2 weeks ago
jl4443
Fluorite | Level 6
Member since
08-08-2014
- 10 Posts
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Latest posts by jl4443
Subject Views Posted 244 2 weeks ago 298 2 weeks ago 479 02-19-2025 11:26 AM 569 02-18-2025 04:03 PM 559 06-24-2024 06:30 PM 791 06-05-2024 02:00 PM 666 06-02-2024 12:39 PM 872 06-01-2024 12:17 PM 752 06-01-2024 12:14 PM -
Activity Feed for jl4443
- Posted Re: PROC FMM PROBMODEL Output Interpretation on Statistical Procedures. 2 weeks ago
- Posted PROC FMM PROBMODEL Output Interpretation on Statistical Procedures. 2 weeks ago
- Posted Re: Joint test in PROC FMM? on Statistical Procedures. 02-19-2025 11:26 AM
- Posted Joint test in PROC FMM? on Statistical Procedures. 02-18-2025 04:03 PM
- Got a Like for SAS PROC FMM PROBMODEL Output. 06-26-2024 07:04 AM
- Posted SAS PROC FMM PROBMODEL Output on Statistical Procedures. 06-24-2024 06:30 PM
- Posted Re: PROC FMM: Test Parameters Across Mixtures on Statistical Procedures. 06-05-2024 02:00 PM
- Posted Re: PROC FMM: Order of Components on Statistical Procedures. 06-02-2024 12:39 PM
- Posted PROC FMM: Test Parameters Across Mixtures on Statistical Procedures. 06-01-2024 12:17 PM
- Posted PROC FMM: Order of Components on Statistical Procedures. 06-01-2024 12:14 PM
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My Liked Posts
Subject Likes Posted 2 06-24-2024 06:30 PM
2 weeks ago
Thanks for your response. I am trying to quantify the extent to which a covariate in the PROBMODEL statement influences subgroup membership. The probabilities on the logit scale as they are output are not straightforward to interpret, so I was thinking of potentially reporting something like 'covariate level A vs B is associated with increased likelihood of membership in the second subgroup (OR xx, 95% CI xx, xx)'. The benefit of reporting the odds ratio instead of the change in predicted probability would be that the values of the other covariates do not need to be specified.
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2 weeks ago
Is it correct that the exponentiated estimates from the PROC FMM PROBMODEL statement output correspond to odds ratios for subgroup membership? The output table header says "Parameter Estimates for Mixing Probabilities" and the documentation indicates that they are on the logit scale, and they seem to correspond to log-odds ratios, but that isn't directly stated in any of the documentation or examples I have come across. If the estimates are indeed log odds ratios, is there anyway to get SAS to exponentiate and output the odds ratio estimates directly? (The fact that it doesn't automatically is what made me question whether this is the proper interpretation, though it does seem to be the case). I appreciate any thoughts on this - thanks in advance.
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02-19-2025
11:26 AM
Thank you for taking a look at this question. When I run PROC FMM with the COV option on the PROC FMM statement and use ODS OUTPUT COV = COV_OUTPUT, The resulting COV_OUTPUT dataset includes a variable 'ModelNo' with a value of 1 and corresponding variable 'Label' = "Weibull" (since I have distribution=Weibull for my FMM outcome model) as well as data corresponding to a 'ModelNo' = 2 and 'Label' = "Probability Model". I interpreted this as the covariance matrix from the component membership model from the PROBMODEL statement, is that not the correct interpretation of that output?
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02-18-2025
04:03 PM
Since PROC FMM does not have an ESTIMATE or CONTRAST statement, the %NLEST macro can be used for hypothesis testing. However, the %NLEST macro does not support joint testing (according to this SAS note: https://support.sas.com/kb/24/094.html). Is there a procedure or alternative SAS macro that facilitates a joint hypothesis test of the PROBMODEL parameters in PROC FMM?
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06-24-2024
06:30 PM
2 Likes
Hi there, 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 here, 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! 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. 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;
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06-05-2024
02:00 PM
This is exactly what I was looking for. Thank you!
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06-02-2024
12:39 PM
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.
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06-01-2024
12:17 PM
Hello, 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. 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;
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06-01-2024
12:14 PM
Hello, 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. 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. 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;
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