mixed effects model output

Let's say we have this:

 model2 <- lmer(milk.amount~(1|cow), data=milk, REML=FALSE)
model1 <- lmer(milk.amount~(1|cow), data=milk)
summary(model2)

Linear mixed model fit by maximum likelihood  ['lmerMod']
Formula: milk.amount ~ (1 | cow)
Data: milk
AIC      BIC   logLik deviance df.resid
186.5    191.6    -90.2    180.5       37

Scaled residuals:
Min      1Q  Median      3Q     Max
-2.0244 -0.4104  0.1795  0.6621  1.3879

Random effects:
Groups   Name        Variance Std.Dev.
cow      (Intercept) 6.755    2.599
Residual             2.999    1.732
Number of obs: 40, groups: cow, 10

Fixed effects:
Estimate Std. Error t value
(Intercept)  27.0150     0.8663   31.18


then

summary(model1)
Linear mixed model fit by REML ['lmerMod']

Formula: milk.amount ~ (1 | cow)
Data: milk
REML criterion at convergence: 178.9

Scaled residuals:
Min      1Q  Median      3Q     Max
-1.9981 -0.4136  0.1775  0.6561  1.4021

Random effects:
Groups   Name        Variance Std.Dev.
cow      (Intercept) 7.589    2.755
Residual             3.000    1.732
Number of obs: 40, groups: cow, 10

Fixed effects:
Estimate Std. Error t value
(Intercept)  27.0150     0.9132   29.58


Why model1 (with REML) doesn't show AIC, BIC, logLik, deviance coefficients? Is it possibly due to some kind of software dependency?

• No software dependency issue. It's defined like that in getLlikAIC, which gets called by summary.merMod. I'd assume there are (theoretical) issues with computing these for a REML fit. (Although AIC(model1) works just fine.) Commented Aug 4, 2014 at 14:19