[R-sig-ME] adjusted values
cri.alessandro at gmail.com
Thu Mar 22 22:12:15 CET 2018
Thanks for the help, and sorry for the mix-up. Rune you are right, I am
using nlme. T
he summary of glht can do various kind of correction: the one I used to
produce the posted results is called "single-step", but I can also use
"Shaffer" and "Westfall" and other (including Bonferroni), if necessary.
The real question is whether, in this case, I should perform MC corrections
(i.e. post-hoc with glht), or I can directly take the p-values associated
the the pre-defined contrasts. I read somewhere that there are statistical
reasons for not trusting the latter. In fact, some packages for LMM do not
even provide such p-values.
Do you have suggestions here? And if I went for MC, what correction would
On Thu, Mar 22, 2018 at 3:16 PM, Rune Haubo <rune.haubo at gmail.com> wrote:
> Maybe we are confusing ourselves here. Christiano, you say that you
> are using lme4, but the output looks more like that from lme (nlme
> package). If the latter is the case, the lmerTest package is not
> directly related to your situation.
> Otherwise I agree with Ben that whether MC corrections are appropriate
> depends on the context. And about the coefficients: they are not
> adjusted or corrected.
> On 22 March 2018 at 19:08, Ben Bolker <bbolker at gmail.com> wrote:
> > 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
> >> "des_day" to the intercept, using the provided t-statistics.
> >> 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
> >> differ because the latter are adjusted with Bonferroni correction. I
> >> whether there is any form of correction in the coefficient estimated of
> >> LMM, and which p-values are more appropriate to use.
> >> Thanks
> >> Cristiano
> >> [[alternative HTML version deleted]]
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