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jl4443 TrackerThu, 15 Aug 2024 18:16:42 GMT2024-08-15T18:16:42ZSAS PROC FMM PROBMODEL Output
https://communities.sas.com/t5/Statistical-Procedures/SAS-PROC-FMM-PROBMODEL-Output/m-p/933615#M46555
<P>Hi there,</P><P> </P><P>I am trying to reconcile different parts of the output for PROC FMM with a PROBMODEL statement. Looking specifically at Table 43.12 in the example from the SAS documentation <A href="https://documentation.sas.com/doc/en/pgmsascdc/9.4_3.4/statug/statug_fmm_examples01.htm" target="_self">here</A>, I understand that looking at the mixing probabilities table is showing the probabilities on the logit scale, which can then be converted to probabilities of belonging to component 1 (the mu-hats shown on the table). However, If I include an OUTPUT statement and look at the values PRED_1 and PRED_2 for the values of the covariates shown on the table, they don't seem to align. I may be missing something, but I couldn't find documentation on PRED_1 - PRED_2 that would explain the difference, so I appreciate any suggestions! </P><P> </P><P>Part two of this question is whether there is a way to obtain the odds ratios for the covariates in the mixture model, in the case that there are two components. I can back into it once the above is addressed and I am sure I am looking at the right values for the probabilities, but was curious if there is a more direct way to get this output from PROC FMM. Thanks again. </P><P> </P><P> </P><PRE>data ossi;
length tx $8;
input tx$ n @@;
do i=1 to n;
input y m @@;
output;
end;
drop i;
datalines;
Control 18 8 8 9 9 7 9 0 5 3 3 5 8 9 10 5 8 5 8 1 6 0 5
8 8 9 10 5 5 4 7 9 10 6 6 3 5
Control 17 8 9 7 10 10 10 1 6 6 6 1 9 8 9 6 7 5 5 7 9
2 5 5 6 2 8 1 8 0 2 7 8 5 7
PHT 19 1 9 4 9 3 7 4 7 0 7 0 4 1 8 1 7 2 7 2 8 1 7
0 2 3 10 3 7 2 7 0 8 0 8 1 10 1 1
TCPO 16 0 5 7 10 4 4 8 11 6 10 6 9 3 4 2 8 0 6 0 9
3 6 2 9 7 9 1 10 8 8 6 9
PHT+TCPO 11 2 2 0 7 1 8 7 8 0 10 0 4 0 6 0 7 6 6 1 6 1 7
;
data ossi;
set ossi;
array xx{3} x1-x3;
do i=1 to 3; xx{i}=0; end;
pht = 0;
tcpo = 0;
if (tx='TCPO') then do;
xx{1} = 1;
tcpo = 100;
end; else if (tx='PHT') then do;
xx{2} = 1;
pht = 60;
end; else if (tx='PHT+TCPO') then do;
pht = 60;
tcpo = 100;
xx{1} = 1; xx{2} = 1; xx{3}=1;
end;
run;
proc fmm data=ossi;
class pht tcpo;
model y/m = / dist=binomcluster;
probmodel pht tcpo pht*tcpo;
output out = chk(keep = pht tcpo pred_:) pred(components);
run;
proc sort data = chk nodupkey; by pht tcpo; run;
proc print data = chk;
run;</PRE><P> </P><P> </P>Mon, 24 Jun 2024 22:30:02 GMThttps://communities.sas.com/t5/Statistical-Procedures/SAS-PROC-FMM-PROBMODEL-Output/m-p/933615#M46555jl44432024-06-24T22:30:02ZRe: PROC FMM: Test Parameters Across Mixtures
https://communities.sas.com/t5/Statistical-Procedures/PROC-FMM-Test-Parameters-Across-Mixtures/m-p/930999#M46387
<P>This is exactly what I was looking for. Thank you!</P>Wed, 05 Jun 2024 18:00:59 GMThttps://communities.sas.com/t5/Statistical-Procedures/PROC-FMM-Test-Parameters-Across-Mixtures/m-p/930999#M46387jl44432024-06-05T18:00:59ZRe: PROC FMM: Order of Components
https://communities.sas.com/t5/Statistical-Procedures/PROC-FMM-Order-of-Components/m-p/930568#M46375
<P>This is helpful, thanks! In my case, I think the easiest way to go will be to adjust the components after running the model in a post-processing step. I appreciate your response. </P>Sun, 02 Jun 2024 16:39:48 GMThttps://communities.sas.com/t5/Statistical-Procedures/PROC-FMM-Order-of-Components/m-p/930568#M46375jl44432024-06-02T16:39:48ZPROC FMM: Test Parameters Across Mixtures
https://communities.sas.com/t5/Statistical-Procedures/PROC-FMM-Test-Parameters-Across-Mixtures/m-p/930527#M46370
<P>Hello,</P><P> </P><P>I am running a two-component mixture model via PROC FMM, and want to compare the coefficient of a variable from the first component to the coefficient of the same variable in the second component. I noticed PROC FMM does not have an ESTIMATE or CONTRAST statement available, and I am wondering if it is otherwise possible to implement this comparison. Using the example below, I would like to compare the coefficient for 'dose' in component 1 to the coefficient for 'dose' in component 2 (and ideally get a confidence interval as well). I appreciate any suggestions. </P><P> </P><PRE>data assay;
label dose = 'Dose of quinoline (microg/plate)'
num = 'Observed number of colonies';
input dose @;
logd = log(dose+10);
do i=1 to 3; input num@; output; end;
datalines;
0 15 21 29
10 16 18 21
33 16 26 33
100 27 41 60
333 33 38 41
1000 20 27 42
;
run;
proc fmm data=assay;
model num = dose logd / dist=Poisson k = 2;
run;</PRE>Sat, 01 Jun 2024 16:17:51 GMThttps://communities.sas.com/t5/Statistical-Procedures/PROC-FMM-Test-Parameters-Across-Mixtures/m-p/930527#M46370jl44432024-06-01T16:17:51ZPROC FMM: Order of Components
https://communities.sas.com/t5/Statistical-Procedures/PROC-FMM-Order-of-Components/m-p/930526#M46369
<P>Hello,</P><P> </P><P>I am running a simulation study using PROC FMM for a two-component mixture model. When I compare the parameter estimates to the underlying simulated values, I want to ensure that what is called "Component 1" in one iteration of the simulation is consistent with "Component 1" across other iterations of the simulation (e.g., Component 1 is always the mixture with a larger intercept), so that components are comparable across iterations.</P><P><BR />I have reviewed the documentation for the RESTRICT statement, and see that it says it can be used "to impose order conditions on the parameters in a model" but I can't quite figure out how to implement this. The current code to implement my mixture model using PROC FMM is shown below. I appreciate any suggestions for ordering the components. Thank you. </P><P> </P><P> </P><PRE>proc fmm data=sim55.scenario55_sim_data_events;
where iteration = 1;
model log_outc_yrs = age_dx_centered / cl dist=normal k = 2 equate=scale;
probmodel pdl1_perc_norm / noint cl;
weight iptw_ipcw_trim97;
run;</PRE><P> </P>Sat, 01 Jun 2024 16:14:25 GMThttps://communities.sas.com/t5/Statistical-Procedures/PROC-FMM-Order-of-Components/m-p/930526#M46369jl44432024-06-01T16:14:25Z