Hi, I ran a linear regression with proc genmod (with a cluster statement). However, when I calculate manually predicted values, they don't fit with what is predicted in the output out statement.   Here is the code of the model:     proc genmod data=SHARE.shareHI ; class smoke_bin(ref='0') obesity(ref='0') ah(ref='0') sex edu3(ref='2') depression(ref='0') vig_pa(ref='0') cntry (ref='5') athlos_id sex; model diff_logHi=hi|hi|hi age_num|age_num sex edu3 smoke_bin obesity ah depression vig_pa cntry / dist=n link=id ; repeated subject=athlos_id / type=exch; /*Generalized Estimating Equations that takes into account correlation among observations*/ weight pond; output out=work.predicted p=PredictedValue; run;     Here are the parameters:   Obs Covariate Category par_difHI P_ 1 Intercept   0.69374 *** 2 hi   -4.44389 *** 3 hi*hi   8.73896 *** 4 hi*hi*hi   -6.21405 *** 5 age_num   0.012015800 *** 6 age_num*age_num   -0.000130071 *** 7 sex 0 -0.01474 *** 8 sex 1 0   9 edu3 0 -0.05901 *** 10 edu3 1 -0.03252 *** 11 edu3 2 0   12 smoke_bin 1 -0.0199 *** 13 smoke_bin 0 0   14 obesity 1 -0.03258 *** 15 obesity 0 0   16 ah 1 -0.01104 ** 17 ah 0 0   18 depression 1 -0.03472 *** 19 depression 0 0   20 vig_pa 1 0.01694 *** 21 vig_pa 0 0   22 cntry 0 0.02285 ** 23 cntry 1 0.00532   24 cntry 4 0.00173   25 cntry 6 0.03573 *** 26 cntry 7 -0.04092 *** 27 cntry 8 0.00929   28 cntry 10 0.00385   29 cntry 11 0.01284 * 30 cntry 15 -0.00555   31 cntry 20 0.03243 *** 32 cntry 21 0.01539 * 33 cntry 24 0.03717 *** 34 cntry 25 0.0168 * 35 cntry 5 0     Here are some selected observations, with the predicted values by SAS:   Obs age_num smoke_bin hi obesity ah sex edu3 depression vig_pa cntry sex diff_logHi PredictedValue 16376 65 0 0.48359 0 0 0 2 0 1 5 0 0.38392 0.11933 18532 66 0 0.43931 0 0 0 1 0 0 5 0 0.04253 0.08041   So if I calculated myself the predicted value for obs=16376, it should be: 0.69374+-4.44389*0.48359+8.73896*0.48359*0.48359+-6.21405*0.48359*0.48359*0.48359+0.0120158*65+-0.000130071*65*65+0.01694*1=0.134064. However, the predicted value by the output out statement is 0.11933   First I though it was a question of decimals for the parameters of age_num or age_num*age_num, but adding more decimals don't change much to the calculation.   ... View more

Hi, I want to model the probability of being obese with a set a risk factors. Data are from a longitudinal survey (so there are many observations for one single individuals). I thus use GEE with proc GENMOD, which I think is the appropriate model for this kind of situation. However, I have this error:   NOTE: Class levels for some variables were not printed due to excessive size. NOTE: PROC GENMOD is modeling the probability that obe1='1'. NOTE: Algorithm converged. ERROR: Error in computing the variance function. ERROR: Error in parameter estimate covariance computation. ERROR: Error in estimation routine.   I identified the problematic variable, which is cntry (for country), but the error is still there. Here is the code for this simplified model:       proc genmod data=work.data1 descending ;   class  smoking(ref='0') obesity(ref='0') ah(ref='0') edu3(ref='2') depression(ref='0') vig_pa(ref='0') cntry(ref='DE') noid sex;    model  obe1=/*obesity sex age_num age_num*age_num edu3 smoking ah depression vig_pa*/ cntry /*cntry*sex dur*//  dist=bin link=logit;    repeated subject=noid / type=exch;    weight pond; run;     Analysis Of GEE Parameter Estimates Empirical Standard Error Estimates Parameter   Estimate Standard Error 95% Confidence Limits Z Pr > |Z| Intercept   -1.6629 . . . . . cntry AT 0.5749 . . . . . cntry BE 0.4967 . . . . . cntry CZ 0.9894 . . . . . cntry DK 0.7170 . . . . . cntry EE 0.8038 . . . . . cntry ES 0.4774 . . . . . cntry FR 13.7220 . . . . . cntry GR 0.2397 . . . . . cntry IT 0.7010 . . . . . cntry NT 0.0756 . . . . . cntry SE 1790526 . . . . . cntry SI 0.6285 . . . . . cntry DE 0.0000 0.0000 0.0000 0.0000 . .     And this is the crosstab for the obe1*cntry. I don’t see any category that may be problematic.     proc freq data=work.data1; table obe1*cntry /nocol norow nopercent; weight pond; run; Table of obe1 by cntry obe1 cntry AT BE CZ DE DK EE ES FR GR IT NT SE SI Total 0 719.568 836.852 508.02 2711.3 275.363 97.5724 3317.06 3159.86 609.726 3309.08 739.526 351.252 139.237 16774.4 1 180.909 197.059 220.64 744.693 41.8123 41.3259 913.276 736.899 147.012 717.024 146.01 62.5714 49.4657 4198.7 Total 900.477 1033.91 728.66 3456 317.176 138.898 4230.34 3896.76 756.738 4026.1 885.536 413.823 188.702 20973.1     Note 1: the model works when I remove the weight statement. The range of the weight is from 0.006735 to 46.43589. Note 2: the model works when I use another dependent variable (and using the weight statement).   ... View more

Hi, I was wondering how increasing the number of decimal in the outputs of PROC GENMOD?   With this very simple model: proc genmod data=SHARE.shareHI ; class id; model diff_hi=age_num age_num *age_num  / dist=n ; repeated subject=id / type=exch; / weight pond; output out=work.predicted p=PredictedValue; run;   I have this output:     Analysis Of GEE Parameter Estimates Empirical Standard Error Estimates Parameter Estimate Standard Error 95% Confidence Limits Z Pr > |Z| Intercept 0.0274 0.0230 -0.0176 0.0724 1.19 0.2324 age_num -0.0010 0.0007 -0.0024 0.0004 -1.39 0.1653 age_num*age_num 0.0000 0.0000 0.0000 0.0000 2.08 0.0372       The parameter for age_num*age_num is 0.0000, but significant (but I have no idea with this outputs what is the parameter). I guess I could just multply the variable age_num by 10000, but I would prefer not touching it.     ... View more

Hi, I performed a regression with proc GLM using these commands (the dataset is attached):   proc glm data=act.taux_activite; class education(ref='Aucun') region(ref='NIAME') sexe(ref='Masculin') grage(ref='30_34'); model logittaux = grage education region sexe grage*sexe grage*education/solution; output out=test predicted=plogittaux; run;   I want predicted values, so I added the statement output out=test predicted=plogittaux;   I manage to have parameters, but for I reason I don't get, the procedure keeps running, and the output dataset "test" is never created.   Is something wrong with my codes? ... View more

Hi, I ran a proc tabulate procedure, but in the ouputs, there are some "missing" cells. However, a small percent should appear in these cells. When I do the same table with proc freq, the expected value is there.   Here is the codes with proc tabulate and the outputs are attached. The link for the dataset: https://www.dropbox.com/s/wn8vp7l2ioj3pkp/migration_edu_calib.sas7bdat?dl=0     proc tabulate data=work.migration_edu_calib out=work.base_age_prop format=20.19; class v2 v3; table v2, v3*ROWPCTN; freq pond_edu; run; proc freq data=work.migration_edu_calib; table v2*v3/nopercent nocol nofreq; weight pond_edu; run; See for instance v2=23 * v3=22, or the whole col v3=97. I guess it was something to do with the weight/freq statement. I use the proc tabulate because I want a output in a SAS dataset where percents are in a single column.   ... View more