similar to: parse in text function of plots, confusing behaviour

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

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, +

Readers, I have been reading 'the r book' by Crawley and think that the generalised additive model is appropriate for this problem. The package 'gam' was installed using the command (as root) install.package("gam") ... library(gam) > library(gam) Loading required package: splines Loading required package: akima > library(mgcv) This is mgcv 1.3-25 Attaching

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 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 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

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'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 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

Hello, I am trying to update the mgcv package on my Linux box and I keep getting an "Out of memory!" error. Does anyone know of a fix for this? Below is a snippet of the message that I keep getting: Thank you. Geoff ** R ** inst ** preparing package for lazy loading ** help *** installing help indices >>> Building/Updating help pages for package 'mgcv' Formats:

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

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

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

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 running gam (mgcv version 1.3-29) and glm (logit) (stats R 2.61) on the same models/data, and I got error messages for the gam() model and warnings for the glm() model. R-help suggested that the glm() warning messages are due to the model perfectly predicting binary output. Perhaps the model overfits the data? I inspected my data and it was not immediately obvious to me (though I

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

Hi, I have used the mgcv library to generate a simple additive model. I want to know how to plot the function on the raw data with confidence intervals whan I have TWO variables in the model. I get it to work with one variable but not with two. I am on the limit for what I understand in R, so be gentle. I have read the help file on predict.gam, but did not get any help out of it. #My model: