# Getting P value with mixed effect with lme4 package [duplicate]

I have problem with getting p value from my mixed model, library(lme4)

DWR<-lmer(DWRm2~Growth.stage+Se.application+Growth.stage:Se.application+(1|Block),data=Sub1)
summary(DWR)


before I used this model and I got p value with summary my model but now I can't get it, I have just t value

## marked as duplicate by Tim♦, gung♦, Michael Chernick, mdewey, MomoMay 11 '17 at 15:29

• library(lme4); help(pvalues) gives some discussion. – conjugateprior Oct 9 '14 at 11:21
• @Smilig In spite of the similar titles the content seems to be completely different, since the other question seems to be focused on memisc not lmer; further, the answer there would be useless for this Q. – Glen_b Oct 9 '14 at 12:06

I'm pasting the information from help("pvalues",package="lme4") here.

Users who need p-values have a variety of options. In the list below, the methods marked MC provide explicit model comparisons; CI denotes confidence intervals; and P denotes parameter-level or sequential tests of all effects in a model. The starred (*) suggestions provide finite-size corrections (important when the number of groups is <50); those marked (+) support GLMMs as well as LMMs.

• likelihood ratio tests via anova (MC,+)
• profile confidence intervals via profile.merMod and confint.merMod (CI,+)
• parametric bootstrap confidence intervals and model comparisons via bootMer (or PBmodcomp in the pbkrtest package) (MC/CI,*,+)
• for random effects, simulation tests via the RLRsim package (MC,*)
• for fixed effects, F tests via Kenward-Roger approximation using KRmodcomp from the pbkrtest package (MC)
• car::Anova and lmerTest::anova provide wrappers for pbkrtest: lmerTest::anova also provides t tests via the Satterthwaite approximation (P,*)
• afex::mixed is another wrapper for pbkrtest and anova providing "Type 3" tests of all effects (P,*,+)
• arm::sim, or bootMer, can be used to compute confidence intervals on predictions.

When all else fails, don't forget to keep p-values in perspective.

p-values in lme4 are deliberately not listed by default, see:

There are some "approximations" but better just forget about p-values in lmm (or generally forget about them because they "measure" mostly the sample size).

• Could you briefly summarize the points made? Links can die, which would render an otherwise valuable answer almost useless. – Glen_b Oct 9 '14 at 10:39
• (I do realize that might be quite a difficult task, but it would make your answer substantially more useful, particularly if both links were lost. The links should of course remain; the extensve discussion is highly valuable.) – Glen_b Oct 9 '14 at 11:32