similar to: Extracting the results of a gam smooth

Hi, I'm working with step.gam in gam package. I'm interested both in spline and lowess functions and when I define all the models that I'm interested in I get something like that: > gam.object.ALC<-gam(X143S~ALC,data=dane,family=binomial) >

hi, i'm using gam() function from package mgcv with default option (edf estimated by GCV). >G=gam(y ~ s(x0, k = 5) + s(x1) + s(x2, k = 3)) >SG=summary(G) Formula: y ~ +s(x0, k = 5) + s(x1) + s(x2, k = 3) Parametric coefficients: Estimate std. err. t ratio Pr(>|t|) (Intercept) 3.462e+07 1.965e+05 176.2 < 2.22e-16 Approximate significance of smooth

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 R-hep, I'm trying to get the first derivative of a smooth from a gam model like: model<-gam(y~s(x,bs="cr", k=5)+z) and need the derivative: ds(x)/dx. Since coef(model) give me all the parameters, including the parameters of the basis, I just need the 1st derivative of the basis s(x).1, s(x).2, s(x).3, s(x).4. If the basis were generated with the function

Hi, all, Does anyone know how to get the functional form of the smooth terms in GAM? eg. I fit y=a+b*s(x) where s is the smooth function. After fitting this model with GAM in R, I want to know the form of the s(x). Any suggestion is appreciated. Thanks, Jin --------------------------------- Ahhh...imagining that irresistible "new car" smell?

All: I am trying to use gam in a scatterplot smoothing problem. The data being smoothed have greater 1000 observation and have multiple "humps". I can smooth the data fine using a function something like: out <- ksmooth(x,y,"normal",bandwidth=0.25) plot(x,out$y,type="l") The problem is when I try to fit the same data using gam out <-

I am a little bit confusing about the following help message on how to fit a GAM model with interaction between factor and smooth terms from http://rss.acs.unt.edu/Rdoc/library/mgcv/html/gam.models.html: ?Sometimes models of the form: E(y)=b0+f(x)z need to be estimated (where f is a smooth function, as usual.) The appropriate formula is: y~z+s(x,by=z) - the by argument ensures that the smooth

Hi, I am fitting two models, a generalized linear model and a generalized additive model, to the same data. The R-Help tells that "A generalized additive model (GAM) is a generalized linear model (GLM) in which the linear predictor is given by a user specified sum of smooth functions of the covariates plus a conventional parametric component of the linear predictor." I am fitting the GAM

Hi, I have been trying to fit an un-penalised gam in mgcv (in order to get more reliable p-values for hypothesis testing), but I am struggling to get the model to fit sucessfully when I add in a te() interaction. The model I am trying to fit is: gam(count~ s(x1, bs = "ts", k = 4, fx = TRUE) + s(x2, bs = "ts", k = 4, fx = TRUE) + te(x2, x3, bs =

dear all, i have a little question, but it make me torment long time hope you can help me and give some advices , thanks i use smoothing spline in package gam the model > m1=gam(y~ost+wst+park10+sch50+comm+build+suite+y05+y06+y07+y99+y98+s(builarea)+s(age)+s(fl)+s(totfl)+s(cbd)+s(redl)) and summary(m1) can show the "s"(smoothing) variables' Signif. codes.

Hi all, I would like to export the smooth function estimate I got from gam to plot it in another graphics software. In S-plus I use the function preplot() for that, but it seems not to work in R. Has somebody an idea how to solve that? Thanks Stephanie ******************************** Stephanie von Klot Institut f?r Epidemiologie GSF - Forschungszentrum f?r Umwelt und Gesundheit Ingolst?dter

Dear useRs, I have a question with respect to fitting a non-linearity using gam (mgcv package, version 1.7-16). In a study I'm currently conducting, I'd like to find out if there is a breakpoint after which the effect of Age of Acquisition (AOA) of the second language changes. I.e. if the slope of AOA before the breakpoint (at a certain AOA) is different from the slope past the

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 :

Picking up an ancient thread (from Oct 2007), I have a somewhat more complex problem than given in Simon Wood's example below. My full model has more than two smooths as well as factor variables as in this simplified example: b <- gam(y~fv1+s(x1)+s(x2)+s(x3)) Judging from Simon's example, my guess is to fit reduced models to get components of deviance as follows: b1 <-

I have contributed a "gam" library to CRAN, which implements "Generalized Additive Models". This implementation follows closely the description in the GAM chapter 7 of the "white" book "Statistical Models in S" (Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy in "Generalized Additive Models" (Hastie & Tibshirani 1990,

I have contributed a "gam" library to CRAN, which implements "Generalized Additive Models". This implementation follows closely the description in the GAM chapter 7 of the "white" book "Statistical Models in S" (Chambers & Hastie (eds), 1992, Wadsworth), as well as the philosophy in "Generalized Additive Models" (Hastie & Tibshirani 1990,

I am trying to test out several mgcv::gam models in a scalar-on-function regression analysis. The following is the 'hierarchy' of models I would like to test: (1) Y_i = a + integral[ X_i(t)*Beta(t) dt ] (2) Y_i = a + integral[ F{X_i(t)}*Beta(t) dt ] (3) Y_i = a + integral[ F{X_i(t),t} dt ] equivalents for discrete data might be: 1) Y_i = a + sum_t[ L_t * X_it * Beta_t ] (2) Y_i

Hi all, I try to interpolate a data set in the form: time Erg 0.000000 48.650000 1.500000 56.080000 3.000000 38.330000 4.500000 49.650000 6.000000 61.390000 7.500000 51.250000 9.000000 50.450000 10.500000 55.110000 12.000000 61.120000 18.000000 61.260000 24.000000 62.670000 36.000000 63.670000 48.000000 74.880000 I want to get smoothed splines by using the class gam The first way I tried , was

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

Greetings This is a long email. I'm struggling with a data set comprising 2,278 hydroacoustic estimates of fish biomass density made along line transects in two lakes (lakes Michigan and Huron, three years in each lake). The data represent lakewide surveys in each year and each data point represents the estimate for a horizontal interval 1 km in length. I'm interested in comparing