Thank you for the clarification, this makes sense now! dose.p from MASS also does the job perfectly, in returning x values for specified proportions. I'm curious, if I use the other parameter in predict.glm type="link" (instead of type="response"), in the case of a logistic binomial, what does this predict?
On Wed, Jan 26, 2011 at 11:41 PM, Gavin Simpson <gavin.simp...@ucl.ac.uk>wrote: > On Wed, 2011-01-26 at 19:25 -1000, Ahnate Lim wrote: > > 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") > > Your original problem was the use of `newdata = as.data.frame(0.5)`. > There are two problems here: i) if you don't name the input (x = 0.5, > say) then you get a data frame with the name(s) "0.5": > > > as.data.frame(0.5) > 0.5 > 1 0.5 > > and ii) if you do name it, you still get a data frame with name(s) "0.5" > > > as.data.frame(x = 0.5) > 0.5 > 1 0.5 > > In both cases, predict wants to find a variable with the name `x` in the > object supplied to `newdata`. It finds `x` but your `x` in the global > workspace, but warns because it knows that `newdata` was a data frame > with a single row - so there was a mismatch and you likely made a > mistake. > > In these cases, `data.frame()` is preferred to `as.data.frame()`: > > predict(mylogit, newdata = data.frame(x = 0), type = "response") > > or we can use a list, to save a few characters: > > predict(mylogit, newdata = list(x = 0), type = "response") > > which give: > > > predict(mylogit, newdata = list(x = 0), type = "response") > 1 > 0.813069 > > predict(mylogit, newdata = data.frame(x = 0), type = "response") > 1 > 0.813069 > > In summary, use `data.frame()` or `list()` to create the object passed > as `newdata` and make sure you give the component containing the new > values a *name* that matches the predictor in the model formula. > > HTH > > G > > > > > # 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. > > -- > %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% > Dr. Gavin Simpson [t] +44 (0)20 7679 0522 > ECRC, UCL Geography, [f] +44 (0)20 7679 0565 > Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk > Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ > UK. WC1E 6BT. [w] http://www.freshwaters.org.uk > %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% > > [[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.
