Getting P value with mixed effect with lme4 package 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
 A: 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.
A: p-values in lme4 are deliberately not listed by default, see:


*

*Bates (author of lme4) on p-values in linear mixed models

*or here

*see also:
How to get an "overall" p-value and effect size for a categorical factor in a mixed model (lme4)?
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).
