similar to: "ask=F" option with plot.gam

hello I'm learning mgcv and would like to obtain numerical output corresponding to plot.gam. I can do so when seWithMean=FALSE (the default) but only approximately when seWithMean=TRUE. Can anyone show how to obtain the exact values? Alternatively, can you clarify the explanation in the manual "Note that, if seWithMean=TRUE, the confidence bands include the uncertainty about the

Hello. I would like to know if there is a command for stopping between multiple grpahs. I have a for in which I create a graph in each iteration. I would like R to wait for a click or an enter to pass to the next graph. Does anybody know how can this be done. Thank you Felipe Parra [[alternative HTML version deleted]]

Recently I was using GAM and couldn't help noticing the following incoherence in prediction: > data(gam.data) > data(gam.newdata) > gam.object <- gam(y ~ s(x,6) + z, data=gam.data) > predict(gam.object)[1] 1 0.8017407 > predict(gam.object,data.frame(x=gam.data$x[1],z=gam.data$z[1])) 1 0.1668452 I would expect that using two types of predict arguments

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

I would like to request help with the following: I am trying to use a Generalized Additive Model (gam) to examine the density distribution of fish as a function of latitude and longitude as continuous variables, and year as a categorical variable. The model is written as:   gam.out   <-  gam(Density ~ s(Lat) + s(Lon) + as.factor(Year))   The fitted model prediction of the link function is

I'm fitting a Poisson gam model, say model<-gam(a65tm~as.factor(day.week )+as.factor(week)+offset(log(pop65))+s(time,k=10,bs="cr",fx=FALSE,by=NA,m=1),sp=c( 0.001),data=dati1,family=poisson) Currently I've difficulties in obtaining right predictions by using gam.predict function with MGCV package in R version 2.2.1 (see below my syntax).

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've got a small problem. I have some observational data (environmental samples: abiotic explanatory variable and biological response) to which I've fitted both a multiple linear regression model and also a gam (mgcv) using smooths for each term. The gam clearly fits far better than the lm model based on AIC (difference in AIC ~ 8), in addition the adjusted R squared for the gam is

I'm beginner in R! I have a lot of problems on R..... I have three questions about GAM 1. What is the function of Gaussian distribution in GAM?(if I choose family is Gaussian) Is it used in the predictand value (Y)? 2. How to plot a graph the gam function? For example: y<-gam(a~s(b),family=gaussian (link=log) ,Data) how to plot x axis is s(b) and y axis is log a??? 3. if I use GAM to

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

Hello everyone. I am doing a logistic gam (package mgcv) on a pretty large dataframe (130.000 cases with 100 variables). Because of that, the gam is fitted on a random subset of 10000. Now when I want to predict the values for the rest of the data, I get the following error: > gam.basis_alleakti.1.pr=predict(gam.basis_alleakti.1, +

Hi all, I am using GAM to model time trends in a logistic regression. Yet I would like to extract the the fitted spline from it to add it to another model, that cannot be fitted in GAM or GAMM. Thus I have 2 questions: 1) How can I fit a smoother over time so that I force one knot to be at a particular location while letting the model to find the other knots? 2) how can I extract the matrix

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) >

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 <-

Dear R users, I have a question about the plot with the package gam. I need to plot different main effect functions, related to different gam models, in the same graphics (i.e. the same covariate about different models). I used the plot.gam e preplot.gam documentations. Using preplot.gam I can plot the single function but I'm not able to put all the functions together. Does anybody can help

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:

Hello R users, I'm working with a time-series of several years and to analyze it, I?m using GAM smoothers from the package mgcv. I?m constructing models where zooplankton biomass (bm) is the dependent variable and the continuous explanatory variables are: -time in Julian days (t), to creat a long-term linear trend -Julian days of the year (t_year) to create an annual cycle - Mean temperature

Hello I'm analyzing a dichotomous dependent variable (dv) with more than 100 measurements (within-subjects variable: hours24) per subject and more than 100 subjects. The high number of measurements allows me to model more complex temporal trends. I would like to compare different models using GLM, GLMM, GAM and GAMM, basically do demonstrate the added value of GAMs/GAMMs relative to

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,