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....imp=clay.knots, abslat.imp=abslat.knots ) ,method = "REML" ,weights = 1/nsame ), error=function(e) e, finally=doyee) if(inherits(r.ints, "error")) {r.ints=doyee; print("an error happened but it got handled.")} -- *Andrew Crane-Droesch* Energy and Resources Group UC Berkeley +1 215 435 2644 andrewcd@berkeley.edu skype: andrew.crane-droesch http://andrewcd.berkeley.edu [[alternative HTML version deleted]]

...cient on T to the smooth function of T*var2, and also adds their standard errors. Of course, I'd like these to be the standard errors estimated by the "sandwich" estimator, above. Thanks for any help and advice, and for bearing with my long post. Best, Andrew -- *Andrew Crane-Droesch* Energy and Resources Group UC Berkeley +1 215 435 2644 andrewcd@berkeley.edu skype: andrew.crane-droesch http://andrewcd.berkeley.edu [[alternative HTML version deleted]]

Hi All, I'm a little stumped by the following problem. I've got a dataset with the following structure: idxy ix iy country (other variables) 1 1 1 c1 x1 2 1 2 c1 x2 3 1 3 c1 x3 . . . . . 3739 55 67 c7 x3739 3740 55 68 c7 x3740 where ix and

Hi All, I'm running into a problem with GAM (in the MGCV package). When I try to estimate the model, I get the following error message: 1> fit <- gam(ndvi~s(rain)+s(temp)+s(rainl1)+s(rainl2)+s(rainxY)+s(rainl1xY)+s(rainl2xY)+s(tempxY), data=dsub, weights=wvec) Error in while (mean(ldxx/(ldxx + ldss)) > 0.4) { : missing value where TRUE/FALSE needed Using

Hi All, I'm having trouble with the colors on my screen getting translated to the colors in the outputted .pdf document. Here is a caricature of my problem: par(mfrow=c(1,1)) x1 = rnorm(1000) x2 = rnorm(1000)+10 y1 = rnorm(1000)+10 y2 = rnorm(1000)+10 palette(rainbow(6)) plot(x=x1,y=y1,col=y1,xlim=c(-10,20)) palette(heat.colors(6)) points(x=x2,y=y2,col=y2) dev.copy2pdf(file =

Dear List, I'm using GAMs in a multiple imputation project, and I want to be able to combine the parameter estimates and covariance matrices from each completed dataset's fitted model in the end. In order to do this, I need the knots to be uniform for each model with partially-imputed data. I want to specify these knots based on the quantiles of the unique values of the non-missing

Dear List, I've just tried to specify a GAM without an intercept -- I've got one of the (rare) cases where it is appropriate for E(y) -> 0 as X ->0. Naively running a GAM with the "-1" appended to the formula and the calling "predict.gam", I see that the model isn't behaving as expected. I don't understand why this would be. Google turns up this old

Dear List, I'm getting an error in mgcv, and I can't figure out where it comes from. The setup is the following: I've got a fitted GAM object called "MI", and a vector of "prediction data" (with default values for predictors). I feed this into predict.gam(object, newdata = whatever) via the following function: makepred = function(varstochange,val){ for

Dear List, I want to use transparency in R to represent downweighting of observations based on clusters (repeated observations in a dataset). Some clusters will have identical covariate values in a parameter space -- in the 2D x,y case, these represent a bunch of semi-tranparent dots in the same place. I'd like these overlapping dots to be completely opaque. In other cases, the

I'm taking the residual-regression approach to semiparametric estimation (Robinson 1988, Econometrica), and basically using SemiPar simply as a convenient means of doing multivariate nonparamteric additive models. The final bit of code is here: finalfit <- spm(res~f(V3,basis="trunc.poly")+f(V5,basis="trunc.poly")+f(V6,basis="trunc.poly")) summary(finalfit)

Dear list, I'm just getting started learning how to use remote supercomputers for execution of parallelized code. I got a lot of initial help from this <http://stackoverflow.com/questions/14553357/parallelizing-on-a-supercomputer-and-then-combining-the-parallel-results-r> previous post, as well as one particularly helpful and patient XSEDE guy. I'm only using one node (for the

I've got a complicated semi-parametric model that I'm fitting with mgcv. I start with a model based on theory. Its got lots of interaction terms. I want to winnow it down: removing each interaction term or un-interacted main effect one by one, checking the AIC, and retaining the model that gives me the lowest AIC. I then want to repeat the procedure on the retained model. Here is