R equivalent to random residual by subject in SAS - r

Question

I can code this problem in SAS with residual as a random effect ( I believe this is a r-side random intercept by fish)
proc glimmix data=one method=mmpl ;
class fish;
model increment =age growth_year age*growth_year;
random residual / subject=fish ;
run;
Here is the same analysis with AR(1) covariance structure.
proc glimmix data=one method=mmpl ;
class fish;
model increment =age growth_year age*growth_year;
random residual/ subject=fish type = ar(1) ;
run;
Here is my attempt in R to reproduce the first model that doesn't work.
model = lmer(increment ~ age + growth_year+ age*growth_year
+ (resid()|fish), data = SR_data)
Please Help, Use of lmer or glmer(gamma instead of normal distribution) or lme or any other package that I am unaware of.

Answer 1

The lme4 package doesn't allow R-side models, but nlme does. If you want correlation within fish without random effects of fish (i.e. R-side effects only, without any G-side effects), then I think you want to use gls: here's an example using the Orthodont data from the nlme package:
library("nlme")
gls(distance~age*Sex, correlation=corAR1(form=~1|Subject), data=Orthodont)
If you want to allow variation in the baseline value/intercept by group (both G- and R-side), then you'd use:
lme(distance~age*Sex, random = ~1|Subject,
correlation=corAR1(form=~1|Subject), data=Orthodont)
If you want variation in the baseline but not correlated residuals within subject (G-side only):
lme(distance~age*Sex, random=~1|Subject, data=Orthodont)
or
library(lme4)
lmer(distance~age*Sex + (1|Subject), data=Orthodont)