.- help for ^relogit^ .- Logit with corrected coefficients --------------------------------- ^relogit^ depvar [indepvars] [weight] [^if^ exp] [^in^ range] [^, wc(^#^) pc(^#|# #^)^ ^nomcn cl^uster^(^varname^) nor^obust ^nocon^stant ^l^evel^(^#^) ] ^fweight^s, ^pweight^s, ^aweight^s, and ^pweight^s are allowed. See help @weight@ Description ----------- ^relogit^ implements the procedures suggested in King and Zeng (1999a,b) for generating approximately unbiased and lower-variance estimates of logit coefficients and their variance-covariance matrix by correcting for small samples and rare events. This procedure also allows for selection on the dependent variable as in case-control studies. After running ^relogit^, use @setx@ and @relogitq@ to compute quantities of interest such as absolute risks (probabilities), relative risks, and attributable risks (first differences). Options ------- ^pc(^#|# #^)^ corrects for selection on the dependent variable by using the method of prior correction. This option requires a numeric argument, the proportion of 1's in the population, which should be between 0 and 1, exclusive. If the true proportion of 1's is known only to fall within some range, the pc option will accept the upper and lower bounds of the range. For instance, pc(.2 .4) indicates that the true proporation lies in the interval (.2 .4). For a discussion of how relogit would interpret pc(0), pc(1) or pc(0 1), see documentation for the wc() option. ^wc(^#^)^ corrects for selection on the dependent variable (case-control designs) by weighting the sample, such that the weighted proportion of 1s and 0s in the sample equals the true proportion in the population. The weight-correction option requires a numeric argument, the proportion of 1s in the population, which should be between 0 and 1 exclusive. If you type wc(0), relogit will presume that you intended a very small positive number, since the true proportion of 1's in the population cannot be 0. Likewise, if you type wc(1) the program will presume you intended a number just shy of 1. ^wc()^ and ^pc()^ cannot be specified simultaneously. ^nomcn^ suppresses the MCN correction for biases arising from small samples. By default ^relogit^ uses a finite sample correction developed by McCullagh and Nelder and extended to simultaneous correction for selection on Y by King and Zeng. ^cl^uster^(^varname^)^ specifies that the observations are independent across groups (clusters) but not necessarily independent within groups. varname indicates to which group each observation belongs. Specifying ^cluster()^ implies robust. Thus, the ^cluster()^ option cannot be used in conjunciton with the ^norobust^ option. ^nor^obust specifies that the traditional variance calculation be used in place of the Huber/White/sandwich estimator. By default, ^relogit^ calculates robust variance estimates. Traditional variance calculations do not make sense with ^wc()^ specified. ^nocon^stant suppresses the constant term. This option cannot be used in conjunction with ^pc()^ ^l^evel^(^#^)^ specifies the confidence level, in percent, for the confidence intervals of the coefficients. See help @level@. Examples -------- To correct for small sample and rare events bias in a logit model where the dependent variable is y and the explanatory variables are x1 and x2 (and the sampling design is random or conditional on x), type . ^relogit y x1 x2^ To correct for small sample and rare events bias, and use the method of prior correction to correct for a case-control sampling design assuming that the true proportion of 1's falls in the interval [.6,.7], type . ^relogit y x1 x2, pc(.6 .7)^ To correct for small sample and rare events bias, and use the weighting procedure to correct for a case-control sampling design, the population fraction of 1s is 0.2, type . ^relogit y x1 x2, wc(.2)^ To run a traditional logit use . ^relogit y x1 x2, nomcn norobust^ which is equivalent to Stata's ^logit y x1 x2^ command. Distribution ------------ ^relogit^ is (C) Copyright, 1999, Michael Tomz, Gary King and Langche Zeng, All Rights Reserved. You may copy and distribute this program provided no charge is made and the copy is identical to the original. To request an exception, please contact: Michael Tomz Department of Government, Harvard University Littauer Center North Yard Cambridge, MA 02138 Please distribute the current version of this program, which is available at http://GKing.Harvard.Edu. References ---------- Gary King and Langche Zeng. 1999a. "Logistic Regression in Rare Events Data," Department of Government, Harvard University, available from http://GKing.Harvard.Edu. Gary King and Langche Zeng. 1999b. "Estimating Absolute, Relative, and Attributable Risks in Case-Control Studies," Department of Government, Harvard University, available from http://GKing.Harvard.Edu.