[R] Random intercept model with time-dependent covariates, results different from SAS

From: Keith Wong <keithw_at_med.usyd.edu.au>
Date: Sun 04 Jul 2004 - 17:21:36 EST

Dear list-members

I am new to R and a statistics beginner. I really like the ease with which I can extract and manipulate data in R, and would like to use it primarily. I've been learning by checking analyses that have already been run in SAS.

In an experiment with Y being a response variable, and group a 2-level between-subject factor, and time a 5-level within-subject factor. 2 time-dependent covariates are also measured (continuous variables W and Z). The subject id variable (ID) is unique to each subject, and is not duplicated across groups.

I tried to fit a random intercept model in R (after setting options(contrasts = c(factor = “contr.SAS”, ordered = “contr.poly”)) as recommended on this list), and making the time, group and id variables factors:

> g2 = lme(Y ~ time + group + time:group + W + Z, random = ~ 1 | id, data =
datamod)

> anova(g2)

            numDF denDF  F-value p-value
(Intercept)     1    42  5.54545  0.0233
time            4    42 16.41069  <.0001
group           1    11  0.83186  0.3813
W               1    42  0.07555  0.7848
Z               1    42 45.23577  <.0001
time:group      4    42  3.04313  0.0273


I compared the results using SAS proc mixed: proc mixed data = datamod;
class id time group;
model Y = time group time*group W Z /s;
random int / sub = id;
run;

And get the following anova table for the fixed effects:

                              Type 3 Tests of Fixed Effects

                                    Num     Den
                     Effect          DF      DF    F Value    Pr > F

                     time             4      42       2.55    0.0534
                     group            1      42       0.54    0.4664
                     time*group       4      42       3.04    0.0273
                     W                1      42       8.80    0.0050
                     Z                1      42      32.52    <.0001

I am perplexed to see that the test for the main effect “time” is quite different. Both models seem to be specified equivalently to me, am I doing something wrong – particularly with the inclusion of the time-dependent covariates W and Z? I have looked at the data in both programmes and they are the same. There are no missing observations.

I a simpler model without the time-dependent covariates, and in this case the results are similar:
[R]
> g1 = lme(Y ~ time + group + time:group, random = ~ 1 | id, data = datamod)
> anova(g1)

            numDF denDF   F-value p-value
(Intercept)     1    44  3.387117  0.0725
time            4    44 10.620547  <.0001
group           1    11  0.508092  0.4908
time:group      4    44  3.961726  0.0079


[SAS]
proc mixed data = datamod;
class id time group;
model Y = time group time*group /s;
random int / sub = id;
run;

                              Type 3 Tests of Fixed Effects

                                    Num     Den
                     Effect          DF      DF    F Value    Pr > F

                     time             4      44       7.75    <.0001
                     group            1      44       0.51    0.4797
                     time*group       4      44       3.96    0.0079

Secondly, is there no R equivalent of the “LSMEANS” statement in SAS? Is there a work-around?

I would very much appreciate assistance.

Thanks. Keith



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https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Sun Jul 04 17:28:32 2004

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