[R-sig-ME] Trouble Replicating Unstructured Mixed Procedure in R

Thompson,Paul Paul.Thompson at SanfordHealth.org
Wed Jan 25 03:48:25 CET 2012


In the CS model, the F values for Gender and Gender*age are really close, but age is quite discrepant. That seems problematic.

-----Original Message-----
From: r-sig-mixed-models-bounces at r-project.org [mailto:r-sig-mixed-models-bounces at r-project.org] On Behalf Of Charles Determan Jr
Sent: Tuesday, January 24, 2012 8:32 PM
To: r-sig-mixed-models at r-project.org
Subject: [R-sig-ME] Trouble Replicating Unstructured Mixed Procedure in R

Greetings,

I have been working on R for some time now and I have begun the endeavor of
trying to replicate some SAS code in R.  I have scoured the forums but
haven't been able to find an answer.  I hope one of you could be so kind as
to enlighten me.

I am attempting to replicate a repeated measures experiment using some
standard data.  I have posted the SAS code and output directly from a
publication as well as my attempts in R to replicate it.  My main issue
comes with the 'unstructured' component.

The 'dental' dataset from 'mixedQF' package,
equivalent to formixed data in SAS

    distance age Subject    Sex
1       26.0   8     M01   Male
2       25.0  10     M01   Male
3       29.0  12     M01   Male
4       31.0  14     M01   Male
5       21.5   8     M02   Male
6       22.5  10     M02   Male
7       23.0  12     M02   Male
8       26.5  14     M02   Male
9       23.0   8     M03   Male
10      22.5  10     M03   Male
11      24.0  12     M03   Male
12      27.5  14     M03   Male
13      25.5   8     M04   Male
14      27.5  10     M04   Male
15      26.5  12     M04   Male
16      27.0  14     M04   Male
17      20.0   8     M05   Male
18      23.5  10     M05   Male
19      22.5  12     M05   Male
20      26.0  14     M05   Male
21      24.5   8     M06   Male
22      25.5  10     M06   Male
23      27.0  12     M06   Male
24      28.5  14     M06   Male
25      22.0   8     M07   Male
26      22.0  10     M07   Male
27      24.5  12     M07   Male
28      26.5  14     M07   Male
29      24.0   8     M08   Male
30      21.5  10     M08   Male
31      24.5  12     M08   Male
32      25.5  14     M08   Male
33      23.0   8     M09   Male
34      20.5  10     M09   Male
35      31.0  12     M09   Male
36      26.0  14     M09   Male
37      27.5   8     M10   Male
38      28.0  10     M10   Male
39      31.0  12     M10   Male
40      31.5  14     M10   Male
41      23.0   8     M11   Male
42      23.0  10     M11   Male
43      23.5  12     M11   Male
44      25.0  14     M11   Male
45      21.5   8     M12   Male
46      23.5  10     M12   Male
47      24.0  12     M12   Male
48      28.0  14     M12   Male
49      17.0   8     M13   Male
50      24.5  10     M13   Male
51      26.0  12     M13   Male
52      29.5  14     M13   Male
53      22.5   8     M14   Male
54      25.5  10     M14   Male
55      25.5  12     M14   Male
56      26.0  14     M14   Male
57      23.0   8     M15   Male
58      24.5  10     M15   Male
59      26.0  12     M15   Male
60      30.0  14     M15   Male
61      22.0   8     M16   Male
62      21.5  10     M16   Male
63      23.5  12     M16   Male
64      25.0  14     M16   Male
65      21.0   8     F01 Female
66      20.0  10     F01 Female
67      21.5  12     F01 Female
68      23.0  14     F01 Female
69      21.0   8     F02 Female
70      21.5  10     F02 Female
71      24.0  12     F02 Female
72      25.5  14     F02 Female
73      20.5   8     F03 Female
74      24.0  10     F03 Female
75      24.5  12     F03 Female
76      26.0  14     F03 Female
77      23.5   8     F04 Female
78      24.5  10     F04 Female
79      25.0  12     F04 Female
80      26.5  14     F04 Female
81      21.5   8     F05 Female
82      23.0  10     F05 Female
83      22.5  12     F05 Female
84      23.5  14     F05 Female
85      20.0   8     F06 Female
86      21.0  10     F06 Female
87      21.0  12     F06 Female
88      22.5  14     F06 Female
89      21.5   8     F07 Female
90      22.5  10     F07 Female
91      23.0  12     F07 Female
92      25.0  14     F07 Female
93      23.0   8     F08 Female
94      23.0  10     F08 Female
95      23.5  12     F08 Female
96      24.0  14     F08 Female
97      20.0   8     F09 Female
98      21.0  10     F09 Female
99      22.0  12     F09 Female
100     21.5  14     F09 Female
101     16.5   8     F10 Female
102     19.0  10     F10 Female
103     19.0  12     F10 Female
104     19.5  14     F10 Female
105     24.5   8     F11 Female
106     25.0  10     F11 Female
107     28.0  12     F11 Female
108     28.0  14     F11 Female

*Mixed modeling and fixed effect test*
SAS
proc mixed data=formixed;
class gender age person;
model y = gender|age;
repeated / type=cs sub=person;
run;

output of interest to me
          Tests of Fixed Effects
Source             NDF   DDF    Type III F    Pr > F
GENDER           1        25        9.29        0.0054
AGE                  3        75       35.35       0.0001
GENDER*AGE   3        75        2.36        0.0781

R (nlme package)
y=lme(distance~Sex*age, random=(~1|Subject), data=dental)
anova(y)

            numDF denDF  F-value p-value
(Intercept)     1    75 4123.156  <.0001
Sex              1    25    9.292  0.0054
age               3    75   40.032  <.0001
Sex:age        3    75    2.362  0.0781

Now this isn't exact but it is extremely close, however when I try to
replicate the unstructured,

SAS
proc mixed data=formixed;
class gender age person;
model y = gender|age;
repeated / type=un sub=person;
run;

             Tests of Fixed Effects
Source          NDF DDF Type III F Pr > F
GENDER         1    25     9.29    0.0054
AGE                3    25    34.45   0.0001
GENDER*AGE 3    25     2.93    0.0532

R
either
y=lme(distance~Sex*age, random=(~1|Subject), corr=corSymm(,~1|Subject),
data=dental)
anova(y)
or
z=lme(distance~Sex*age, random=(~1|Subject), corr=corSymm(), data=dental)
anova(z)

gives the output

            numDF denDF  F-value    p-value
(Intercept)     1    75     4052.028  <.0001
Sex              1    25       8.462      0.0075
age               3    75      39.022    <.0001
Sex:age        3    75       2.868      0.0421

What am I doing wrong to replicate the unstructured linear mixed model from
SAS?

Regards,

Charles

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