Introduction to Logistic regression in SAS

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The following is the summary of the common logistics regression in sas:

ods graphics on; ods trace on; /* to see what tables might be available in output */ ods output ParameterEstimates=Parameter1; /*output parameter estimate*/ proc logistic data=Data1Acknowledgement: The tutorial is based on the notes from: www.ats.ucla.edu.descending/* the same as (event='1');*/namelen=100 /* give enough length so not supress variables' output */ /*namelen can be also applied in other regression e.g. proc glm */ plots=roc /* generate ROC AUC(area under curve) ouptut */outest=Cov_betas covout; /*generate variance/covariance of variables*/ model dep_var (event='1') = &vars./CTABLE PPROB=(0 to 1 by .10) /*generate the misclassification rate for each slice*/ or/CTABLE PPROB=(0.3, 0.5 to 0.8 by 0.1) /* generate the rate for cutoff at 0.3, 0.5, 0.6, 0.7, and 0.8*/for example: Classification TableP_Level True_Pos True_neg False_Pos False_neg % Sensi Speci False_Pos_% False_Neg_% 0.000 75 0 572 0 11.6 100.0 0.0 88.4 . 0.100 55 377 195 20 66.8 73.3 65.9 78.0 5.0 0.200 28 502 70 47 81.9 37.3 87.8 71.4 8.6 0.300 16 547 25 59 87.0 21.3 95.6 61.0 9.7 ... 0.800 1 572 0 74 88.6 1.3 100.0 0.0 11.5 0.900 1 572 0 74 88.6 1.3 100.0 0.0 11.5 1.000 0 572 0 75 88.4 0.0 100.0 . 11.6outroc=rocout/*generate the sensitivity and specifity output*/oddsratioHeat / at(Soak=1 2 3 4); /*Odds Ratios of Heat at Several Values of Soak */rsq/* generate generalized R Square measure for the model */lackfit; performs the Hosmer and Lemeshow goodness-of-fit test for the binary response model. The subjects are divided into approximately 10 groups of roughly the same size based on the percentiles of the estimated probabilities. The discrepancies between the observed and expected number of observations in these groups are summarized by the Pearson chi-square statistic, which is then compared to a chi-square distribution with t degrees of freedom, where t is the number of groups minus n. By default, n = 2. Asmall p-value suggests that the fitted model is not an adequate model. Hosmer and Lemeshow Goodness-of-Fit Test Chi-Square DF Pr > ChiSq 4.6781 80.7914weightsplit; /* if it's negative, missing or 0, then not used in the model*/ Output out=Data2predicted=p_hat/* output the predictive prob. of each obs */xbeta=xbeta; /*ouptut the value of all linear predictor x * beta, which is Y_hat*/ /*you can derive p_hat from xbeta by:p_hat=1/(1+exp(-xbeta))*/ Contrast statement: say a class variable has 4 levels, then 3 parameters for first 3 levels: b1,b2,b3 the last level as reference level:= -b1-b2-b3 compare the first with the last, b1=-b1-b2-b3 ==>2b1+b2+b3=0Contrast '1 vs 4' A 2 1 1;compare the 3r with the avg of first 2, b3=(b1+b2)/2 ==>-b1-b2+2b3=0Contrast '1&2 vs 3' A -1 -1 2;Contrast '1&2 vs 3&4' A 2 2 0;Contrast 'Main Effect' A 1 0 0, A 0 1 0, A 0 0 1;run; ods graphics off;