Hi,   I am running a Proc HPForest model with several iterations based on different parameters. When I review the results, I find that the iteration (last row) that has the highest sensitivity, also has higher PredAll and PredOOB compared to other iterations. Any thoughts on this would be much appreciated. Thank you.   Obs vars leafsize maxdepth NTrees NLeaves PredAll PredOob MiscAll MiscOob LogLossAll LogLossOob Specificity Sensitivity Specificity_Test Sensitivity_Test 1 4 10 20 100 13178 0.175 0.208 0.251 0.328 0.528 0.602 0.9315 0.56466 0.88214 0.660 2 3 10 20 100 11980 0.180 0.208 0.259 0.332 0.539 0.602 0.9370 0.59655 0.89163 0.692 3 4 5 20 100 25094 0.150 0.208 0.199 0.326 0.470 0.601 0.9500 0.45690 0.86802 0.620 4 3 5 20 100 22631 0.158 0.207 0.212 0.329 0.488 0.601 0.9495 0.48966 0.87518 0.636 5 3 10 20 150 18026 0.180 0.208 0.257 0.326 0.539 0.602 0.9385 0.59483 0.88952 0.684 6 2 10 15 75 7226 0.188 0.209 0.286 0.334 0.559 0.605 0.9455 0.68534 0.91609 0.730 ... View more

Hello,   Would it be correct to assign weights to variables based on the inverse of their variance? The weights could be normalized to total to 1. Say, I have a binary(0/1) response variable Y, and 2 independent variables X1 and X2. I assign weights to each as mentioned before, say W1 and W2. Could I model as logOdds(Y) =  W1*X1 + W2*X2 ? Thanks in advance! ... View more

Hello,   I am getting an error when running proc logistic as follows: All observations have the same response. No statistics are computed. I know that my binary(0/1) response variable (bad) does NOT have the same value for observations in the data. I can't figure out why I am getting this error. The distribution of bads in the data is roughly 40% with value 1 and 60% with value 0. Any help is much appreciated. The code I am running is:   proc logistic data= train /*model_score_data(where=(selected=1))*/ descending /*outmodel=train_model*/; model bad = W_equipment W_Ever_31Plus_DPD W_TIB_Month W_PG1_VantageScore W_SBRI_Lease_Score W_PG1_InquiriesDuringLast6Months W_Months_Aged / selection=forward sle=0.1 ctable pprob=(0.1 to 0.5 by 0.05) lackfit outroc=train_roc clodds=pl link=logit ; weight weights; output out=train_scored_data predicted=Pred_prob predprobs=individual reschi=res_chi resdev=res_dev h=levrg difdev=dif_dev ; *score data = model_score_data(where=(selected=0)) out=test_scored_data ; run; ... View more

Hi, does anyone know how one could download files and sample programs associated with the book SAS for Linear Models by Littel, Stroup, Freund? Are these available? Thanks! ... View more