Even when I try to predict y values from x, let's say I want to predict y at x=0. Looking at the graph from the provided syntax, I would expect y to be about 0.85. Is this right:
predict(mylogit,newdata=as.data.frame(0),type="response") # I get: Warning message: 'newdata' had 1 rows but variable(s) found have 34 rows # Why do I need 34 rows? So I try: v=vector() v[1:34]=0 predict(mylogit,newdata=as.data.frame(v),type="response") # And I get a matrix of 34 values that appear to fit a logistic function, instead of 0.85.. On Wed, Jan 26, 2011 at 6:59 PM, David Winsemius <dwinsem...@comcast.net>wrote: > > On Jan 26, 2011, at 10:52 PM, Ahnate Lim wrote: > > Dear R-help, >> >> I have fitted a glm logistic function to dichotomous forced choices >> responses varying according to time interval between two stimulus. x >> values >> are time separation in miliseconds, and the y values are proportion >> responses for one of the stimulus. Now I am trying to extrapolate x values >> for the y value (proportion) at .25, .5, and .75. I have tried several >> predict parameters, and they don't appear to be working. Is this correct >> use >> and understanding of the predict function? It would be nice to know the >> parameters for the glm best fit, but all I really need are the >> extrapolated >> x values for those proportions. Thank you for your help. Here is the code: >> >> x <- >> c(-283.9, -267.2, -250.5, -233.8, -217.1, -200.4, -183.7, -167, >> -150.3, -133.6, -116.9, -100.2, -83.5, -66.8, -50.1, -33.4, -16.7, >> 16.7, 33.4, 50.1, 66.8, 83.5, 100.2, 116.9, 133.6, 150.3, 167, >> 183.7, 200.4, 217.1, 233.8, 250.5, 267.2, 283.9) >> >> y <- >> c(0, 0.333333333333333, 0, 0, 0, 0, 0, 0, 0, 0.333333333333333, >> 0, 0.133333333333333, 0.238095238095238, 0.527777777777778, >> 0.566666666666667, >> 0.845238095238095, 0.55, 1, 0.888888888888889, 1, 1, 1, 1, 1, >> 1, 1, 1, 1, 1, 1, 1, 1, 1, 0.5) >> >> weight <- >> c(1, 3, 2, 5, 4, 4, 3, 5, 5, 4, 5, 11, 22, 11, 15, 16, 11, 7, >> 14, 10, 16, 19, 11, 5, 4, 5, 6, 9, 4, 2, 5, 5, 2, 2) >> >> mylogit <- glm(y~x,weights=weight, family = binomial) >> >> # now I try plotting the predicted value, and it looks like a good fit, >> hopefully I can access what the glm is doing >> >> ypred <- predict(mylogit,newdata=as.data.frame(x),type="response") >> plot(x, ypred,type="l") >> points(x,y) >> >> # so I try to predict the x value when y (proportion) is at .5, but >> something is wrong.. >> > > Right. Predict goes in the other direction ... x predicts y. > > Perhaps if you created a function of y w.r.t. x and then inverted it. > > ?approxfun # and see the posting earlier this week "Inverse Prediction > with splines" where it was demonstrated how to reverse the roles of x and y. > >> >> predict(mylogit,newdata=as.data.frame(0.5)) >> >> [[alternative HTML version deleted]] >> >> ______________________________________________ >> R-help@r-project.org mailing list >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide >> http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. >> > > David Winsemius, MD > West Hartford, CT > > [[alternative HTML version deleted]] ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
