Hi Ted, So here's what I'm doing: This is my call to predict.glm:
> pY <- predict.glm(from69.fin.glm, newdata=d.tab, type="response") This is what the fitted glm object looks like: > from69.fin.glm Call: glm(formula = TR ~ z1 + e12_div_p_n + z2 + p_n, data = j2.tab) Coefficients: (Intercept) z1 e12_div_p_n z2 p_n 0.0462932 0.0063221 -0.0202138 0.0063221 0.0004168 Degrees of Freedom: 137 Total (i.e. Null); 133 Residual Null Deviance: 34.32 Residual Deviance: 21.93 AIC: 149.8 This is an example of what the data file looks like TR s_n p_n z1 z2 z1_div_s_n z2_div_s_n z1_div_p_n z2_div_p_n e1 e2 e1_div_s_n e2_div_s_n e1_div_p_n e2_div_p_n e12 e12_div_s_n e12_div_p_n 0 169.000 167.141 8.800 3.800 0.052 0.022 0.053 0.023 -2295.000 -4007.000 -13.580 -23.710 -13.731 -23.974 0.000 0.000 0.000 1 615.500 615.352 29.700 21.800 0.048 0.035 0.048 0.035 -5344.000 -4248.000 -8.682 -6.902 -8.684 -6.903 141.740 0.230 0.230 0 409.500 388.149 5.400 19.000 0.013 0.046 0.014 0.049 -6328.000 -4597.000 -15.453 -11.226 -16.303 -11.843 1069.890 2.613 2.756 0 782.500 776.276 26.100 28.800 0.033 0.037 0.034 0.037 -1279.000 1260.000 -1.635 1.610 -1.648 1.623 67.500 0.086 0.087 1 355.500 355.117 28.800 32.400 0.081 0.091 0.081 0.091 -10600.000 -9670.000 -29.817 -27.201 -29.849 -27.230 418.560 1.177 1.179 0 184.500 164.012 4.900 9.500 0.027 0.051 0.030 0.058 -4519.000 -1901.000 -24.493 -10.304 -27.553 -11.591 -963.600 -5.223 -5.875 Thanks, rohit On Mon, 31 Oct 2005 [EMAIL PROTECTED] wrote: > On 31-Oct-05 Rohit Singh wrote: > > Hi, > > > > This is a newbie question. I have been using glm to perform some > > logistic regression. However, if I take the fitted parameters (as > > part of the glm object) and pass them on the glm.predict function, > > for some test cases I am getting predicted values that are a little > > over 1. This is a bit puzzling for me, because my understanding > > was that these numbers are probabilities and so should be between > > 0 and 1. > > > > Thanks a lot! I'd appreciate any help you could provide. > > > > -rohit > > Indeed this should not happen, and probably there is some mistake > in the way you use the predict function (which requires a little > care). > > However, it's not possible to point-point what is happening > without seeing a specific case. Can you post an example of the > code you use when this happens? And, if feasible, also an example > of data. > > Best wishes, > Ted. > > > -------------------------------------------------------------------- > E-Mail: (Ted Harding) <[EMAIL PROTECTED]> > Fax-to-email: +44 (0)870 094 0861 > Date: 31-Oct-05 Time: 23:00:39 > ------------------------------ XFMail ------------------------------ > ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
