When using lmer, one can compare two models using anova:
mod.ld.frq = lmer(ln_rt_offset ~ 1 + lfrq +
(1 + lfrq | PID) + (1| Item),
data=ldfw.std)
mod.ld.frq.dur = lmer(ln_rt_offset ~ 1 + lfrq + duration +
(1 + lfrq | PID) + (1| Item),
data=ldfw.std)
anova(mod.ld.frq, mod.ld.frq.dur)
What is the equivalent for jglmm, e.g.:
jmod.ld.frq = jglmm(ln_rt_offset ~ 1 + lfrq +
(1 + lfrq | PID) + (1| Item),
data=ldfw.std)
jmod.ld.frq.dur = jglmm(ln_rt_offset ~ 1 + lfrq + duration +
(1 + lfrq | PID) + (1| Item),
data=ldfw.std)
If I try using anova, I get an error like this:
anova(jmod.ld.frq, jmod.ld.frq.dur.phon)
Error in UseMethod("anova") :
no applicable method for 'anova' applied to an object of class "jglmm"
Thank you for any advice!
This doesn't seem to exist, but I wrote a crude anova function based on the existing jglmm::extractAIC.jglmm method, which has to extract the same information (df, log-likelihood) from the model.
library(JuliaCall)
my_anova <- function(m1, m2) {
julia_assign("model1", m1$model)
julia_assign("model2", m2$model)
df1 <- julia_eval("dof(model1);")
df2 <- julia_eval("dof(model2);")
loglik1 <- julia_eval("loglikelihood(model1);")
loglik2 <- julia_eval("loglikelihood(model2);")
ddf <- df1 - df2
ddev <- 2*(loglik1 - loglik2)
c(ddev, ddf, pchisq(ddev, ddf, lower.tail = FALSE))
}
jglmm results:
m1 <- jglmm(Reaction ~ Days + (Days|Subject),lme4::sleepstudy)
m2 <- jglmm(Reaction ~ 1 + (Days|Subject),lme4::sleepstudy)
my_anova(m1, m2)
## [1] 2.353654e+01 1.000000e+00 1.225640e-06
Compare with lme4:
library(lme4)
m1B <- lmer(Reaction ~ Days + (Days|Subject),lme4::sleepstudy)
m2B <- lmer(Reaction ~ 1 + (Days|Subject),lme4::sleepstudy)
anova(m1B, m2B)
Results:
refitting model(s) with ML (instead of REML)
Data: lme4::sleepstudy
Models:
m2B: Reaction ~ 1 + (Days | Subject)
m1B: Reaction ~ Days + (Days | Subject)
npar AIC BIC logLik deviance Chisq Df Pr(>Chisq)
m2B 5 1785.5 1801.4 -887.74 1775.5
m1B 6 1763.9 1783.1 -875.97 1751.9 23.537 1 1.226e-06 ***
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The lme4 results are much prettier, but the key components (change in deviance, change in df, p-value) are the same.
On second thought, we could have done this more easily by calling the logLik accessor method for the fit (the returned information includes both the log-likelihood and the df) and putting that into the my_anova() function ...