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The PROBIT Procedure |
The PROBIT procedure calculates maximum likelihood estimates of regression parameters and the natural (or threshold) response rate for quantal response data from biological assays or other discrete event data. This includes probit, logit, ordinal logistic, and extreme value (or gompit) regression models. Probit analysis developed from the need to analyze qualitative (dichotomous or polytomous) dependent variables within the regression framework. Many response variables are binary by nature (yes/no), while others are measured ordinally rather than continuously (degree of severity). Ordinary least squares (OLS) regression has been shown to be inadequate when the dependent variable is discrete (Collett, 1991 and Agresti, 1990). Probit or logit analyses are more appropriate in this case.
The PROBIT procedure computes maximum likelihood estimates of the parameters and C of the probit equation using a modified Newton-Raphson algorithm. When the response Y is binary, with values 0 and 1, the probit equation is
where
You can set or estimate the natural (threshold) response rate C. Estimation of C can begin either from an initial value that you specify or from the rate observed in a control group. By default, the natural response rate is fixed at zero. An observation in the data set analyzed by the PROBIT procedure may contain the response and explanatory values for one subject. Alternatively, it may provide the number of observed events from a number of subjects at a particular setting of the explanatory variables. In this case, PROC PROBIT models the probability of an event.
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