Sorry if this is an obvious question...
I'm estimating a simple binomial generalized additive model using the gam function in the package mgcv. The model makes sense given my data, and the predicted values also make sense given what I know about the data. However, I'm having trouble interpreting the y-axis of the plot of the gam object. The y-axis is labeled "s(x,2.52)" which I understand to basically mean a smoothing estimator with approximately 2.52 degrees of freedom. The y-axis in my case ranges from -2 to 6 and I thought that it would be possible to convert the Y axis estimate to a probability via exp(Y)/(1+exp(Y)). So for instance, my lowest y-axis estimate is -2 for a probability of: > exp(-2)/(1+exp(-2)) [1] 0.1192029 However, if I use the predict function my lowest estimate is -3.53862893 for a probability of 2.8%. The 2.8% estimate is a much better estimate than 11.9% given my specific data, so I'm clearly not interpreting the plot correctly. The help files say plot.gam provides "the component smooth functions that make it up, on the scale of the linear predictor." I'm just not sure what that description means. Does someone have another description that might help me grasp the plot? Similar plots are on page 286 of Venables and Ripley (3rd Edition)... Thanks, Paul [[alternative HTML version deleted]] ______________________________________________ R-help@stat.math.ethz.ch mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html