similar to: Mag Gam

http://bayviewelc.co.nz/ylih/yhrdib.euyeznulw Mag Gam 7/21/2013 7:27:55 AM

http://bruidium.nl/qgqf/rkr.tzbotgkedmrfffzc Mag Gam 7/21/2013 7:26:27 AM

http://dks.shrikrishnaent.com/jarhfjf/jkhiymwm.zkcupfeoseu Mag Gam 7/21/2013 7:26:35 AM

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 :

Hello, I am using the function step.gam() from the 'gam' package (header info from library(help=gam) included below) and have come across some behavior that I cannot understand. In short, I have written a function that 1) creates a dataframe, 2) calls gam() to create a gam object, then 3) calls step.gam() to run stepwise selection on the output from gam(). When I do this, gam()

Dear Trevor, I've noticed a discrepancy in the degrees of freedom reported by gam() from the gam package in R vs. gam() in S-PLUS. The nonparametric df differ by 1; otherwise (except for things that depend upon the df), the output is the same: --------- snip ------------ *** From R (gam version 0.93): > mod.gam <- gam(prestige ~ lo(income, span=.6), data=Prestige) >

Dear Mag. Ferri Leberl, I'm using something like: ----------------------- tex.tex --------------------------- \documentclass{article} \usepackage{graphicx} \usepackage{fancyvrb} \newcommand{\Rlogo}{\protect\includegraphics[height=1.8ex,keepaspectratio]{Rlogo.pdf}} \newcommand{\myinput}[1] {\begin{scriptsize} \VerbatimInput[frame=single,label=#1]{#1} \end{scriptsize}} \title{The R logo,

Dear Mag. Ferri Leberl, I'm using something like: ----------------------- tex.tex --------------------------- \documentclass{article} \usepackage{graphicx} \usepackage{fancyvrb} \newcommand{\Rlogo}{\protect\includegraphics[height=1.8ex,keepaspectratio]{Rlogo.pdf}} \newcommand{\myinput}[1] {\begin{scriptsize} \VerbatimInput[frame=single,label=#1]{#1} \end{scriptsize}} \title{The R logo,

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

Hi all, until now I have generally used mgcv for gams, however, I decided to experiment with the package gam, and have ran into the previously outlined problem (see below), for which I have not yet found a solution in the archives. If anyone has any suggestions, please let me know. The only difference for my case is that I am running R 2.9.0 under windows Thank you! I'm running R

Dear List, I'm just teaching myself semi-parametric techniques. Apologies in advance for the long post. I've got observational data and a longitudinal, semi-parametric model that I want to fit in GAM (or potentially something equivalent), and I'm not sure how to do it. I'm posting this to ask whether it is possible to do what I want to do using "canned" commands

Dear R users, I recently stumbled upon problems of reproducibility while running GAM analyses in different R and gam package versions. In the example below, a small dataset is created in which the y and x1 variables are 100% correlated. The intents of this example were primarily for regression testing and, secondarily, to evaluate how the gam algorithm behaves under extreme/limit conditions. I

Hi, I'm working with gamm models of this sort: gm<- gamm(Z~te(x,y),data=DATA,random=list(Group=~1)) gm1<-gamm(Z~te(x,y,by=Factor)+Factor,data=DATA,random=list(Group=~1)) with a dataset of about 70000 rows and 110 levels for Group if I use plot(gm1$gam), I obtain 3 different surface plots, one for each level of my factor but I would like to create more complex contour plots for those 3

Hi all, Has anyone tried to plot math symbols with vis.gam() - a function that uses (I think) plot.gam() internally? This is from the package mgcv - I am new to gam(). For example, with the normal plot(), the expression() works fine for Math symbols: plot(xx, ylab=expression(paste(Delta,"H",sep=""))) # works fine But, with vis.gam(), the same

Hallo I need some help with the output provided by gam.check after a gam fit (using the package mgcv). To give a brief description of my data, I have claims: a vector of values, which include NA's and one large negative value - otherwise all positive (55 values in total that are not NA). origin: a factor with 10 levels j : taking the values 1, 2, ...., 10 I have fitted a gam, with: >

R-users E-mail: r-help@r-project.org I have a quenstion on "gam()" in "gam" package. The help of gam() says: 'gam' uses the _backfitting algorithm_ to combine different smoothing or fitting methods. On the other hand, lm.wfit(), which is a routine of gam.fit() contains: z <- .Fortran("dqrls", qr = x * wts, n = n, p = p, y = y *

Dear r-helpers, I use function step.gam (package gam, T. Hastie) with several explanatory variables to build a model. Unfortunately, I obviously have too many variables. This message occurs on my 4 core 64bit machine with 8GB RAM in R2.11.1 for Windows (64bit build): Error in array(FALSE, term.lengths) : 'dim' specifies too large an array I read that this message occurs when running out

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

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 <-