Hello,
I am doing a parametric survival analysis with a loglogistic baseline hazard function. I will like to control for unobserved heterogeneity through a finite fixture model.
My problem is that the estimation does not converge for models with 3 or more groups.
I have tried to take out variabels with little variation, changing the technique, starting with different values and iterations. Nothing seems to work.
I would therefore like to here, if anyone can elaborate on, why this is not possibel or what I am doing wrong.
The data, you see, is generated from the sample function (10%) because of confidentiality.
If necessary, I can also upload the entire sample data-file (Just do not know, if it is allowed in here). It experiences the same problems as the entire dataset does.
Here is my code:
Data set:
I am doing a parametric survival analysis with a loglogistic baseline hazard function. I will like to control for unobserved heterogeneity through a finite fixture model.
My problem is that the estimation does not converge for models with 3 or more groups.
I have tried to take out variabels with little variation, changing the technique, starting with different values and iterations. Nothing seems to work.
I would therefore like to here, if anyone can elaborate on, why this is not possibel or what I am doing wrong.
The data, you see, is generated from the sample function (10%) because of confidentiality.
If necessary, I can also upload the entire sample data-file (Just do not know, if it is allowed in here). It experiences the same problems as the entire dataset does.
Here is my code:
Code:
clear all cls use "..." stset TIME, failure(EVENT) global xlist VAR_1 VAR_2 VAR_3 VAR_4 VAR_5 VAR_6 VAR_7 VAR_8 VAR_9 VAR_10 VAR_11 VAR_12 VAR_13 VAR_14 VAR_15 VAR_16 VAR_17 VAR_18 VAR_19 global dist logl //Base: fmm 1, emopts(iterate(1000)) iterate(200) technique(bhhh 30 nr 30) difficult: streg, distribution($dist) estimates store logl_base estat ic eststo logl_base estat summarize //1 group: fmm 1, emopts(iterate(1000)) iterate(1000) technique(bhhh 30 nr 30) difficult: streg $xlist , distribution($dist) estimates store en_old estat ic eststo logl_en estat summarize //2 groups: fmm 2, emopts(iterate(1000)) iterate(1000) technique(bhhh 30 nr 30) difficult: streg $xlist , distribution($dist) estimates store to_old estat ic eststo logl_to estat summarize //3 groups: fmm 3, emopts(iterate(1000)) iterate(1000) technique(bhhh 30 nr 30) difficult: streg $xlist , distribution($dist) estimates store tre_old estat ic eststo logl_tre estat summarize //4 groups: fmm 4, emopts(iterate(1000)) iterate(1000) technique(bhhh 30 nr 30) difficult: streg $xlist , distribution($dist) estimates store fire_old estat ic eststo logl_fire estat summarize
Data set:
Code:
* Example generated by -dataex-. To install: ssc install dataex clear input double(VAR_1 VAR_2 VAR_3 VAR_4 VAR_5 VAR_6 VAR_7 VAR_8 VAR_9 VAR_10 VAR_11 VAR_12 VAR_13 VAR_14 VAR_15 VAR_16 VAR_17 VAR_18 VAR_19 TIME EVENT) 85 1 0 0 0 0 0 0 0 0 0 0 0 3.891891891891892 0 .02702702702702703 0 1 0 37 0 99 1 0 0 0 0 0 0 0 0 0 0 0 3.0416666666666665 0 3.2916666666666665 1 1 1 11 1 100 1 0 1 0 0 0 0 0 0 0 0 0 6.857142857142857 .14285714285714285 .5714285714285714 1 1 0 7 1 83 1 0 0 0 0 0 1 0 0 0 0 0 3.5 3.5 .125 1 1 0 4 1 45 1 0 0 0 0 1 0 0 0 0 0 0 6.0588235294117645 .8235294117647058 1.2352941176470589 1 1 1 17 1 76 0 0 0 0 0 0 0 0 0 0 0 1 4.75 0 0 1 1 1 4 0 77.87949381650849 1 0 0 0 0 0 0 0 0 0 0 0 3.6122448979591835 .3877551020408163 .12244897959183673 0 1 0 50 1 89 0 0 0 0 0 0 0 0 0 0 0 0 4.4 .75 .15 1 1 1 20 1 66 1 0 1 0 0 0 0 0 0 0 0 0 2.6666666666666665 1.2 .06666666666666667 1 1 0 1 1 90 1 0 1 0 0 0 0 0 0 0 0 1 5.424242424242424 .030303030303030304 .3939393939393939 1 1 0 33 0 55 1 0 1 0 0 0 0 0 0 0 0 1 6.6 0 .6 0 1 1 5 1 89 0 0 0 0 0 0 0 0 0 0 0 1 3.7142857142857144 0 .14285714285714285 1 1 0 14 0 76 1 1 0 0 0 0 0 1 0 0 0 0 3.2903225806451615 .4032258064516129 .22580645161290322 1 1 0 64 0 79 1 0 0 0 0 0 0 0 0 0 0 0 2.6666666666666665 0 .2222222222222222 1 1 0 9 1 78 0 0 0 0 0 0 0 0 0 0 0 1 6.490196078431373 0 .37254901960784315 0 1 0 51 0 77.87949381650849 1 0 0 0 0 0 0 0 0 0 0 0 6.39622641509434 0 .24528301886792453 0 1 0 53 0 86 1 0 0 1 0 0 0 0 1 0 0 0 6.555555555555555 0 .5 1 1 0 18 1 78 1 0 0 0 0 0 0 0 0 0 0 0 2 1.6 .4 1 1 0 5 0 71 1 0 1 0 0 0 0 0 0 0 0 1 4.3428571428571425 0 .17142857142857143 1 1 0 8 1 73 1 0 0 0 0 0 0 0 0 0 0 0 5.25 5.25 .5 1 1 0 4 1 83 0 0 1 0 0 0 0 0 0 0 0 0 7 0 0 1 1 1 3 1 34 0 0 0 0 0 0 0 0 0 0 0 0 3.608108108108108 0 .14864864864864866 0 1 1 74 0 95 1 0 0 0 1 0 0 1 0 0 0 0 5.571428571428571 0 .7142857142857143 1 1 1 7 1 92 1 0 0 0 1 0 1 0 0 1 0 0 6.195652173913044 .06521739130434782 .10869565217391304 1 1 1 47 0 87 1 0 1 0 0 0 0 0 0 0 0 0 3.857142857142857 0 4.4523809523809526 1 1 1 2 1 66 1 1 0 0 0 0 0 1 0 0 0 0 3.5714285714285716 .2857142857142857 14.714285714285714 1 1 1 7 1 84 1 0 1 0 0 0 0 0 0 0 0 0 7 0 .75 1 1 1 7 1 74 1 0 0 0 0 1 0 0 0 0 0 0 3.727272727272727 0 .06060606060606061 1 1 0 38 0 77.87949381650849 1 0 1 0 0 0 0 0 0 0 0 0 4 0 0 0 1 1 1 1 84 1 1 0 0 0 0 0 0 0 0 0 0 3.6 0 .06666666666666667 1 1 0 15 0 78 1 0 0 0 0 0 1 0 0 0 0 1 2 0 0 1 1 1 2 1 91 1 0 0 0 0 0 0 0 0 0 0 0 4 0 1 1 1 0 1 1 84 1 1 0 0 0 0 0 1 0 0 0 0 5.857142857142857 2.761904761904762 .5714285714285714 1 1 1 21 0 85 1 0 1 0 0 0 0 0 0 0 0 0 2.903225806451613 0 .0967741935483871 1 1 0 16 1 83 1 0 0 0 0 0 0 0 0 0 0 0 1.75 1.5 .25 1 1 0 5 1 85 1 0 0 0 0 0 0 0 0 0 0 0 4.048387096774194 0 .27419354838709675 1 1 1 62 0 84 0 0 0 0 0 0 0 0 0 1 0 0 6.638888888888889 6.722222222222222 .5833333333333334 1 1 1 36 1 84 1 0 1 0 0 0 0 0 0 0 0 0 6.6521739130434785 0 .08695652173913043 1 1 1 23 0 45 0 0 0 0 0 0 1 0 0 0 0 0 4.586206896551724 .22413793103448276 .1896551724137931 1 1 1 66 1 93 1 0 0 0 0 0 0 0 0 0 0 0 3.7777777777777777 0 .14814814814814814 1 1 1 27 0 93 0 0 0 0 0 0 0 0 0 0 0 0 5.5 0 .5 1 1 0 2 1 44 1 0 1 0 0 0 0 0 0 0 0 0 6.1 .3 .75 1 1 0 20 1 67 1 0 1 0 0 0 0 0 0 0 0 0 5.25 0 .25 0 1 0 4 0 88 0 0 1 0 0 0 0 0 0 0 0 1 3.673469387755102 0 .4387755102040816 1 1 0 100 0 86 1 0 0 0 0 0 0 0 0 0 0 1 2 0 7 1 1 0 3 1 86 0 0 0 0 0 0 0 0 0 0 0 0 4.150943396226415 0 .2830188679245283 1 1 0 32 1 85 0 0 0 0 0 0 0 0 0 0 0 0 6.5 0 6.5 1 1 0 2 1 94 0 0 0 0 0 0 0 0 0 0 0 1 5.912087912087912 0 .21978021978021978 1 1 0 6 1 77.87949381650849 1 0 0 0 0 0 0 0 0 0 0 0 4 1 0 0 1 1 1 1 77 0 0 1 0 0 0 0 0 0 0 0 0 7 0 .5 1 1 1 2 1 62 0 1 0 0 0 0 0 0 0 0 0 1 10.11111111111111 10.444444444444445 18.88888888888889 0 1 1 10 0 67 0 0 0 0 0 0 0 0 0 0 0 0 6.6 0 .7 1 1 0 40 1 93 1 1 0 0 0 0 0 1 0 0 0 0 4.033333333333333 .05 .06666666666666667 1 1 0 62 0 39 1 0 1 0 0 0 0 0 0 0 0 0 6 4 .3333333333333333 1 1 1 3 1 89 1 0 1 0 0 0 0 0 0 0 0 0 4.085714285714285 .2 .24285714285714285 1 1 1 70 0 87 0 0 0 0 0 0 0 0 0 0 0 0 3.8285714285714287 .02857142857142857 .11428571428571428 1 1 0 70 0 82 1 0 0 0 1 0 0 0 0 0 0 0 3.6666666666666665 0 .0784313725490196 1 1 0 52 0 73 1 0 0 0 0 0 0 1 0 0 0 0 6.625 4.25 7.1875 1 1 0 16 1 81 0 0 0 0 0 0 0 0 0 0 0 0 3.0638297872340425 3.106382978723404 2.0638297872340425 1 1 0 40 1 83 1 0 1 0 0 0 0 0 0 0 0 0 6.47457627118644 .03389830508474576 .5423728813559322 1 1 1 59 0 83 1 0 1 0 0 0 0 0 0 1 0 0 4 0 .4 1 1 1 5 0 90 1 0 0 0 0 0 0 0 0 1 0 0 3.8536585365853657 0 .04878048780487805 1 0 1 41 0 84 1 0 0 0 0 0 0 0 1 0 0 1 3.923076923076923 0 .07692307692307693 1 1 0 27 1 54 1 0 1 0 0 0 0 0 0 0 0 0 3.52 .12 .06 1 1 0 50 0 77.87949381650849 0 0 0 0 0 0 0 0 0 0 0 0 2.090909090909091 4 0 0 1 0 14 1 79 0 0 0 0 0 0 0 0 0 0 0 1 3.8947368421052633 1.7894736842105263 .3684210526315789 1 1 0 12 1 77 0 0 0 0 0 0 0 0 0 0 0 0 3.789473684210526 0 .10526315789473684 1 1 1 13 1 76 0 0 1 0 0 0 0 1 0 1 0 0 4.666666666666667 0 .6666666666666666 1 1 1 3 0 85 0 0 0 0 0 0 0 0 0 0 0 1 6.527472527472527 0 .34065934065934067 0 1 0 91 0 94 0 0 0 0 0 0 0 0 0 0 0 1 4 1.6666666666666667 1.3333333333333333 0 1 1 3 1 78 1 0 0 0 0 0 0 0 0 0 0 0 6.4411764705882355 0 .4117647058823529 1 1 0 69 0 89 0 0 0 0 0 0 0 0 0 0 0 1 2 0 1 1 1 0 1 1 79 1 0 0 0 0 0 1 0 0 0 0 0 3 0 1 1 1 1 1 1 73 0 1 0 0 0 0 0 0 0 0 0 0 3.8666666666666667 0 .03333333333333333 1 1 1 32 0 48 0 0 0 0 0 0 0 0 0 0 0 1 4.615384615384615 6.107692307692307 7.523076923076923 1 1 1 65 1 84 1 0 1 0 0 0 0 0 0 0 0 1 3.761904761904762 0 .14285714285714285 1 1 0 21 0 78 1 0 1 0 0 0 0 1 0 0 0 0 3.8048780487804876 .12195121951219512 .4146341463414634 1 1 0 1 1 87 1 0 0 0 0 0 0 1 0 0 0 0 2.933333333333333 0 .2 1 1 0 19 1 86 0 0 1 0 0 0 0 0 1 0 1 0 3.9298245614035086 0 .3333333333333333 1 1 0 57 1 64 0 0 0 0 0 0 0 0 0 0 0 0 6.65625 3.375 .3958333333333333 1 1 0 96 0 85 1 1 0 0 0 0 0 0 0 0 0 0 3.814814814814815 0 .4074074074074074 1 1 1 27 0 73 1 1 0 0 0 0 0 0 0 0 0 0 3.87 0 .1 1 1 0 100 0 81 1 1 0 0 0 0 0 1 0 0 0 0 5.666666666666667 0 1 1 1 0 6 0 77 1 0 0 0 0 0 0 0 0 0 0 0 4 0 1 1 1 0 1 1 75 1 0 0 0 0 0 0 0 0 0 0 0 5 0 .3333333333333333 1 1 0 3 1 77.87949381650849 0 0 0 0 0 0 0 0 0 0 0 0 4 0 0 0 1 0 1 1 76 0 0 0 0 0 0 0 0 0 0 0 0 .5 .5 .5 1 1 0 2 0 50 0 0 0 0 0 0 0 0 0 0 0 0 3.8135593220338984 .6440677966101694 .0847457627118644 0 1 0 73 0 94 1 0 0 0 0 0 0 1 0 0 0 0 6.12 0 .12 1 1 0 26 0 76 1 1 0 0 0 0 0 0 0 0 0 1 5.2727272727272725 0 .23636363636363636 1 1 1 13 1 92 1 0 1 0 0 0 0 0 0 0 0 0 3.0714285714285716 .29591836734693877 .20408163265306123 1 1 0 98 0 45 1 0 0 0 0 0 0 0 0 0 0 0 7.733333333333333 .8666666666666667 2.466666666666667 1 1 0 15 1 83 1 0 1 0 0 1 0 0 0 0 0 0 5.666666666666667 0 .1111111111111111 1 1 1 12 1 86 1 1 0 0 0 0 0 1 0 0 0 1 7 0 4 1 1 1 2 1 94 1 0 1 0 0 0 0 0 0 0 0 0 5 0 .5 1 1 0 2 1 75 1 0 0 0 0 0 0 0 0 0 0 0 5.75 0 0 1 1 1 4 1 75 1 0 1 0 0 0 0 0 0 0 0 0 5.305555555555555 0 .1388888888888889 1 1 0 25 1 88 1 0 1 0 0 0 0 0 0 0 0 0 7 7 .5 1 1 0 3 1 89 1 0 0 0 0 0 0 0 0 0 0 1 5.819672131147541 .26229508196721313 .29508196721311475 1 1 1 61 0 85 1 0 0 0 0 0 0 0 0 0 0 0 5.6923076923076925 0 .15384615384615385 1 1 0 27 0 end
Comment