similar to: GAMs and isotropic bivariate functions with mgcv

mgcv 1.0 (package providing gams etc) will be released with R 1.9.0. (R 1.8.x compatible versions can be found at: http://www.stats.gla.ac.uk/~simon/simon/mgcv.html) There are quite a few changes from mgcv 0.9: hence this message. The main new features are: * A generalized additive mixed modelling function `gamm' (which uses lme from the nlme library of glmmPQL from the MASS library for

mgcv 1.0 (package providing gams etc) will be released with R 1.9.0. (R 1.8.x compatible versions can be found at: http://www.stats.gla.ac.uk/~simon/simon/mgcv.html) There are quite a few changes from mgcv 0.9: hence this message. The main new features are: * A generalized additive mixed modelling function `gamm' (which uses lme from the nlme library of glmmPQL from the MASS library for

Hi all, My question concerns 2 error messages; one in the gam package and one in the mgcv package (see below). I have read help files and Chambers and Hastie book but am failing to understand how I can solve this problem. Could you please tell me what I must adjust so that the command does not generate error message? I am trying to achieve model selection for a GAM which is required for

This is my first time running GAMs in R. My csv file has these column headings: "X" "Y" "Sound" "Atlantic" "Blacktip" "Bonnet" "Bull" "Finetooth" "Lemon" "Scalloped" "Sandbar" "Spinner" "Abundance" "Diversity"

Dear list, I?m using the ?mgcv? package to fit some GAMs. Some of my covariates are derived quantities and have an associated standard error, so I would like to incorporate this uncertainty into the GAM estimation process. Ideally, during the estimation process less importance would be given to observations whose covariates have high standard errors. The gam() function in the ?mgcv? package

Dear list, I?m wondering if there is something analogous to the TukeyHSD function that could be used for parametric terms in a GAM. I?m using the mgcv package to fit models that have some continuous predictors (modeled as smooth terms) and a single categorical predictor. I would like to do post hoc test on the categorical predictor in the models where it is significant. Any suggestions?

I am using gam in mgcv (1.3-22) and trying to use gcv to help with model selection. However, I'm a little confused by the process of assessing GCV scores based on their magnitude (or on relative changes in magnitude). Differences in GCV scores often seem "obvious" with my poisson gams but with negative binomial, the decision seems less clear. My data represent a similar pattern as

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Dear R-experts, I have a question on the formulas used in the gam function of the mgcv package. I am trying to understand the relationships between: y~s(x1)+s(x2)+s(x3)+s(x4) and y~s(x1,x2,x3,x4) Does the latter contain the former? what about the smoothers of all interaction terms? I have (tried to) read the manual pages of gam, formula.gam, smooth.terms, linear.functional.terms but

Dear list, I do apologize if these are basic questions. I am fitting some GAM models using the mgcv package and following the model selection criteria proposed by Wood and Augustin (2002, Ecol. Model. 157, p. 157-177). One criterion to decide if a term should be dropped from a model is if the estimated degrees of freedom (EDF) for the term are close to their lower limit. What would be the

R-users -- I would be interested in tools in R to fit the following gam model: logit(p) = a + f(x1) + f(x2) + f(x1,x2), where f(x1,x2) defines a surface. I have looked into the mgcv library, but it seems only to fit models of the form: logit(p) = a + f(x1) + f(x2) Any ideas? Cheers, Dan =-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-=-= Dan Powers Associate Professor,

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

Hi.., is there a function in R to fit bivariate splines ? I came across 'polymars' (POLSPLINE) and 'mars' (mda) packages. Are these the one to use or are there other specific commands? Thanks. Harsh

For summary(GAM) in the mgcv package smooth the degrees of freedom for the F value for test of smooth terms are the rank of covariance matrix of \hat{beta} and the residuals df. I've noticed that in a lot of GAMs I've fit the rank of the covariance turns out to be 9. In Simon Wood's book, the rank of covariance matrix is usually either 9 or 99 (pages 239-230 and 259). Can anyone

Hi, I need further help with my GAMs. Most models I test are very obviously non-linear. Yet, to be on the safe side, I report the significance of the smooth (default output of mgcv's summary.gam) and confirm it deviates significantly from linearity. I do the latter by fitting a second model where the same predictor is entered without the s(), and then use anova.gam to compare the

Hi there, Does R has built-in codes for nonpara. bivariate regression so that I can estimate the joint distribution of two variables as a function of some covariates? Thanks a lot. --------------------------------------------------- Ximing Wu Department of Economics University of Guelph Guelph, Ontario, Canada, N1G 2W1 Tel: (519) 842-4120, ext 53014 Fax: (519) 763-8497 email: xiwu at

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

I am trying to compare and contrast the smoothing in the {mgcv} version of gam vs. the {gam} version of gam but I get a strange side effects when I try to alternate calls to these routines, even though I detach and unload namespaces. Specifically when I start up R the following code runs successfully until the last line i.e. plot(g4,se=TRUE) when I get "Error in dim(data) <- dim :

Hi, I am using GAMs (package mgcv) to smooth event rates in a penalized regression setting and I was wondering if/how one can select the order of the derivative penalty. For my particular problem the order of the penalty (parameter "m" inside the "s" terms of the formula argument) appears to have a larger effect on the AIC/deviance of the estimated model than the