similar to: Using $ accessor in GAM formula

Why doesn't this work? x = zoo(1:5, as.Date('2001-01-01')+1:5) x[as.Date('2001-01-05')] x[as.Date('2001-01-05')] = 0 x I think this is especially bad because it doesn't cause an error. It lets you do something to x, but then you can't see x again to see what it did. [[alternative HTML version deleted]]

Does anyone know if Is there a way to manually install RSPython? I get this error when I try to run the script from my DOS prompt. V:\>R CMD INSTALL -c C:/Users/gene.leynes/Downloads/RSPython_0.7-1.tar.gz * installing to library 'C:/Users/gene.leynes/Documents/R/win-library/2.13' * installing *source* package 'RSPython' ... **********************************************

Hello, I have a question regarding model selection and dropping of terms for GAMs fitted with package mgcv. I am following the approach suggested in Wood (2001), Wood and Augustin (2002). I fitted a saturated model, and I find from the plots that for two of the covariates, 1. The confidence interval includes 0 almost everywhere 2. The degrees of freedom are NOT close to 1 3. The partial

** Disclaimer: I'm looking for general suggestions ** I'm sorry, but can't send out the file I'm using, so there is no reproducible example. I'm using read.table and it's taking over 30 seconds to read a tiny file. The strange thing is that it takes roughly the same amount of time if the file is 100 times larger. After re-reviewing the data Import / Export manual I think

I thought the "apply" functions are faster than for loops, but my most recent test shows that apply actually takes a significantly longer than a for loop. Am I missing something? It doesn't matter much if I do column wise calculations rather than row wise ## Example of how apply is SLOWER than for loop: #rm(list=ls()) ## DEFINE VARIABLES mu=0.05 ; sigma=0.20 ; dt=.25 ; T=50 ;

I've been wondering what L means in the R computing context, and was wondering if someone could point me to a reference where I could read about it, or tell me what it's called so that I can search for it myself. (L by itself is a little too general for a search term). I encounter it in strange places, most recently in the "save" documentation. save(..., list = character(0L),

Does anyone know how to pass a list of parameters into a function? for example: somefun=function(x1,x2,x3,x4,x5,x6,x7,x8,x9){ ans=x1+x2+x3+x4+x5+x6+x7+x8+x9 return(ans) } somefun(1,2,3,4,5,6,7,8,9) # I would like this to work: temp=c(x3=3,x4=4,x5=5,x6=6,x7=7,x8=8,x9=9) somefun(x1=1,x2=2,temp) # OR I would like this to work: temp=list(x3=3,x4=4,x5=5,x6=6,x7=7,x8=8,x9=9)

I just followed the instructions on CRAN<http://cran.r-project.org/bin/linux/ubuntu/README> to install R on an Ubuntu instance. sudo apt-get install r-base Why does it install an old version of R? Can I install version 15.1? I changed my sources.list to be a current cran mirror. I believe that I have entered the URL correctly because at first I had it wrong (there was a trailing

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

I don't understand how this function can subset by i when i is missing.... ## My function: myfun = function(vec, i){ ret = vec[i] ret } ## My data: i = 10 vec = 1:100 ## Expected input and behavior: myfun(vec, i) ## Missing an argument, but error is not caught! ## How is subsetting even possible here??? myfun(vec) Is there a way to check for missing function arguments, *and*

hello to everybody: I would to know with ranges of GCV or UBRE values can be considered as adequate to consider a GAM as correct Thanks in advance -- David Nogu?s Bravo Functional Ecology and Biodiversity Department Pyrenean Institute of Ecology Spanish Research Council Av. Monta?ana 1005 Zaragoza - CP 50059 976716030 - 976716019 (fax)

Dear R-devel, I am experiencing issues with running GAM models using mclapply, it fails to return any values if the data input becomes large. For example here the code runs fine with a df of 100 rows, but fails at 1000. library(mgcv) library(parallel) > df <- data.frame( + x = 1:100, + y = 1:100 + ) > > mclapply(1:2, function(i, df) { + fit <- gam(y ~ s(x, bs =

My apologies, I know that this is not a new problem, but I'm not sure how to find the answer I want to recursively loop over an object and trim trailing white space. When I use this function on a list of data.frame I get output like this: [1] "c(\" many spaces \", \" many spaces \")" "c(\" many spaces \", \" many spaces

On the Mac it's pretty easy to get to a USB drive by name. For example the following command works if you have a USB drive named "MYUSB" setwd('/Volumes/MYUSB') Is there a way to do the same thing in Windows (without knowing the drive letter)? Thanks! [[alternative HTML version deleted]]

What does Generalized Cross Validation score mean. I preform and ordisurf on an ordination (nmds) with an environmental variable. I am trying to figure out "how well" the environmental varibles predict/explain the sites placements in species space. Any help would be greatly appreciated. Any pointers to literature... would be welcome. thanks in advance, -- Stephen Sefick Research

# Just when I thought I had the basic stuff mastered.... # This has been quite perplexing, thanks for any help ## Here's the example: db1=data.frame( olditems=c('soup','','','','nuts'), prices=c(4.45, 3.25, 4.42, 2.25, 3.98)) db2=data.frame( newitems=c('stew','crackers','tofu','goatsmilk','peanuts'))

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

Dear all, I'm trying to fit a pure additive model of the following formula : fit <- gam(y~x1+te(x2, x3, bs="cr")) ,with the smoothing parameter estimation method "magic"(default). Regarding this, I have two questions : Question 1 : In some cases the value of "mgcv.conv$fully.converged" becomes "FALSE", which tells me that the method stopped with a

Hi.. I'm using gam() to fit a spline model for a data set that has two predictor variables (say A and B). The results indicate that the higher order interaction terms are significant. The R^2 jumps from .5 to .9 when I change the maximum order for the interaction from 10 to 15 (i.e. (AB)^10 to (AB)^15). Is there any way of finding out which of the terms in the model are really

I have 11 possible predictor variables and use them to model quite a few target variables. In search for a consistent manner and possibly non-manual manner to identify the significant predictor vars out of the eleven I thought the option "select=T" might do. Example: (here only 4 pedictors) first is vanilla with "select=F" >