In lm
and glm
models, I use functions coef
and confint
to achieve the goal:
m = lm(resp ~ 0 + var1 + var1:var2) # var1 categorical, var2 continuous
coef(m)
confint(m)
Now I added random effect to the model - used mixed effects models using lmer
function from lme4 package. But then, functions coef
and confint
do not work any more for me!
> mix1 = lmer(resp ~ 0 + var1 + var1:var2 + (1|var3))
# var1, var3 categorical, var2 continuous
> coef(mix1)
Error in coef(mix1) : unable to align random and fixed effects
> confint(mix1)
Error: $ operator not defined for this S4 class
I tried to google and use docs but with no result. Please point me in the right direction.
EDIT: I was also thinking whether this question fits more to https://stats.stackexchange.com/ but I consider it more technical than statistical, so I concluded it fits best here (SO)... what do you think?
I'm going to add a bit here. If m
is a fitted (g)lmer
model (most of these work for lme
too):
fixef(m)
is the canonical way to extract coefficients from mixed models (this convention began with nlme
and has carried over to lme4
)coef(summary(m))
; if you have loaded lmerTest
before fitting the model, or convert the model after fitting (and then loading lmerTest
) via coef(summary(as(m,"lmerModLmerTest")))
, then the coefficient table will include p-values. (The coefficient table is a matrix; you can extract the columns via e.g. ctab[,"Estimate"]
, ctab[,"Pr(>|t|)"]
, or convert the matrix to a data frame and use $
-indexing.)confint(m)
; these may be computationally intensive. If you use confint(m, method="Wald")
you'll get the standard +/- 1.96SE confidence intervals. (lme
uses intervals(m)
instead of confint()
.)If you prefer to use broom.mixed
:
tidy(m,effects="fixed")
gives you a table with estimates, standard errors, etc.tidy(as(m,"merModLmerTest"), effects="fixed")
(or fitting with lmerTest
in the first place) includes p-valuesconf.int=TRUE
gives (Wald) CIsconf.method="profile"
(along with conf.int=TRUE
) gives likelihood profile CIsYou can also get confidence intervals by parametric bootstrap (method="boot"
), which is considerably slower but more accurate in some circumstances.