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?
There are two new packages, lmerTest and lsmeans, that can calculate 95% confidence limits for lmer and glmer output. Maybe you can look into those? And coefplot2, I think can do it too (though as Ben points out below, in a not so sophisticated way, from the standard errors on the Wald statistics, as opposed to Kenward-Roger and/or Satterthwaite df approximations used in lmerTest and lsmeans)... Just a shame that there are still no inbuilt plotting facilities in package lsmeans (as there are in package effects(), which btw also returns 95% confidence limits on lmer and glmer objects but does so by refitting a model without any of the random factors, which is evidently not correct).
I suggest that you use good old lme (in package nlme). It has confint, and if you need confint of contrasts, there is a series of choices (estimable in gmodels, contrast in contrasts, glht in multcomp).
Why p-values and confint are absent in lmer: see http://finzi.psych.upenn.edu/R/Rhelp02a/archive/76742.html .
Assuming a normal approximation for the fixed effects (which confint would also have done), we can obtain 95% confidence intervals by
estimate + 1.96*standard error.
The following does not apply to the variance components/random effects.
library("lme4")
mylm <- lmer(Reaction ~ Days + (Days|Subject), data =sleepstudy)
# standard error of coefficient
days_se <- sqrt(diag(vcov(mylm)))[2]
# estimated coefficient
days_coef <- fixef(mylm)[2]
upperCI <- days_coef + 1.96*days_se
lowerCI <- days_coef - 1.96*days_se
Not sure when it was added, but now confint() is implemented in lme4. For example the following example works:
library(lme4)
m = lmer(Reaction ~ Days + (Days | Subject), sleepstudy)
confint(m)
To find the coefficient, you can simply use the summary function of lme4
m = lm(resp ~ 0 + var1 + var1:var2) # var1 categorical, var2 continuous
m_summary <- summary(m)
to have all coefficients :
m_summary$coefficient
If you want the confidence interval, multiply the standart error by 1.96:
CI <- m_summary$coefficient[,"Std. Error"]*1.96
print(CI)
来源：https://stackoverflow.com/questions/11072544/how-to-get-coefficients-and-their-confidence-intervals-in-mixed-effects-models