search for: l_yx

Hello, I'd like to understand 'what' is predicting the response for library(mgcv) gam? For example: library(mgcv) fit <- gam(y~s(x),data=as.data.frame(l_yx),family=binomial) xx <- seq(min(l_yx[,2]),max(l_yx[,2]),len=101) plot(xx,predict(fit,data.frame(x=xx),type="response"),type="l") I want to see the generalized function(s) used to predict the response that is plotted above. In other words, f(x) = {[what?]}. I'm new to gam...

Hello, How do I plot a gam fit object on probability (Y axis) vs raw values (X axis) axis and include the confidence plot lines? Details... I'm using the gam function like this: l_yx[,2] = log(l_yx[,2] + .0004) fit <- gam(y~s(x),data=as.data.frame(l_yx),family=binomial) And I want to plot it so that probability is on the Y axis and values are on the X axis (i.e. I don't want log likelihood on the Y axis or the log of my values on my X axis): xx <- seq(min(l_yx[,2]),...

...Y=1 (response=1) or the other way around). I don't feel my data has this issue. Please point out how it does! The list post instructions state that I can attach pdf's, so I attached plots of my data right before I do the call to glm. The attachments are plots of my data stored in variable l_yx (as can be seen in the axis names): My response (vertical axis) by row index (horizontal axis): plot(l_yx[,1],type='h') My predictor variable (vertical axis) by row index index (horizontal axis): plot(l_yx[,2],type='h') So here is more info on my data frame/data (in case you can...

Hello, I don't understand what went wrong or how to fix this. How do I set qr=TRUE for gam? When I produce a fit using gam like this: fit = gam(y~s(x),data=as.data.frame(l_yx),family=family,control = list(keepData=T)) ...then try to use predict: (see #1 below in the traceback() ) > traceback() 6: stop("lm object does not have a proper 'qr' component.\n Rank zero or should not have used lm(.., qr=FALSE).") at #81 5: qr.lm(object) at #81 4: summary....