Programming the statistical procedures from SAS

ODS output ParmInfo not created with proc GEE

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New Contributor
Posts: 4

ODS output ParmInfo not created with proc GEE

Dear All,

 

I am using 9.4 (TS1M5).
I am trying to write ods output while using a weighted proc gee by imputation. While it does generate ods output GEEEmpPEst and GEERCov, I get an error message for parminfo: Output 'ParmInfo' was not created. Make sure that the output object name, label, or path is spelled correctly. Also, verify that the appropriate procedure options are used to produce the requested output object. For example, verify that the NOPRINT option is not used.

 

I have tried adding the PRINTMLE option to the repeated statement which was suggested for a similar problem on this board. That did not work.

 

Here is my code: 

Proc gee data =  a;
where age_mo <= &age;
weight weight;
by _imputation_;
Class psnum visit haz4 (ref = '3') season (ref = first) sector (ref = first) 
hbasch (ref = 'AA') /param = ref;
Model &malvar = haz4 nmanem4 laginfection season momed ses2 age_mo age_mosq age_mocu 
malaria sector mompar hbasch sexa lagzheight lag2zheight mal1prev mal2prev haz4*nmanem4 / dist = mult link = glogit;
Repeated subject = psnum / within = visit ECOVB;
ods output GEEEmpPEst=parm_zheight_int_nma_&malvar ParmInfo= parminfo GEERCov= cov_&malvar;
run; 

 

 

Any suggestion will be appreciated please.

 

Thanks

Katy

Super User
Posts: 23,709

Re: ODS output ParmInfo not created with proc GEE

Do you see the estimated regression parameter empirical covariance matrix in the output?
New Contributor
Posts: 4

Re: ODS output ParmInfo not created with proc GEE

Hi Reeza,

 

Yes, I see the covariance matrix for each imputation in the output.

 

Katy

SAS Employee
Posts: 97

Re: ODS output ParmInfo not created with proc GEE

Can you post the LOG for the entire GEE call?

New Contributor
Posts: 4

Re: ODS output ParmInfo not created with proc GEE

  Proc gee data = /*mizheight descending*/ diss.aim1_mi_analysis descending  /*small_mi
13 ! descending*/ ;
NOTE: Writing HTML Body file: sashtml.htm
14       where age_mo <= &age;
15       weight weight;
16       by _imputation_;
17       Class psnum visit haz4 (ref = '3') season (ref = first) sector (ref = first)
18               hbasch (ref = 'AA') /param = ref;
19       Model &malvar = haz4 nmanem4 laginfection season momed ses2 age_mo age_mosq age_mocu
20           malaria sector mompar hbasch sexa lagzheight lag2zheight mal1prev mal2prev
20 ! haz4*nmanem4 / dist = mult link = glogit;
21       Repeated subject = psnum / within = visit ECOVB;
22       ods output  GEEEmpPEst=parm_zheight_int_nma_&malvar ParmInfo= parminfo GEERCov=
22 ! cov_&malvar;
23   run;

NOTE: The data set WORK.COV_MAL1 has 2800 observations and 58 variables.
NOTE: The data set WORK.PARM_ZHEIGHT_INT_NMA_MAL1 has 2800 observations and 10 variables.
WARNING: Output 'ParmInfo' was not created.  Make sure that the output object name, label, or path
         is spelled correctly.  Also, verify that the appropriate procedure options are used to
         produce the requested output object.  For example, verify that the NOPRINT option is not
         used.
NOTE: PROCEDURE GEE used (Total process time):
      real time           59.64 seconds
      cpu time            41.01 seconds

SAS Employee
Posts: 97

Re: ODS output ParmInfo not created with proc GEE

Proc GEE does not produce a ParmInfo table when you have a multinomial response, although it probably should.

 

That being said, it looks like you are requesting it in order to run Proc MIANALYZE in the next step.  Thankfully you do not actually need the COVB or PARMINFO output when you run MIANALYZE with a CLASS statement.  The only time you need those is with the TEST statment or the MULT option, both of which are unavailable when you have a CLASS statement.  Instead you should be able to mimic the example below for your model.

 

 

data Housing;
input ID Housing Time Sec;
datalines;
1 1 0 1
1 2 6 1
1 2 12 1
1 2 24 1
2 1 0 1
2 2 6 1
2 2 12 1
2 1 24 1
3 0 0 1
3 2 6 1
3 2 12 1
3 2 24 1
4 1 0 1
4 1 6 1
4 1 12 1
4 1 24 1
5 0 0 1
5 1 6 1
5 2 12 1
5 2 24 1
6 2 0 1
6 2 6 1
6 2 12 1
6 2 24 1
7 2 0 1
7 2 6 1
7 2 12 1
7 2 24 1
8 2 0 1
8 0 6 1
8 0 12 1
8 2 24 1
9 1 0 1
9 1 6 1
9 2 12 1
9 2 24 1
10 2 0 1
10 . 6 1
10 . 12 1
10 . 24 1
11 1 0 1
11 2 6 1
11 . 12 1
11 1 24 1
12 1 0 1
12 2 6 1
12 1 12 1
12 1 24 1
13 0 0 1
13 0 6 1
13 2 12 1
13 . 24 1
14 0 0 1
14 2 6 1
14 2 12 1
14 2 24 1
15 0 0 1
15 1 6 1
15 0 12 1
15 0 24 1
16 0 0 1
16 . 6 1
16 . 12 1
16 . 24 1
17 0 0 1
17 2 6 1
17 2 12 1
17 1 24 1
18 1 0 1
18 1 6 1
18 2 12 1
18 2 24 1
19 2 0 1
19 1 6 1
19 2 12 1
19 2 24 1
20 1 0 1
20 2 6 1
20 2 12 1
20 2 24 1
21 0 0 1
21 2 6 1
21 2 12 1
21 0 24 1
22 0 0 1
22 1 6 1
22 2 12 1
22 2 24 1
23 1 0 1
23 2 6 1
23 2 12 1
23 2 24 1
24 1 0 1
24 2 6 1
24 2 12 1
24 2 24 1
25 2 0 1
25 2 6 1
25 2 12 1
25 2 24 1
26 1 0 1
26 2 6 1
26 2 12 1
26 2 24 1
27 1 0 1
27 1 6 1
27 2 12 1
27 . 24 1
28 0 0 1
28 0 6 1
28 0 12 1
28 0 24 1
29 0 0 1
29 2 6 1
29 1 12 1
29 1 24 1
30 2 0 1
30 . 6 1
30 . 12 1
30 . 24 1
31 2 0 1
31 2 6 1
31 2 12 1
31 2 24 1
32 0 0 1
32 0 6 1
32 . 12 1
32 . 24 1
33 1 0 1
33 . 6 1
33 . 12 1
33 . 24 1
34 2 0 1
34 2 6 1
34 0 12 1
34 0 24 1
35 0 0 1
35 1 6 1
35 0 12 1
35 0 24 1
36 2 0 1
36 2 6 1
36 2 12 1
36 2 24 1
37 0 0 1
37 1 6 1
37 1 12 1
37 . 24 1
38 1 0 1
38 . 6 1
38 2 12 1
38 1 24 1
39 1 0 1
39 2 6 1
39 2 12 1
39 2 24 1
40 1 0 1
40 2 6 1
40 2 12 1
40 2 24 1
41 0 0 1
41 2 6 1
41 0 12 1
41 2 24 1
42 1 0 1
42 2 6 1
42 2 12 1
42 0 24 1
43 2 0 1
43 2 6 1
43 1 12 1
43 1 24 1
44 1 0 1
44 2 6 1
44 2 12 1
44 2 24 1
45 0 0 1
45 2 6 1
45 1 12 1
45 2 24 1
46 0 0 1
46 2 6 1
46 2 12 1
46 2 24 1
47 0 0 1
47 2 6 1
47 2 12 1
47 2 24 1
48 0 0 1
48 2 6 1
48 2 12 1
48 2 24 1
49 0 0 1
49 2 6 1
49 2 12 1
49 2 24 1
50 0 0 1
50 . 6 1
50 . 12 1
50 . 24 1
51 1 0 1
51 2 6 1
51 2 12 1
51 2 24 1
52 1 0 1
52 . 6 1
52 . 12 1
52 . 24 1
53 0 0 1
53 2 6 1
53 . 12 1
53 0 24 1
54 0 0 1
54 1 6 1
54 1 12 1
54 1 24 1
55 0 0 1
55 2 6 1
55 2 12 1
55 2 24 1
56 0 0 1
56 1 6 1
56 . 12 1
56 0 24 1
57 1 0 1
57 1 6 1
57 2 12 1
57 1 24 1
58 2 0 1
58 2 6 1
58 2 12 1
58 2 24 1
59 1 0 1
59 2 6 1
59 2 12 1
59 2 24 1
60 2 0 1
60 2 6 1
60 2 12 1
60 2 24 1
61 1 0 1
61 2 6 1
61 2 12 1
61 2 24 1
62 1 0 1
62 2 6 1
62 2 12 1
62 2 24 1
63 1 0 1
63 . 6 1
63 . 12 1
63 . 24 1
64 1 0 1
64 1 6 1
64 1 12 1
64 1 24 1
65 2 0 1
65 2 6 1
65 1 12 1
65 . 24 1
66 0 0 1
66 2 6 1
66 . 12 1
66 0 24 1
67 1 0 1
67 0 6 1
67 2 12 1
67 1 24 1
68 1 0 1
68 2 6 1
68 . 12 1
68 2 24 1
69 0 0 1
69 2 6 1
69 2 12 1
69 2 24 1
70 0 0 1
70 2 6 1
70 2 12 1
70 2 24 1
71 1 0 1
71 2 6 1
71 2 12 1
71 1 24 1
72 0 0 1
72 1 6 1
72 2 12 1
72 1 24 1
73 1 0 1
73 . 6 1
73 2 12 1
73 2 24 1
74 0 0 1
74 1 6 1
74 2 12 1
74 2 24 1
75 0 0 1
75 2 6 1
75 2 12 1
75 2 24 1
76 0 0 1
76 1 6 1
76 . 12 1
76 2 24 1
77 1 0 1
77 2 6 1
77 2 12 1
77 2 24 1
78 0 0 1
78 1 6 1
78 2 12 1
78 2 24 1
79 0 0 1
79 2 6 1
79 2 12 1
79 2 24 1
80 1 0 1
80 2 6 1
80 1 12 1
80 1 24 1
81 1 0 1
81 2 6 1
81 2 12 1
81 2 24 1
82 0 0 1
82 1 6 1
82 2 12 1
82 2 24 1
83 1 0 1
83 2 6 1
83 2 12 1
83 2 24 1
84 0 0 1
84 2 6 1
84 2 12 1
84 2 24 1
85 1 0 1
85 2 6 1
85 2 12 1
85 2 24 1
86 0 0 1
86 1 6 1
86 0 12 1
86 1 24 1
87 2 0 1
87 2 6 1
87 0 12 1
87 . 24 1
88 2 0 1
88 2 6 1
88 2 12 1
88 1 24 1
89 0 0 1
89 2 6 1
89 2 12 1
89 2 24 1
90 1 0 1
90 2 6 1
90 2 12 1
90 2 24 1
91 1 0 1
91 . 6 1
91 . 12 1
91 . 24 1
92 1 0 1
92 . 6 1
92 . 12 1
92 . 24 1
93 1 0 1
93 2 6 1
93 2 12 1
93 2 24 1
94 0 0 1
94 0 6 1
94 0 12 1
94 0 24 1
95 0 0 1
95 2 6 1
95 2 12 1
95 2 24 1
96 0 0 1
96 2 6 1
96 2 12 1
96 2 24 1
97 0 0 1
97 1 6 1
97 . 12 1
97 . 24 1
98 0 0 1
98 2 6 1
98 2 12 1
98 2 24 1
99 0 0 1
99 0 6 1
99 0 12 1
99 0 24 1
100 1 0 1
100 2 6 1
100 1 12 1
100 1 24 1
101 0 0 1
101 0 6 1
101 0 12 1
101 0 24 1
102 2 0 1
102 1 6 1
102 2 12 1
102 0 24 1
103 0 0 1
103 2 6 1
103 2 12 1
103 2 24 1
104 1 0 1
104 2 6 1
104 2 12 1
104 2 24 1
105 1 0 1
105 1 6 1
105 2 12 1
105 2 24 1
106 2 0 1
106 2 6 1
106 2 12 1
106 2 24 1
107 1 0 1
107 1 6 1
107 1 12 1
107 1 24 1
108 1 0 1
108 2 6 1
108 2 12 1
108 1 24 1
109 0 0 1
109 2 6 1
109 2 12 1
109 2 24 1
110 1 0 1
110 2 6 1
110 2 12 1
110 2 24 1
111 1 0 1
111 . 6 1
111 . 12 1
111 . 24 1
112 0 0 1
112 1 6 1
112 0 12 1
112 1 24 1
113 2 0 1
113 1 6 1
113 2 12 1
113 2 24 1
114 0 0 1
114 1 6 1
114 1 12 1
114 2 24 1
115 1 0 1
115 0 6 1
115 0 12 1
115 1 24 1
116 1 0 1
116 . 6 1
116 . 12 1
116 . 24 1
117 0 0 1
117 1 6 1
117 2 12 1
117 1 24 1
118 2 0 1
118 2 6 1
118 2 12 1
118 2 24 1
119 1 0 1
119 1 6 1
119 1 12 1
119 . 24 1
120 2 0 1
120 2 6 1
120 2 12 1
120 2 24 1
121 1 0 1
121 2 6 1
121 2 12 1
121 2 24 1
122 0 0 1
122 2 6 1
122 2 12 1
122 2 24 1
123 0 0 1
123 0 6 1
123 2 12 1
123 2 24 1
124 0 0 1
124 2 6 1
124 2 12 1
124 2 24 1
125 0 0 1
125 2 6 1
125 2 12 1
125 2 24 1
126 0 0 1
126 2 6 1
126 2 12 1
126 2 24 1
127 1 0 1
127 2 6 1
127 2 12 1
127 2 24 1
128 0 0 1
128 1 6 1
128 1 12 1
128 1 24 1
129 1 0 1
129 2 6 1
129 2 12 1
129 2 24 1
130 2 0 1
130 2 6 1
130 2 12 1
130 2 24 1
131 1 0 1
131 1 6 1
131 0 12 1
131 1 24 1
132 0 0 1
132 2 6 1
132 2 12 1
132 2 24 1
133 1 0 1
133 2 6 1
133 2 12 1
133 . 24 1
134 1 0 1
134 . 6 1
134 2 12 1
134 2 24 1
135 1 0 1
135 . 6 1
135 2 12 1
135 1 24 1
136 0 0 1
136 1 6 1
136 1 12 1
136 1 24 1
137 1 0 1
137 2 6 1
137 2 12 1
137 2 24 1
138 1 0 1
138 2 6 1
138 2 12 1
138 2 24 1
139 0 0 1
139 2 6 1
139 2 12 1
139 2 24 1
140 0 0 1
140 2 6 1
140 2 12 1
140 2 24 1
141 1 0 1
141 1 6 1
141 1 12 1
141 2 24 1
142 2 0 1
142 2 6 1
142 2 12 1
142 2 24 1
143 0 0 1
143 1 6 1
143 1 12 1
143 1 24 1
144 0 0 1
144 2 6 1
144 2 12 1
144 2 24 1
145 0 0 1
145 1 6 1
145 . 12 1
145 . 24 1
146 0 0 1
146 1 6 1
146 . 12 1
146 2 24 1
147 0 0 1
147 0 6 1
147 0 12 1
147 1 24 1
148 0 0 1
148 1 6 1
148 0 12 1
148 0 24 1
149 1 0 1
149 2 6 1
149 2 12 1
149 1 24 1
150 0 0 1
150 1 6 1
150 1 12 1
150 1 24 1
151 1 0 1
151 2 6 1
151 2 12 1
151 2 24 1
152 1 0 1
152 0 6 1
152 0 12 1
152 0 24 1
153 1 0 1
153 2 6 1
153 2 12 1
153 2 24 1
154 1 0 1
154 1 6 1
154 . 12 1
154 . 24 1
155 1 0 1
155 . 6 1
155 . 12 1
155 0 24 1
156 0 0 1
156 0 6 1
156 . 12 1
156 1 24 1
157 1 0 1
157 2 6 1
157 2 12 1
157 2 24 1
158 0 0 1
158 2 6 1
158 2 12 1
158 2 24 1
159 0 0 1
159 1 6 1
159 2 12 1
159 2 24 1
160 1 0 1
160 2 6 1
160 2 12 1
160 2 24 1
161 1 0 1
161 2 6 1
161 2 12 1
161 0 24 1
162 2 0 1
162 1 6 1
162 2 12 1
162 2 24 1
163 1 0 1
163 2 6 1
163 2 12 1
163 . 24 1
164 0 0 1
164 0 6 1
164 2 12 1
164 2 24 1
165 0 0 1
165 1 6 1
165 2 12 1
165 2 24 1
166 0 0 1
166 0 6 1
166 0 12 1
166 0 24 1
167 1 0 1
167 1 6 1
167 2 12 1
167 2 24 1
168 0 0 1
168 1 6 1
168 2 12 1
168 2 24 1
169 1 0 1
169 2 6 1
169 2 12 1
169 2 24 1
170 0 0 1
170 2 6 1
170 1 12 1
170 1 24 1
171 1 0 1
171 2 6 1
171 2 12 1
171 2 24 1
172 1 0 1
172 . 6 1
172 1 12 1
172 2 24 1
173 2 0 1
173 2 6 1
173 2 12 1
173 2 24 1
174 0 0 1
174 1 6 1
174 0 12 1
174 0 24 1
175 0 0 1
175 . 6 1
175 1 12 1
175 1 24 1
176 0 0 1
176 2 6 1
176 2 12 1
176 1 24 1
177 1 0 1
177 2 6 1
177 2 12 1
177 2 24 1
178 0 0 1
178 . 6 1
178 2 12 1
178 2 24 1
179 1 0 1
179 . 6 1
179 2 12 1
179 2 24 1
180 2 0 1
180 2 6 1
180 1 12 1
180 1 24 1
181 0 0 1
181 2 6 1
181 2 12 1
181 2 24 1
182 1 0 0
182 0 6 0
182 0 12 0
182 1 24 0
183 2 0 0
183 . 6 0
183 1 12 0
183 0 24 0
184 0 0 0
184 1 6 0
184 1 12 0
184 1 24 0
185 1 0 0
185 1 6 0
185 1 12 0
185 0 24 0
186 1 0 0
186 1 6 0
186 1 12 0
186 1 24 0
187 0 0 0
187 2 6 0
187 2 12 0
187 2 24 0
188 2 0 0
188 2 6 0
188 1 12 0
188 1 24 0
189 0 0 0
189 1 6 0
189 0 12 0
189 . 24 0
190 0 0 0
190 1 6 0
190 1 12 0
190 1 24 0
191 0 0 0
191 0 6 0
191 2 12 0
191 2 24 0
192 0 0 0
192 1 6 0
192 1 12 0
192 1 24 0
193 1 0 0
193 . 6 0
193 1 12 0
193 . 24 0
194 0 0 0
194 1 6 0
194 . 12 0
194 . 24 0
195 1 0 0
195 2 6 0
195 1 12 0
195 1 24 0
196 0 0 0
196 0 6 0
196 0 12 0
196 0 24 0
197 1 0 0
197 1 6 0
197 1 12 0
197 2 24 0
198 0 0 0
198 2 6 0
198 2 12 0
198 2 24 0
199 1 0 0
199 1 6 0
199 1 12 0
199 1 24 0
200 0 0 0
200 1 6 0
200 1 12 0
200 2 24 0
201 2 0 0
201 1 6 0
201 1 12 0
201 1 24 0
202 0 0 0
202 0 6 0
202 1 12 0
202 0 24 0
203 1 0 0
203 1 6 0
203 1 12 0
203 1 24 0
204 1 0 0
204 1 6 0
204 1 12 0
204 1 24 0
205 1 0 0
205 1 6 0
205 1 12 0
205 2 24 0
206 0 0 0
206 0 6 0
206 0 12 0
206 2 24 0
207 0 0 0
207 . 6 0
207 . 12 0
207 . 24 0
208 2 0 0
208 1 6 0
208 . 12 0
208 2 24 0
209 0 0 0
209 1 6 0
209 . 12 0
209 . 24 0
210 2 0 0
210 2 6 0
210 2 12 0
210 2 24 0
211 0 0 0
211 1 6 0
211 0 12 0
211 0 24 0
212 0 0 0
212 2 6 0
212 1 12 0
212 2 24 0
213 0 0 0
213 1 6 0
213 2 12 0
213 2 24 0
214 0 0 0
214 1 6 0
214 1 12 0
214 1 24 0
215 1 0 0
215 1 6 0
215 2 12 0
215 1 24 0
216 1 0 0
216 0 6 0
216 1 12 0
216 1 24 0
217 0 0 0
217 0 6 0
217 0 12 0
217 0 24 0
218 1 0 0
218 1 6 0
218 1 12 0
218 1 24 0
219 0 0 0
219 2 6 0
219 2 12 0
219 2 24 0
220 0 0 0
220 . 6 0
220 . 12 0
220 . 24 0
221 1 0 0
221 1 6 0
221 2 12 0
221 2 24 0
222 0 0 0
222 1 6 0
222 . 12 0
222 0 24 0
223 1 0 0
223 0 6 0
223 . 12 0
223 . 24 0
224 0 0 0
224 1 6 0
224 2 12 0
224 . 24 0
225 0 0 0
225 0 6 0
225 . 12 0
225 . 24 0
226 1 0 0
226 1 6 0
226 1 12 0
226 2 24 0
227 0 0 0
227 2 6 0
227 2 12 0
227 2 24 0
228 0 0 0
228 1 6 0
228 0 12 0
228 . 24 0
229 0 0 0
229 1 6 0
229 1 12 0
229 2 24 0
230 1 0 0
230 2 6 0
230 . 12 0
230 1 24 0
231 . 0 0
231 . 6 0
231 . 12 0
231 . 24 0
232 0 0 0
232 2 6 0
232 2 12 0
232 2 24 0
233 1 0 0
233 1 6 0
233 2 12 0
233 2 24 0
234 2 0 0
234 2 6 0
234 2 12 0
234 1 24 0
235 0 0 0
235 0 6 0
235 1 12 0
235 1 24 0
236 1 0 0
236 1 6 0
236 1 12 0
236 1 24 0
237 0 0 0
237 . 6 0
237 1 12 0
237 1 24 0
238 0 0 0
238 2 6 0
238 2 12 0
238 . 24 0
239 1 0 0
239 2 6 0
239 . 12 0
239 2 24 0
240 0 0 0
240 . 6 0
240 1 12 0
240 1 24 0
241 0 0 0
241 1 6 0
241 . 12 0
241 0 24 0
242 0 0 0
242 1 6 0
242 2 12 0
242 2 24 0
243 0 0 0
243 1 6 0
243 1 12 0
243 2 24 0
244 1 0 0
244 1 6 0
244 2 12 0
244 1 24 0
245 0 0 0
245 0 6 0
245 1 12 0
245 1 24 0
246 1 0 0
246 1 6 0
246 1 12 0
246 1 24 0
247 1 0 0
247 2 6 0
247 1 12 0
247 1 24 0
248 2 0 0
248 2 6 0
248 2 12 0
248 2 24 0
249 1 0 0
249 1 6 0
249 1 12 0
249 2 24 0
250 0 0 0
250 . 6 0
250 . 12 0
250 . 24 0
251 1 0 0
251 2 6 0
251 1 12 0
251 2 24 0
252 0 0 0
252 1 6 0
252 1 12 0
252 1 24 0
253 1 0 0
253 1 6 0
253 2 12 0
253 2 24 0
254 0 0 0
254 1 6 0
254 . 12 0
254 1 24 0
255 0 0 0
255 0 6 0
255 1 12 0
255 2 24 0
256 2 0 0
256 1 6 0
256 1 12 0
256 2 24 0
257 0 0 0
257 0 6 0
257 0 12 0
257 0 24 0
258 0 0 0
258 1 6 0
258 1 12 0
258 1 24 0
259 0 0 0
259 0 6 0
259 1 12 0
259 1 24 0
260 1 0 0
260 . 6 0
260 . 12 0
260 . 24 0
261 1 0 0
261 1 6 0
261 1 12 0
261 1 24 0
262 0 0 0
262 1 6 0
262 2 12 0
262 1 24 0
263 0 0 0
263 2 6 0
263 2 12 0
263 1 24 0
264 0 0 0
264 1 6 0
264 2 12 0
264 2 24 0
265 1 0 0
265 2 6 0
265 2 12 0
265 2 24 0
266 1 0 0
266 2 6 0
266 2 12 0
266 2 24 0
267 0 0 0
267 0 6 0
267 0 12 0
267 0 24 0
268 1 0 0
268 1 6 0
268 2 12 0
268 2 24 0
269 1 0 0
269 1 6 0
269 1 12 0
269 . 24 0
270 0 0 0
270 1 6 0
270 1 12 0
270 0 24 0
271 1 0 0
271 1 6 0
271 1 12 0
271 2 24 0
272 1 0 0
272 1 6 0
272 2 12 0
272 2 24 0
273 0 0 0
273 2 6 0
273 2 12 0
273 2 24 0
274 1 0 0
274 0 6 0
274 1 12 0
274 0 24 0
275 1 0 0
275 1 6 0
275 0 12 0
275 0 24 0
276 1 0 0
276 1 6 0
276 . 12 0
276 1 24 0
277 0 0 0
277 2 6 0
277 2 12 0
277 2 24 0
278 1 0 0
278 1 6 0
278 1 12 0
278 1 24 0
279 1 0 0
279 . 6 0
279 1 12 0
279 1 24 0
280 0 0 0
280 1 6 0
280 1 12 0
280 1 24 0
281 0 0 0
281 1 6 0
281 1 12 0
281 1 24 0
282 0 0 0
282 0 6 0
282 2 12 0
282 2 24 0
283 0 0 0
283 2 6 0
283 1 12 0
283 1 24 0
284 0 0 0
284 2 6 0
284 1 12 0
284 . 24 0
285 2 0 0
285 1 6 0
285 1 12 0
285 2 24 0
286 1 0 0
286 . 6 0
286 . 12 0
286 . 24 0
287 0 0 0
287 1 6 0
287 1 12 0
287 1 24 0
288 1 0 0
288 . 6 0
288 . 12 0
288 . 24 0
289 0 0 0
289 1 6 0
289 . 12 0
289 . 24 0
290 0 0 0
290 1 6 0
290 1 12 0
290 1 24 0
291 0 0 0
291 2 6 0
291 2 12 0
291 2 24 0
292 0 0 0
292 1 6 0
292 1 12 0
292 2 24 0
293 0 0 0
293 1 6 0
293 1 12 0
293 1 24 0
294 0 0 0
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294 2 24 0
295 1 0 0
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295 1 24 0
296 0 0 0
296 1 6 0
296 1 12 0
296 1 24 0
297 0 0 0
297 2 6 0
297 1 12 0
297 1 24 0
298 0 0 0
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298 1 12 0
298 1 24 0
299 0 0 0
299 1 6 0
299 2 12 0
299 2 24 0
300 0 0 0
300 0 6 0
300 1 12 0
300 1 24 0
301 2 0 0
301 1 6 0
301 1 12 0
301 1 24 0
302 0 0 0
302 . 6 0
302 . 12 0
302 . 24 0
303 1 0 0
303 1 6 0
303 1 12 0
303 2 24 0
304 2 0 0
304 2 6 0
304 2 12 0
304 2 24 0
305 0 0 0
305 1 6 0
305 1 12 0
305 1 24 0
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306 1 6 0
306 1 12 0
306 2 24 0
307 0 0 0
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308 2 24 0
309 0 0 0
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310 1 0 0
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310 1 12 0
310 1 24 0
311 0 0 0
311 0 6 0
311 0 12 0
311 0 24 0
312 2 0 0
312 1 6 0
312 . 12 0
312 2 24 0
313 0 0 0
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313 2 12 0
313 . 24 0
314 2 0 0
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315 2 0 0
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315 2 12 0
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316 1 0 0
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317 1 0 0
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317 2 12 0
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318 0 0 0
318 2 6 0
318 2 12 0
318 2 24 0
319 0 0 0
319 1 6 0
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319 . 24 0
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320 1 12 0
320 1 24 0
321 0 0 0
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321 2 12 0
321 2 24 0
322 0 0 0
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322 0 24 0
323 0 0 0
323 0 6 0
323 1 12 0
323 1 24 0
324 0 0 0
324 . 6 0
324 . 12 0
324 . 24 0
325 1 0 0
325 1 6 0
325 2 12 0
325 . 24 0
326 0 0 0
326 0 6 0
326 2 12 0
326 2 24 0
327 0 0 0
327 1 6 0
327 1 12 0
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328 1 0 0
328 1 6 0
328 1 12 0
328 2 24 0
329 0 0 0
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330 2 0 0
330 . 6 0
330 . 12 0
330 1 24 0
331 1 0 0
331 1 6 0
331 2 12 0
331 1 24 0
332 0 0 0
332 . 6 0
332 . 12 0
332 . 24 0
333 0 0 0
333 1 6 0
333 1 12 0
333 1 24 0
334 1 0 0
334 0 6 0
334 . 12 0
334 . 24 0
335 0 0 0
335 1 6 0
335 1 12 0
335 . 24 0
336 0 0 0
336 . 6 0
336 . 12 0
336 . 24 0
337 1 0 0
337 1 6 0
337 2 12 0
337 2 24 0
338 1 0 0
338 2 6 0
338 2 12 0
338 2 24 0
339 0 0 0
339 0 6 0
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340 1 0 0
340 1 6 0
340 1 12 0
340 0 24 0
341 0 0 0
341 . 6 0
341 . 12 0
341 . 24 0
342 0 0 0
342 . 6 0
342 . 12 0
342 1 24 0
343 0 0 0
343 0 6 0
343 0 12 0
343 2 24 0
344 2 0 0
344 . 6 0
344 . 12 0
344 . 24 0
345 1 0 0
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345 1 12 0
345 2 24 0
346 0 0 0
346 1 6 0
346 1 12 0
346 0 24 0
347 2 0 0
347 0 6 0
347 1 12 0
347 1 24 0
348 0 0 0
348 1 6 0
348 2 12 0
348 . 24 0
349 1 0 0
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350 2 0 0
350 1 6 0
350 1 12 0
350 2 24 0
351 1 0 0
351 0 6 0
351 1 12 0
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352 0 0 0
352 0 6 0
352 . 12 0
352 . 24 0
353 0 0 0
353 0 6 0
353 1 12 0
353 . 24 0
354 2 0 0
354 2 6 0
354 2 12 0
354 2 24 0
355 1 0 0
355 1 6 0
355 1 12 0
355 0 24 0
356 0 0 0
356 2 6 0
356 . 12 0
356 1 24 0
357 0 0 0
357 2 6 0
357 . 12 0
357 . 24 0
358 0 0 0
358 1 6 0
358 1 12 0
358 2 24 0
359 1 0 0
359 1 6 0
359 1 12 0
359 1 24 0
360 0 0 0
360 1 6 0
360 1 12 0
360 1 24 0
361 0 0 0
361 0 6 0
361 . 12 0
361 . 24 0
362 1 0 0
362 1 6 0
362 1 12 0
362 1 24 0
;
proc mi data=housing seed=1 out=housing_imp nimpute=5;
class housing time sec;
var sec time housing;
fcs logistic(housing/link=glogit);
run;


proc gee data=Housing_imp;
by _imputation_;
class ID Housing Time SEC;
model Housing=Sec / dist=multinomial link=glogit;
repeated subject=ID / within=Time ecovb;
ods output GEEEmpPEst=parms;
run;

/*Sort by the RESPONSE since you have to run each logit separately in MIANALYZE*/
proc sort data=parms;
by response _imputation_;
run;


proc mianalyze parms(classvar=level)=parms;
by response;
class sec;
modeleffects intercept sec;
run;

New Contributor
Posts: 4

Re: ODS output ParmInfo not created with proc GEE

Hi Rob,

 

Thanks for this information. This was how I had run my other models, so it's good to know I did it correctly. I was trying to utilize the covariance matrix and parminfo table to create confidence intervals for odds ratios involving my interaction term. It doesn't seem like there is code to do this within proc mianalyze (such as estimate or contrast statements) and the covariance table is by imputation. I am starting to think this might be too complex for me, and my best option might be to simply run stratified models, rather than using an interaction term. Unless there is some way to combine the covariances from the various imputations, similar to the way proc mianalyze combines the estimates from the imputations?

 

If you have any further thoughts on this I would welcome them, otherwise thanks for your help.

 

Katy

SAS Employee
Posts: 97

Re: ODS output ParmInfo not created with proc GEE

You could actually compute the log odds ratio in GEE with either an ESTIMATE statement or the LSMEANS statement.  You could then combine those values in MIANALYZE.  Then the final step would be to exponentiate the combined estimates and confidence intervals in a data step to convert them to odds ratios.

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