I am converting a SAS PROC GENMOD example into R, using glm in R. The SAS code was:

proc genmod data=data0 namelen=30;
model boxcoxy=boxcoxxy ~ AGEGRP4 + AGEGRP5 + AGEGRP6 + AGEGRP7 + AGEGRP8 + RACE1 + RACE3 + WEEKEND + 
SEQ/dist=normal;
FREQ REPLICATE_VAR;  
run;

My R code is:

parmsg2 <- glm(boxcoxxy ~ AGEGRP4 + AGEGRP5 + AGEGRP6 + AGEGRP7 + AGEGRP8 + RACE1 + RACE3 + WEEKEND + 
SEQ , data=data0, family=gaussian, weights = REPLICATE_VAR)

When I use summary(parmsg2) I get the same coefficient estimates as in SAS, but my standard errors are wildly different.

The summary output from SAS is:

Name         df   Estimate      StdErr    LowerWaldCL  UpperWaldCL      ChiSq   ProbChiSq
Intercept    1   6.5007436    .00078884      6.4991975    6.5022897    67911982 0
agegrp4      1   .64607262    .00105425      .64400633    .64813891   375556.79 0
agegrp5      1    .4191395    .00089722      .41738099    .42089802   218233.76 0
agegrp6      1  -.22518765    .00083118     -.22681672   -.22355857   73401.113 0
agegrp7      1  -1.7445189    .00087569     -1.7462352   -1.7428026   3968762.2 0
agegrp8      1  -2.2908855    .00109766     -2.2930369   -2.2887342   4355849.4 0
race1        1  -.13454883    .00080672     -.13612997   -.13296769    27817.29 0
race3        1  -.20607036    .00070966     -.20746127   -.20467944   84319.131 0
weekend      1    .0327884    .00044731       .0319117    .03366511   5373.1931 0
seq2          1 -.47509583    .00047337     -.47602363   -.47416804   1007291.3 0
Scale         1 2.9328613     .00015586      2.9325559    2.9331668     -127

The summary output from R is:

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  6.50074    0.10354  62.785  < 2e-16 
AGEGRP4      0.64607    0.13838   4.669 3.07e-06 
AGEGRP5      0.41914    0.11776   3.559 0.000374 
AGEGRP6     -0.22519    0.10910  -2.064 0.039031  
AGEGRP7     -1.74452    0.11494 -15.178  < 2e-16
AGEGRP8     -2.29089    0.14407 -15.901  < 2e-16
RACE1       -0.13455    0.10589  -1.271 0.203865    
RACE3       -0.20607    0.09315  -2.212 0.026967 
WEEKEND      0.03279    0.05871   0.558 0.576535 
SEQ         -0.47510    0.06213  -7.646 2.25e-14

The importance of the difference in the standard errors is that the SAS coefficients are all statistically significant, but the RACE1 and WEEKEND coefficients in the R output are not. I have found a formula to calculate the Wald confidence intervals in R, but this is pointless given the difference in the standard errors, as I will not get the same results.

Apparently SAS uses a ridge-stabilized Newton-Raphson algorithm for its estimates, which are ML. The information I read about the glm function in R is that the results should be equivalent to ML. What can I do to change my estimation procedure in R so that I get the equivalent coefficents and standard error estimates that were produced in SAS?

To update, thanks to Spacedman's answer, I used weights because the data are from individuals in a dietary survey, and REPLICATE_VAR is a balanced repeated replication weight, that is an integer (and quite large, in the order of 1000s or 10000s). The website that describes the weight is here. I don't know why the FREQ rather than the WEIGHT command was used in SAS. I will now test by expanding the number of observations using REPLICATE_VAR and rerunning the analysis.