Hi, I want to estimate the impact of the number of years of schooling (yrschool, continuous variable, no negative)  on the salary (incwage, also continuous variable with no negative), controlling for age. The dataset included several surveys from different countries and different years (surveys are identified by the variable "sample"). I thus think the best model would be a Poisson regression with random intercept. According to SAS Guide, I should use the BGLIMM Procedure: http://documentation.sas.com/doc/en/pgmsascdc/9.4_3.4/statug/statug_bglimm_examples03.htm   My code is:   proc bglimm data=test.reduced3 seed=10571042 nmc=10000; class sample; model incwage = yrschool age / dist=poisson; random int / sub=sample; run; However I get the following error message: " ERROR: PROC BGLIMM failed to generate samples from the posterior distribution." How can I fix this? With the same dataset, I can run a normal Poisson model with no random intercept with proc genmod. ... View more

Hi, I want to run a ordered logistic model with unequalslopes. The predicted variables edunum has 3 categories: 0,1,2. The code is as follows: proc logistic data=work.immigrants_from_lfs; class sex pob_num(ref='0') period /param=ref; model edunum(descending)= period sex|cohort2 pob_num|cohort2 /unequalslopes; weight weight / norm; score data=work.immigdataset out=work.scored_immig; run; I get this weird warning message:   WARNING: Negative individual predicted probabilities were identified in the final model fit. You may want to modify your UNEQUALSLOPES specification. WARNING: The LOGISTIC procedure continues in spite of the above warning. Results shown are based on the last maximum likelihood iteration. Validity of the model fit is questionable.   The message only appears when both sex|cohort2 pob_num|cohort2 are included in predictors. Sex has 2 categories (0,1), pob_num has 8 categories (0-7) and cohort 2 has 99 categories (0-98).   Parameters look however ok. I tried to remove the weight and score statement, but it does not fix it. Any idea why is this happening? I don't understand how a logistic model can predict negative probabilities. ... View more

Hi, I'm a bit noob with scoring using proc logistic. I want a dataset that includes probabilities for all possible combination of categories of variables used in a logistic model.    I built the dataset to score as follows:   data work.immigdataset; do cohort2=0 to 98; do sex=0,1; do period=0,1; do pob_num=0 to 7; output; end; end; end; end; run; The logit model with the score statement is then: proc logistic data=work.immigrants_from_lfs; class sex pob_num(ref='0') period /param=ref; model edunum(descending)= period sex|cohort2 pob_num|cohort2 /unequalslopes; weight weight / norm; score data=work.immigdataset out=work.scored_immig; run; Edunum has 3 categories and the model is an ordered logit with unequal slopes.    There are no error messages. However, the problem is that the scoring is not done for each second row (see screen shot below). How can I fix this?   I don't see any obvious problem with parameters of the logit model.   Analysis of Maximum Likelihood Estimates Parameter   edunum DF Estimate Standard Error Wald Chi-Square Pr > ChiSq Intercept   2 1 -1.7993 0.0814 489.0406 <.0001 Intercept   1 1 -0.5025 0.0742 45.9176 <.0001 period 0 2 1 -0.2697 0.0127 451.0235 <.0001 period 0 1 1 -0.2942 0.0139 449.2595 <.0001 sex 0 2 1 1.5305 0.0868 311.0672 <.0001 sex 0 1 1 1.4247 0.0855 277.4533 <.0001 cohort2   2 1 0.0190 0.000992 366.2465 <.0001 cohort2   1 1 0.0204 0.000924 485.8860 <.0001 cohort2*sex 0 2 1 -0.0195 0.00104 349.6493 <.0001 cohort2*sex 0 1 1 -0.0172 0.00103 277.7880 <.0001 pob_num 1 2 1 -1.0595 0.1789 35.0924 <.0001 pob_num 1 1 1 -2.3889 0.1596 224.0873 <.0001 pob_num 2 2 1 1.6704 0.1560 114.6657 <.0001 pob_num 2 1 1 1.1015 0.1442 58.3199 <.0001 pob_num 3 2 1 1.8591 0.1383 180.7171 <.0001 pob_num 3 1 1 0.4209 0.1334 9.9507 0.0016 pob_num 4 2 1 0.7457 0.1458 26.1710 <.0001 pob_num 4 1 1 -0.1724 0.1411 1.4935 0.2217 pob_num 5 2 1 2.0834 0.2466 71.3532 <.0001 pob_num 5 1 1 -0.6860 0.2495 7.5600 0.0060 pob_num 6 2 1 4.3626 0.2696 261.7725 <.0001 pob_num 6 1 1 4.2384 0.4575 85.8354 <.0001 pob_num 7 2 1 2.3235 0.1332 304.4506 <.0001 pob_num 7 1 1 0.6312 0.1351 21.8367 <.0001 cohort2*pob_num 1 2 1 0.00791 0.00213 13.7568 0.0002 cohort2*pob_num 1 1 1 0.0210 0.00193 118.7234 <.0001 cohort2*pob_num 2 2 1 -0.0254 0.00188 182.9621 <.0001 cohort2*pob_num 2 1 1 -0.0217 0.00173 156.8366 <.0001 cohort2*pob_num 3 2 1 -0.0239 0.00167 205.5414 <.0001 cohort2*pob_num 3 1 1 -0.0115 0.00161 51.0017 <.0001 cohort2*pob_num 4 2 1 -0.00618 0.00174 12.5973 0.0004 cohort2*pob_num 4 1 1 -0.00090 0.00170 0.2826 0.5950 cohort2*pob_num 5 2 1 -0.0125 0.00287 18.9461 <.0001 cohort2*pob_num 5 1 1 0.0195 0.00299 42.8260 <.0001 cohort2*pob_num 6 2 1 -0.0338 0.00324 109.0780 <.0001 cohort2*pob_num 6 1 1 -0.0270 0.00549 24.1746 <.0001 cohort2*pob_num 7 2 1 -0.0246 0.00162 230.6967 <.0001 cohort2*pob_num 7 1 1 -0.00275 0.00166 2.7353 0.0982   ... View more

Hi, is there a way to do multilevel ordered logistic regression with unequal slopes with SAS? I want to model educational attainment (no high school, high school, postsecondary) with variables such as cohort, country cohort*country, religion, language and education of the mother. My understanding is that with a normal ordered logistic model, parameters for religion, language and education of the mother might be biased.  ... View more

Hi, The variable lab_pred is a binary variable indicating whether or not the person works (1=work).   Edu, sex, and agegr are categorical variables for the education (4 categories: 0,1,2,3), sex (two categories: 0,01) and age groups (from 15 to 70 by 5 years). For each combination of sex*edu*agegr, I want to calculate the proportion that works and the lower and upper bounds.   To produce this outcomes, I use the following code, but it produces a different table for each sub-category of sex*edu*agegr.     proc sort data=work.predict2; by sex edu agegr; run; proc freq data=work.predict2; table lab_pred /nocol nofreq nopercent binomial (CL=Wald(correct)); by sex edu agegr; run; How can I generate an output that will look like:   Sex Agegr Edu % lab_pred=1 Lower CI Upper CI 0 15 0       0 15 1       0 15 2       0 15 3       … … …       1 70 3               ... View more