[R-sig-ME] adjusted values

[Ben Bolker]

  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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