similar to: Time series in R

I've got a data frame which I've split by a factor, creating a list of dataframes which I have then done various operations on individually. I next want to recombine the resulting dataframes (still held in a list, still with the same number of columns with the same names) and there does not appear to be a `good' way to do this - at the moment, I'm using a for-loop with the rbind

Hi, Currently the `gaussian' family's initialization code signals an error if any response data are zero or negative and a log link is used. Given that zero or negative response data are perfectly legitimate under the GLM fitted using `gaussian("log")', this seems a bit unsatisfactory. Might it be worth changing it? The current offending code from `gaussian' is:

x <- NA na.act <- na.action(na.exclude(x)) y <- rep(0,0) naresid(na.act,y) ... currently produces the result... numeric(0) ... whereas the documentation might lead you to expect NA The behaviour is caused by the line if (length(x) == 0L) return(x) in `stats:::naresid.exclude'. Removing this line results in the behaviour I'd expected in the above example (and in a

Hello All, I have a working port of R on my SL5500. I've not tested the X windowing support yet, but was more concerned about the accuracy of the fp emulation. The following is the result of the test which Stuart Leask recommended I should try: Mandrake 8.2 > x<-NA > is.na(x) [1] TRUE > x+1 [1] NA > 2*x [1] NA Zaurus OZ3 > x<-NA > is.na(x) [1] TRUE > x+1 [1] 1

Hi all R users ! I'm using gamm function from Simon Wood's mgcv package, to fit a spatial regression Generalized Additive Mixed Model, as covariates I have the geographical longitude and latitude locations of indexed data. I include a random effect for each district (dist) so the code is fit <- gamm(y~s(lon,lat,bs="tp", m=2)+offset(log(exp.)), random=list(dist=~1),

Dear R users I have a list, as follows: > intvl.period.myrs $Devonian [1] 4.8 4.2 9.5 5.7 $Ordovician [1] 7.2 5.1 10.2 1.9 $Silurian [1] 4.7 3.0 7.8 2.0 3.3 1.6 2.6 2.7 I want to write a loop that will sum up the values in each part, and give me a vector containing the (in this case 3) summed values this is what I have so far: for (i in 1:length(names(intvl.periods.myrs)) {

Can anyone tell me the trick for obtaining the smoother matrix from smooth.spline when there are non-unique values for x. I have the following code but, of course, it only works when all values of x are unique. ## get the smoother matrix (x having unique values smooth.matrix = function(x, df){ n = length(x); A = matrix(0, n, n); for(i in 1:n){ y = rep(0, n); y[i]=1; yi =

Hello Everyone I have an R script (and a source file which I keep my functions) that I need to run on 70 data sets (each consisting of a pair of files). I wish to run these data sets in a computer cluster that is run by my uni (HOWEVER they cannot help me with this problem but say it is do-able) the cluster is clever enough that if i set my data up as follows: within one folder called

HELLO ALL, I AM GETTING AN ERROR MESSAGE WHEN TRYING TO RUN A GAMM MODEL LIKE THE ONE BELOW. I AM USING R VERSION 2.14.1 (2011-12-22) AND MGCV 1.7-12. M1 <-gamm(DepVar ~ Treatment + s(Year, by =Treatment), random=list(Block=~1), na.action=na.omit, data = mydata, correlation = corARMA(form =~ Year|Treatment, p = 1, q = 0)) THIS IS THE ERROR MESSAGE Error in `*tmp*`[[k]] : attempt to

Hello everyone I have problem with axis labels on graphs, I have my code as below: plot(0,0,xlim=c(1,ncol(PA)),ylim=c(1,nrow(PA)),main="Stratigraphic Range",xlab="Time Bins",ylab="Taxa",cex.axis=1.5,cex.lab=2,cex.main=2.5,mgp=c(5,1.5,0),xaxt="n") text(1:(length(strat_name)), y= 0, adj=1, srt=45,labels=strat_name,xpd=TRUE, cex=1) #adds text to x

Dear useRs, This is the first time I post to this list and I would appreciate any help available. I've used the excellent mgcv package for a while now to investigate geographical patterns of language variation, and it has has always worked without any problems for me. The problem below occurs using R 2.14.0 (both 32 and 64 bit versions in Windows and the 64 bit version in Unix) and mgcv (both

Dear All Hopefully someone out there can point out what I am missing! I have a (large, several hundred) dataset of gardens in which over two years the presence/absence of a particular bird species is noted each week. I have good reason to believe there is a difference between the two years in the weekly proportion of gardens and would like to assess this, before going on to look in more detail at

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This is in regards to the suggested use of type="lpmatrix" in the documentation for mgcv::predict.gam. Could one not get the same result more simply by using type="terms" and interpolating each term directly? What is the advantage of the lpmatrix approach for prediction outside R? Thanks. -- View this message in context:

Hi, I am trying to build GAM with linear constraints, for a general link function, not only identity. If I understand it correctly, the function pcls() can solve the problem, if the smoothness penalties are given. What I need is to incorporate the constraints before calculating the penalties. Can this be done in R? Any help would be greately appreciated. -- View this message in context:

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 friends, I have install the last version 2.10 and now I have probles with 'mgcv' which can be load. I proved to eliminated and reinstalled but always obtain the same message: > library (mgcv) This is mgcv 1.6-1. For overview type `help("mgcv-package")'. Error in runif(1) : .Random.seed is not an integer vector but of type 'list' Error : .onAttach non

hi, I am trying to run a mixed effect gam using gamm (mgcv) and I get the following error: "Error in lme.formula(y ~ X - 1, random = rand, data = strip.offset(mf), : nlminb problem, convergence error code = 1 message = iteration limit reached without convergence (9)" I've read all about 'notExp2' but since I am not an expert I didn't understand that much. I was

R-users E-mail: r-help@r-project.org My understanding is that package "mgcv" is based on "Generalized Additive Models: An Introduction with R (by Simon N. Wood)". On the page 126 of this book, eq(3.4) looks a quartic equation with respect to "x", not a cubic equation. I am wondering if all routines which uses cubic splines in mgcv are based on this quartic

Hello all, I was wondering if anyone is aware of formal approaches and tools for comparing partial response curves produced in GAM? My interest is in determining if two partial response curves are "statistically" different. I recognize that point-wise standard error estimates can be produced using the GAM package but Im not certain how to translate this into a statistical test for the