{smcl} {* 28feb2011}{...} {hline} help for {hi:grmarg}{right:- v001 28feb2011 jx } {hline} {title:Marginal effects and their differences from binary regressions} {p 8 15 2}{cmd:grmarg} [if] [in] [{cmd:,} {cmd:x(}{it:variables_and_values}{cmd:)} {cmd:rest(}{it:stat}{cmd:)} {cmdab:l:evel(}{it:#}{cmd:)} {cmdab:del:ta} {cmdab:boot:strap} {cmdab:r:eps(}{it:#}{cmd:)} {cmdab:si:ze(}{it:#}{cmd:)} {cmd:save} {cmd:diff} {cmdab:noba:se} {cmd:all match dots} {cmd:dydxmat(}{it:matrix_kxk}{cmd:)}] {p 4 4 2} where {it:variables_and_values} is an alternating list of variables and either numeric values or mean, median, min, max, upper, lower, previous and {it:stat} is either mean, median, min, max, upper, lower, previous, grmean(group mean), mrmedian, grmin, grmax. {title: Description} {p 4 4 2} {cmd:grmarg} is a command that returns marginal effects, differences in marginal effects and their confidence intervals using both {help bootstrap} and {help delta} methods {title: Options} {p 4 8 2} {cmd:x(}{it:variables_and_values}{cmd:)} sets the values of independent variables for calculating predicted values (marginal effects). The list must alternate variable names and either numeric values or types of {cmd:stat}. {p 4 8 2} {cmd:rest(}{it:stat}{cmd:)} sets the independent variables not specified in {cmd:x(}{it:variables_and_values}{cmd:)} to one of the types of {cmd:stat}. {p 4 8 2} {cmd:level()} sets the {help level} of the confidence interval for predicted values or probabilities for the commands for which these are provided. The default is 95. {p 4 8 2} {cmdab:del:ta} calculates confidence intervals by the delta method using analytical derivatives. This method works with cloglog, logistic, logit and probit. {p 4 8 2} {cmdab:boot:strap} computes confidence intervals using the bootstrap method. This method takes roughly 1,000 times longer to compute than other methods. This method works with cloglog, logistic, logit, and probit. {p 4 8 2} {cmd:reps(}{it:#}{cmd:)} specifies the number of bootstrap replications to be performed. The default is 1000. {p 4 8 2} {cmd: size(}{it:#}{cmd:)} specifies the size of the samples to be drawn. The default is e(N), the same size as the estimation sample. {p 4 8 2} {cmd:save} saves current values of independent variables and predictions for computing changes using the diff option. {p 4 8 2} {cmd:diff} computes difference between current predictions and those that were saved. {p 4 8 2} {cmd:nobase} suppresses inclusion of the base values of x in the output. {p 4 8 2} {cmd:all} specifies that any calculation of means, medians, etc., should use the entire sample instead of the sample used to estimate the model. {p 4 8 2} {cmd:match} requests {cmd:grmarg} to resample from each category group of the dependent variable in proportion of the resample size to the original sample size. {p 4 8 2} {cmd:dots} requests a dot be placed on the screen at the beginning of each replication, thus providing entertainment when a large number of reps() are requested. It also prints out the percent replications finished. {p 4 8 2} {cmd:dydxmat(}{it:matrix_kxk}{cmd:)} supplies a matrix by users of dy/dx or dxb/dx, where there is polynomial or interaction terms. {title:Examples} {p 4 4 2} To compute the predicted marginal effects, differences in marginal effects, and their confidence intervals using the delta method for a logit model. All independent variables are held at their means except for black and education specifed in x(). {p 4 8 2}{cmd:.logit vote black educ income} {p 4 8 2}{cmd:.grmarg, x(black=1 educ=16) save} {p 4 8 2}{cmd:.grmarg, x(black=0 educ=16) diff} {p 4 8 2} ::: {hline} {p 2 4 2}Authors: Jun Xu{p_end}