similar to: Fwd: Re: residual checking for GAM (mgcv)

Dear list, i'm checking the residuals plots of a gam model after a processus of model selection. I found the "best" model, all my terms are significant, the r-square and the deviance explained are good, but I have strange residuals plots: http://dl.dropbox.com/u/1169100/gam.check.png http://dl.dropbox.com/u/1169100/residuals_vs_fitted.png What does explains the "curve"

mgcv 1.4 is now on CRAN. It includes new features to allow mgcv::gam to fit almost any (quadratically) penalized GLM, plus some extra smoother classes. New gam features ------------------------- * Linear functionals of smooths can be included in the gam linear predictor, allowing, e.g., functional generalized linear models/signal regression, smooths of interval data, etc. * The parametric

mgcv 1.4 is now on CRAN. It includes new features to allow mgcv::gam to fit almost any (quadratically) penalized GLM, plus some extra smoother classes. New gam features ------------------------- * Linear functionals of smooths can be included in the gam linear predictor, allowing, e.g., functional generalized linear models/signal regression, smooths of interval data, etc. * The parametric

mgcv 1.5-0 is now on CRAN. Main changes are: * REML and ML smoothness selection are now available. * A Tweedie family has been added. * `gam.method' has been replaced (see arguments `method' and `optimizer' for `gam') For other changes see the changeLog. Simon -- > Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK > +44 1225 386603

mgcv 1.5-0 is now on CRAN. Main changes are: * REML and ML smoothness selection are now available. * A Tweedie family has been added. * `gam.method' has been replaced (see arguments `method' and `optimizer' for `gam') For other changes see the changeLog. Simon -- > Simon Wood, Mathematical Sciences, University of Bath, Bath, BA2 7AY UK > +44 1225 386603

re: Cubic splines in package "mgcv" I don't have access to Gu (2002) but clearly the function R(x,z) defined on p126 of Simon Wood's book is piecewise quartic, not piecewise cubic. Like Kunio Takezawa (below) I was puzzled by the word "cubic" on p126. As Simon Wood writes, this basis is not actually used by mgcv when specifying bs="cr". Maybe the point is

Dear users, I am trying to show the equation (including coefficients from the model estimates) for a gam model but do not understand how to. Slide 7 from one of the authors presentations (gam-theory.pdf URL: http://people.bath.ac.uk/sw283/mgcv/) shows a general equation log{E(yi )} = ?+ ?xi + f (zi ) . What I would like to do is put my model coefficients and present the equation used. I am an

x <- NA na.act <- na.action(na.exclude(x)) y <- rep(0,0) naresid(na.act,y) ... currently produces the result... numeric(0) ... whereas the documentation might lead you to expect NA The behaviour is caused by the line if (length(x) == 0L) return(x) in `stats:::naresid.exclude'. Removing this line results in the behaviour I'd expected in the above example (and in a

Hi Greg, For quasi families I've used extended quasi-likelihood (see Mccullagh and Nelder, Generalized Linear Models 2nd ed, section 9.6) in place of the likelihood/quasi-likelihood in the expression for the (RE)ML score. I hadn't realised that this was possible before the paper was published. best, Simon ps. sorry for slow reply, the original message slipped through my filter for

Hi, According to the help file for model.frame/get_all_vars, the following should produce the same output from both functions, but it doesn't... > dat <- list(X=matrix(1:15,5,3),z=26:30) > model.frame(~z+X,dat) z X.1 X.2 X.3 1 26 1 6 11 2 27 2 7 12 3 28 3 8 13 4 29 4 9 14 5 30 5 10 15 > get_all_vars(~z+X,dat) [1] z X <NA> <NA> <0

Hi, Currently the `gaussian' family's initialization code signals an error if any response data are zero or negative and a log link is used. Given that zero or negative response data are perfectly legitimate under the GLM fitted using `gaussian("log")', this seems a bit unsatisfactory. Might it be worth changing it? The current offending code from `gaussian' is:

Dear list, does anybody know whether there is a way to easily retrieve the so called "partial residuals" of a gam fit with package mgcv? The partial residuals are the residuals you would get if you would "leave out" a particular predictor and are the dots in the plots created by plot(gam.object,residuals=TRUE) residuals.gam() gives me whole model residuals and

Sometimes, for specific models, I get this error from predict.gam in library mgcv: Error in complete.cases(object) : negative length vectors are not allowed Here's an example: model.calibrate <- gam(meansalesw ~ s(tscore,bs="cs",k=4), data=toplot, weights=weight, gam.method="perf.magic") > test <- predict(model.calibrate,newdata) Error in

Dear all, I run the GAMs (generalized additive models) in gam(mgcv) using the following codes. m.gam <-gam(mark~s(x)+s(y)+s(lstday2004)+s(ndvi2004)+s(slope)+s(elevation)+disbinary,family=binomial(logit),data=point) And two repeated warnings appeared. Warnings$B!'(B 1: In gam.fit(G, family = G$family, control = control, gamma = gamma, ... : Algorithm did not converge 2: In gam.fit(G,

In Dr. Wood's book on GAM, he suggests in section 4.1.6 that it might be useful to shrink a single smooth by adding S=S+epsilon*I to the penalty matrix S. The context was the need to be able to shrink the term to zero if appropriate. I'd like to do this in order to shrink the coefficients towards zero (irrespective of the penalty for "wiggliness") - but not necessarily all the

I would like to fit a logistic regression using a smothing spline, where the spline is a piecewise cubic polynomial. Is the knots option used to define the subintervals for each piece of the cubic spline? If yes and there are k knots, then why does the coefficients field in the returned object from gam only list k coefficients? Shouldn't there be 4k -4 coefficients? Sincerely, Bill

Please help! Im trying to run a GAM: model3=gam(data2$Symptoms~as.factor(data2$txerad)+s(data2$maritalStatus),family=binomial,data=data2) But keep getting this error: Error in dl[[i]] : subscript out of bounds Can someone please tell me what this error is? Thanks -- View this message in context: http://r.789695.n4.nabble.com/Help-with-GAM-mgcv-tp3074165p3074165.html Sent from the R help

This is in regards to the suggested use of type="lpmatrix" in the documentation for mgcv::predict.gam. Could one not get the same result more simply by using type="terms" and interpolating each term directly? What is the advantage of the lpmatrix approach for prediction outside R? Thanks. -- View this message in context:

Hi there, I'm using presence-absence data in a gam (i.e. 0 or 1 as values) I am trying to run a gam with 'dummy covariates' i.e. 1~1 unfortunately my model: * model<-gam(1~1, data=bats, family=negbin)* keeps putting out: * Error in gam(1 ~ 1, data = bats, family = negbin) : Not enough (non-NA) data to do anything meaningful* Is there a specific reason it would do this? I have

Dear listers, I'm using gam(from mgcv) for semi-parametric regression on small and noisy datasets(10 to 200 observations), and facing a problem of overfitting. According to the book(Simon N. Wood / Generalized Additive Models: An Introduction with R), it is suggested to avoid overfitting by inflating the effective degrees of freedom in GCV evaluation with increased "gamma"