[R] adjusted values

[Cristiano Alessandro]

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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