed use MC;
I probably wouldn't bother for n=4).
I don't know exactly what kind of MC correction glht does, but it
probably shouldn't be Bonferroni (which is very conservative, and
ignores correlations among the tests).
On 18-03-22 01:28 PM, Cristiano Alessandro wrote:
> Hi all,
>
> I am fitting a linear mixed model with lme4 in R. The model has a single
> factor (des_days) with 4 levels (-1,1,14,48), and I am using random
> intercept and slopes.
>
> Fixed effects: data ~ des_days
> Value Std.Error DF t-value p-value
> (Intercept) 0.8274313 0.007937938 962 104.23757 0.0000
> des_days1 -0.0026322 0.007443294 962 -0.35363 0.7237
> des_days14 -0.0011319 0.006635512 962 -0.17058 0.8646
> des_days48 0.0112579 0.005452614 962 2.06469 0.0392
>
> I can clearly use the previous results to compare the estimations of each
> "des_day" to the intercept, using the provided t-statistics. Alternatively,
> I could use post-hoc tests (z-statistics):
>
>> ph_conditional <- c("des_days1 = 0",
> "des_days14 = 0",
> "des_days48 = 0");
>> lev.ph <- glht(lev.lm, linfct = ph_conditional);
>> summary(lev.ph)
>
> Simultaneous Tests for General Linear Hypotheses
>
> Fit: lme.formula(fixed = data ~ des_days, data = data_red_trf, random
> = ~des_days |
> ratID, method = "ML", na.action = na.omit, control = lCtr)
>
> Linear Hypotheses:
> Estimate Std. Error z value Pr(>|z|)
> des_days1 == 0 -0.002632 0.007428 -0.354 0.971
> des_days14 == 0 -0.001132 0.006622 -0.171 0.996
> des_days48 == 0 0.011258 0.005441 2.069 0.101
> (Adjusted p values reported -- single-step method)
>
>
> The p-values of the coefficient estimates and those of the post-hoc tests
> differ because the latter are adjusted with Bonferroni correction. I wonder
> whether there is any form of correction in the coefficient estimated of the
> LMM, and which p-values are more appropriate to use.
>
> Thanks
> Cristiano
>
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>
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