Thank you for people's help and replies.  I have decided that actually R is doing the sensible thing and I've been confused with SAS.  For completeness, let me include the solution in this e-mail.  The equivalent models are:

e2<-lme(y~a+b+c, random=~1|name/run, data=eg, na.action=na.omit)

proc mixed data=first;
class name run;
model y= a b c/s;
random name;
random run(name);
run;

This worked before.  The breakthrough for  the differences came in recognising that the SAS code was inducing a correlation structure between the runs with a random statement NOT the time points within the runs as with a repeated statement.  Hence the following 2 pieces of code are equivalent.

e2<-lme(y~a+b+c, random=~1|name/run, correlation=corAR1(form=~1|name/run),  data=first3, na.action=na.omit)

proc mixed data=first;
class name run;
model y= a b c/s;
random name;
random run(name);
repeated/subject=run(name) type=ar(1);
run;

But the code I was using below was not sensible:

proc mixed data=first;
class name run;
model y= a b c/s;
random name;
random int/subject=run(name) type=ar(1);
run;

Paul

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