1/6/2024 0 Comments Risk probability xbeta![]() The ILINK option adds estimates of the Style level probabilities for each School. The LSMEANS statement provides estimates of the log odds for each School. The ORDER=DATA response variable option orders the Styles as they first appear in the data so that the logits are log(p self/p class) and log(p team/p class). The following statements fit a generalized logit model to the multinomial response, Style. ![]() Using PROC LOGISTIC and the NLMeans macro Input School Program $ Style $ Count regular self 10 1 regular team 17 1 regular class 26ġ afternoon self 5 1 afternoon team 12 1 afternoon class 50Ģ regular self 21 2 regular team 17 2 regular class 26Ģ afternoon self 16 2 afternoon team 12 2 afternoon class 36ģ regular self 15 3 regular team 15 3 regular class 16ģ afternoon self 12 3 afternoon team 12 3 afternoon class 20 There are three response levels (Styles) and three populations (Schools). The data below are presented in the example titled "Nominal Response Data: Generalized Logits Model" in the PROC LOGISTIC documentation comparing styles of instruction at several schools. PROC CATMOD can also be used by fitting a model to the log probabilities rather than logits. These nonlinear functions can be estimated using the NLMeans macro, the NLEST/NLEstimate macro, or by fitting the model and doing the estimation in PROC NLMIXED. In the multinomial case, relative risk estimates are nonlinear functions of the parameters in a generalized logit model, which can be fit using PROC LOGISTIC. When the response is binary, you can obtain relative risk estimates as discussed in this note. Estimating the relative risk comparing two populations on the probability of one response level In LOGISTIC, and most other procedures, generalized logits are requested by the LINK=GLOGIT option in the MODEL statement. For repeated measures data with a multinomial response, use PROC SURVEYLOGISTIC with a CLUSTER statement. As discussed in this note, the generalized logit model can also be fit in other procedures such as GLIMMIX, HPGENSELECT, FMM, CATMOD, and SURVEYLOGISTIC. In the following examples, a generalized logit model is fit to the nominal, multinomial response. You might want to compare two populations with respect to an individual response level probability (P(Y= i|X= j)/P(Y= i|X= k)), or you might want to compare response level probabilities in a given population (P(Y= i|X= j)/P(Y= k|X= j). There are two types of relative risks that might be of interest when modeling a multinomial response.
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