similar to: gam and step

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

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)

Dear R-gurus, I have an interpretation problem regarding lme models. I am currently working on dog locomotion, particularly on some variation factors. I try to figure out which limb out of 2 generated more dispersed data. I record a value called Peak, around 20 times for each limb with a record. I repeat the records during a single day, and on several days. I tried to build two models, one

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

Hello, in the paper "Avoiding the effects of concurvity in GAM's .." of Figueiras et al. (2003) it is mentioned that in GLM collinearity is taken into account in the calc of se but not in GAM (-> results in confidence interval too narrow, p-value understated, GAM S-Plus version). I haven't found any references to GAM and concurvity or collinearity on the R page. And I

Dear all, I'm using a binomial distribution with a logit link function to fit a GAM model. I have 2 questions about it. First i am not sure if i've chosen the most adequate distribution. I don't have presence/absence data (0/1) but I do have a rate which values vary between 0 and 1. This means the response variable is continuous even if within a limited interval. Should i use

Qusetion #1 ********* Model selection in GAM can be done by using: 1. step.gam {gam} : A directional stepwise search 2. gam {mgcv} : Smoothness estimation using GCV or UBRE/AIC criterion Suppose my model starts with a additive model (linear part + spline part). Using gam() {mgcv} i got estimated degrees of freedom(edf) for the smoothing splines. Now I want to use the functional form of my model

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

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,

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

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

I fitted a GAM model with Poisson distribution using the function gam() in the mgcv package. My model is of the form: mod<-gam(y~s(x0)+s(x1)+s(x2),family=poisson). To extract estimates at a specified set of covariate values I used the gam `predict' method. But I want to get estimate and standard error of the difference of two fitted values. Can someone explain what should I do? Thank

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?

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)

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

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

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

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

Dear all, I am analysing growth data - response variable - using GAM and GAMM models, and 4 covariates: mean size, mean capture year, growth interval, having tumors vs. not The models work fine, and fit the data well, however when I try to compare models using AIC I cannot get an AIC value. This is the code for the gam model:

Hi, Is it possible to compare two GAM objects created with the gam() function from the mgcv package. I use a slightly modified version of anova.glm() named anova.gam(), modified from John Fox (2002). It often gives me some aberant responses, especially with "F" test. I use a quasibinomial model and scale (dispersion) is calculated and used in the calculation of the F value. Does someone