Autoregressive covariance structure for lme object and R/SAS

...using AR1 instead of Variance Components in SAS, which fits the

Dear R users,
We are working on a data set in which we have measured repeatedly a 
physiological response variable (y) every 20 min for 12 h (time 
variable; 'x') in subjects ('id') beloning to one of five groups 
('group'; 'A' to 'E'). Data are located at:
https://www.dropbox.com/s/hf455aev3teb5e0/data.csv?dl=0

We are interested to model if the response in y differences with time 
(i.e. 'x') for the two groups. Thus:
require(nlme)
m1<-lme(y~group*x+group*I(x^2),random=~x|id,data=data.df,na.action=na.
omit)

But because data are collected repeatedly over short time intervals 
for each subject, it seemed prudent to consider an autoregressive 
covariance structure. Thus:
m2<-update(m1,~.,corr=corCAR1(form=~x|id))

AIC values indicate the latter (i.e. m2) as more appropriate:
  anova(m1,m2)
#   Model df      AIC      BIC       logLik        Test  L.Ratio
p-value
#m1     1 19 2155.996 2260.767 -1058.9981
#m2     2 20 2021.944 2132.229  -990.9718 1 vs 2 136.0525  <.0001

Fixed effects and test statistics differ between models. A look at 
marginal ANOVA tables suggest inference might differ somewhat between
models:

anova.lme(m1,type="m")
#              numDF denDF  F-value p-value
#(Intercept)      1  1789 63384.80  <.0001
#group             4    45      1.29  0.2893
#x                   1  1789     0.05  0.8226
#I(x^2)            1  1789     4.02  0.0451
#group:x          4  1789     2.61  0.0341
#group:I(x^2)   4  1789     4.37  0.0016

anova.lme(m2,type="m")
#             numDF denDF  F-value p-value
#(Intercept)      1  1789 59395.79  <.0001
#group             4    45      1.33  0.2725
#x                    1  1789     0.04  0.8379
#I(x^2)            1  1789     2.28  0.1312
#group:x          4  1789     2.09  0.0802
#group:I(x^2)  4  1789     2.81  0.0244

Now, this is all well. But: my colleagues have been running the same 
data set using PROC MIXED in SAS and come up with substantially 
different results when comparing SAS default covariance structure 
(variance
components) and AR1. Specifically, there is virtually no change in 
either test statistics or fitted values when using AR1 instead of 
Variance Components in SAS, which fits the observation that AIC values 
(in SAS) indicate both covariance structures fit data equally well.

This is not very satisfactory to me, and I would be interesting to 
know what is happening here. Realizing this might not be the correct 
forum for this question, I would like to ask you all if anyone would 
have any input as to what is going on here, e.g. am I setting up my 
model erroneously, etc.?

N.b. I have no desire to replicate SAS results, but I would most 
certainly be interested to know what could possibly explain  such a 
large discrepancy between the two platforms. Any suggestions greatly

(Data are located at:
https://www.dropbox.com/s/hf455aev3teb5e0/data.csv?dl=0)

With all best wishes,
Andreas

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