similar to: prediction, gam, mgcv

Hi all! I?ve got some problems with the function gam (library mgcv). For some models I get the error message : Error: no valid set of coefficients has been found:please supply starting values In addition: Warning message: NaNs produced in: log(x) This is a shortened code I used: gam(y ~ M1 + M3 + M4 + M5 + M6 + sex + M1*M3 + s(age), family=Gamma(link ="identity"), weights=days) If

How can I obtain the values of confidence intervals from gam anf glm objects? Thanks in advance -- David Nogu?s Bravo Functional Ecology and Biodiversity Department Pyrenean Institute of Ecology Spanish Research Council Av. Monta?ana 1005 Zaragoza - CP 50059 976716030 - 976716019 (fax)

Dear all, I'm a fairly new R user having two questions regarding gam: 1. The prediction example on p. 38 in the mgcv manual. In order to get predictions based on the original data set, by leaving out the 'newdata' argument ("newd" in the example), I get an error message "Warning message: the condition has length > 1 and only the first element will be used in: if

Hola! I have some problems with gam in mgcv. Firts a detail: it would be nice igf gam would accept an na.action argument, but that not the main point. I want to have a smooth term for time over a year, the same pattern repeating in succesive years. It would be natural then to impose the condition s(0)=s(12). Is this possible within mgcv? I tried to obtain this with trigonometric terms, aca:

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:

Dear R-users: Im trying (using gam package) to develop a stepwise analysis. My gam object contains five pedictor variables (a,b,c,d,e,f). I define the step.gam: step.gam(gamobject, scope=list("a"= ~s(a,4), "b"= ~s(b,4), "c"= ~s(c,4), "d"= ~s(d,4), "e"= ~s(e,4), "f"= ~s(f,4))) However, the result shows a formula containing the whole

I fitted a GAM model with Poisson distribution to a data with about 200 observations. I noticed that the plot of the residuals versus fitted values show a trend. Residuals tend to be lower for higher fitted values. Because, I'm dealing with count data, I'm thinking that this might be due to overdispersion. Is there a way to account for overdispersion in any of the packages MGCV or GAM?

Hi, I've got the R library "mgcv" for GAM written by Simon Wood which works well in many instances. However, over the years I got attached to the S+ implementation of GAM which allows loess smoothing in more than 1 dimension as well as spline smoothing. Has anyone ported the S+ GAM library to R? Regards, Doug Beare. Fisheries Research Services, Marine Laboratory, Victoria Road,

hi, i'm working with mgcv packages and specially gam. My exemple is: >test<-gam(B~s(pred1)+s(pred2)) >plot(test,pages=1) when ploting test, you can view pred1 vs s(pred1, edf[1] ) & pred2 vs s(pred2, edf[2] ) I would like to know if there is a way to access to those terms (s(pred1) & s(pred2)). Does someone know how? the purpose is to access to equation of smooths terms

Get some question about the function "gam". Suppose I have a semiparametric model, Y~x1+x2+s(z1). Using "gam", how could I get the estimates for the parametric part and nonparametric part respectively? And another question: we could find the coefficients for both parametric term and nonparametric term, what do these coefficients for the nonparametric term stand for, the

hello, I am looking for a step() function for GAM's. In the book Statistical Computing by Crawley and a removal of predictors has been done "by hand" model <- gam(y ~s(x1) +s(x2) + s(x3)) summary(model) model2 <- gam(y ~s(x2) + s(x3)) # removal of the unsignificant variable #then comparing these two models if an significant increase occurs. anova(model, model2,

I have a question about how to compare a GLM with a GAM model using anova function. A GLM is performed for example: model1 <-glm(formula = exitus ~ age+gender+diabetes, family = "binomial", na.action = na.exclude) A second nested model could be: model2 <-glm(formula = exitus ~ age+gender, family = "binomial", na.action = na.exclude) To compare these two GLM

Hi, Is there any way to change the y axis range of values in a plot.gam()? I need that two different GAM plots to be of the same scale. Also, it is possible to change the labels? I tried with "ylab" and "ylim" and did not work Thanks in advance Ricardo Lopes Ricardo Lopes ............................................. Instituto do Mar Departamento de Zoologia

All, Sorry for bombarding you with GAM related questions, but... I know a partial residual option in plot.gam() is on Simon Wood's todo list, but since I'm in the midst of a project and not yet having acquired sufficient R knowledge to code something usable myself I'll have to put my trust in you. Anybody got some code lying around for doing this? Or if someone can supply me with

hello to everybody: I would to know with ranges of GCV or UBRE values can be considered as adequate to consider a GAM as correct Thanks in advance -- David Nogu?s Bravo Functional Ecology and Biodiversity Department Pyrenean Institute of Ecology Spanish Research Council Av. Monta?ana 1005 Zaragoza - CP 50059 976716030 - 976716019 (fax)

The documentation for predict.gam in library mgcv gives an example of using an "lpmatrix" to do approximate prediction via interpolation. However, the code is specific to the example wrt the number of smooth terms, df's for each,etc. (which is entirely appropriate for an example) Has anyone generalized this to directly generate code from a gam object (eg SAS or C code)? I wanted to

Hello, Is it possible to estimate prediction interval using GAM? I looked through ?gam, ?predict.gam etc and the mgcv.pdf Simon Wood. I found it can calculate confidence interval but not clear if I can get it to calculate prediction interval. I read "Inference for GAMs is difficult and somewhat contentious." in Kuhnert and Venable An Introduction to R, and wondering why and if that

Hi.. I'm using gam() to fit a spline model for a data set that has two predictor variables (say A and B). The results indicate that the higher order interaction terms are significant. The R^2 jumps from .5 to .9 when I change the maximum order for the interaction from 10 to 15 (i.e. (AB)^10 to (AB)^15). Is there any way of finding out which of the terms in the model are really

Hi, is there a way in R to generate a polynomial spline with multiple predictor variables? I have one response and two predictors and I'm trying to fit a spline model for this... Please cc me on the reply.. Thanks, nirmal

Christoph Lehman had problems with seperated data in two-class logistic regression. One useful little trick is to penalize the logistic regression using a quadratic penalty on the coefficients. I am sure there are functions in the R contributed libraries to do this; otherwise it is easy to achieve via IRLS using ridge regressions. Then even though the data are separated, the penalized