On Sun, 8 Nov 2009, Dorian Litvine wrote: Following is a script I used in an introductory econometrics course; you can adapt it to your case. In this case KL6 (number of children less than 6 years old) is a variable that takes on values 0, 1, 2 and 3 in the dataset, and we're showing the nonlinear relationship between the value of KL6 and the probability that the dependent variable (LFP, labor force participation for a given woman) equals 1. <script> open mroz87.gdt -q # list of independent variables list Xlist = KL6 WA WE MTR # run probit probit LFP 0 Xlist # set variables other than KL6 to their sample means matrix Xrow = { 1, 0, mean(WA), mean(WE), mean(MTR) } # generate 4 rows matrix X = Xrow | Xrow | Xrow | Xrow # set KL6 values from 0 to 3 matrix Kcol = { 0, 1, 2, 3 }' X[,2] = Kcol # check X print X # compute Phi(X\beta) matrix P = Kcol ~ cnorm(X*$coeff) # give names to the columns of P (optional) colnames(P, "KL6 P(LFP=1)") print P # and make a graph g1 <- gnuplot 2 1 --matrix=P --suppress --with-lines g1.show </script> This is a probit example, so we use the cnorm() function (cumulative normal distribution). Gretl CVS/snapshot has a similar function, logistic(), for the logistic CDF, but if you don't have the snapshot you can write it yourself: function matrix my_logistic (matrix x) scalar m = rows(x) scalar n = cols(x) matrix ret = x loop i=1..m -q loop j=1..n -q ret[i,j] = 1 / (1 + exp(-x[i,j])) endloop endloop return ret end function Allin Cottrell