similar to: plot.gam

In the following special case, seq fails to give the right answer (which is 0 ) > seq(0,0,1) Error in if (dd < sqrt(.Machine$double.eps)) return(from) : missing value where logical needed For any other equal from and to , it works: > seq(1,1,1) [1] 1 The error occurs in the statement dd <- abs(del)/max(abs(to), abs(from)) of seq.default -- for obvious reasons.

symbols(iris[,1],iris[,2],rectangles=as.matrix(iris[,3:4]),inches=0.3, ylim=c(1,5)) Error in plot.default(NA, NA, type = "n", ylim = ylim, xlim = xlim, xlab = xlab, : formal argument "ylim" matched by multiple actual arguments Since plot is called inside symbols, with specified xlim and ylim, any specification through ,... hurts this call. I am pretty sure that you

>>>>> "WSt" == Werner Stahel <stahel@stat.math.ethz.ch> writes: [to me privately] WSt> I do not want to call this a bug, but it bugged me. Please try the WSt> following: WSt> tform <- sqrt(RADAI) ~ sqrt(RADAI.e) + TAGE.ej + SPITAL + ARZT + DAS28 + WSt> SJC + TJC + DGA + HAQ + PGA + PAIN + YEAR.SYM + YEAR.DIA + WSt> ALTER +

Hallo, I'm looking for a "sunflower" plot. I noticed the Splus function "p.sunflowers" (Andreas Ruckstuhl, Werner Stahel, Martin Maechler, Tim Hesterberg) don't work... If its not available I would port this function... Thanks for 1 answer P *************************************************************************** P.Malewski Tel.: 0531 500965 Maschplatz 8

Dear all, I've now spent a couple of days trying to learn R and, in particular, the gam() function, and I now have a few questions and reflections regarding the latter. Maybe these things are implemented in some way that I'm not yet aware of or have perhaps been decided by the R community to not be what's wanted. Of course, my lack of complete theoretical understanding of what

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

I am trying to apply methods used by Chaloupka & Limpus (1997) ( http://www.int-res.com/articles/meps/146/m146p001.pdf) to my own turtle growth data. I am having trouble with two things... 1) After the GAM is fit, the residuals are skewed. >m1 <- gam(growth~s(mean.size, bs="cr")+s(year,bs="cr",k=7)+s(cohort,bs="cr")+s(age,bs="cr"), data=grow,

Hello, As I am relatively new to the R environment this question may be either a) Really simple to answer b) Or I am overlooking something relatively simple. I am trying to add a VarIdent structure to my gam model which is fitting smoothing functions to the time variables year and month for a particular species. When I try to add the varIdent weights to variable Month I get this error returned.

I am writing my thesis with the function gam(), with the package {mgcv}. My command is: gam(y~s(x1,bs="cr")+s(x2, bs="cr")). I need help to know what are the default basis funcitons for gam. I have not found any detailed reference for this. Can anyone help me with this?? -- View this message in context:

Hi, I am using mgcv:gam and have developed a model with 5 smoothed predictors and one factor. gam1 <- gam(log.sp~ s(Spr.precip,bs="ts") + s(Win.precip,bs="ts") + s( Spr.Tmin,bs="ts") + s(P.sum.Tmin,bs="ts") + s( Win.Tmax,bs="ts") +factor(site),data=dat3) The total deviance explained = 70.4%. I would like to extract the variance explained

Hi folks, I originally tried R-SIG-Mixed-Models for this one (https://stat.ethz.ch/pipermail/r-sig-mixed-models/2010q3/004170.html), but I think that the final steps to a solution aren't mixed-model specific, so I thought I'd ask my final questions here. I used gamm4 to fit a generalized additive mixed model to data from a AxBxC design, where A is a random effect (human participants in

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 =

Dear R-hep, I'm trying to get the first derivative of a smooth from a gam model like: model<-gam(y~s(x,bs="cr", k=5)+z) and need the derivative: ds(x)/dx. Since coef(model) give me all the parameters, including the parameters of the basis, I just need the 1st derivative of the basis s(x).1, s(x).2, s(x).3, s(x).4. If the basis were generated with the function

We are using GAM in mgcv (Wood), relatively new users, and wonder if anyone can advise us on a problem we are encountering as we analyze many short time series datasets. For each dataset, we have four models, each with intercept, predictor x (trend), z (treatment), and int (interaction between x and z). Our models are Model 1: gama1.1 <- gam(y~x+z+int, family=quasipoisson) ##no smooths Model

Hi everybody, I am trying to fit a GAM model without intercept using library mgcv. However, the result has nothing to do with the observed data. In fact the predicted points are far from the predicted points obtained from the model with intercept. For example: #First I generate some simulated data: library(mgcv) x<-seq(0,10,length=100) y<-x^2+rnorm(100) #then I fit a gam model with

Hello, How do I plot a gam fit object on probability (Y axis) vs raw values (X axis) axis and include the confidence plot lines? Details... I'm using the gam function like this: l_yx[,2] = log(l_yx[,2] + .0004) fit <- gam(y~s(x),data=as.data.frame(l_yx),family=binomial) And I want to plot it so that probability is on the Y axis and values are on the X axis (i.e. I don't want log

Hello I'm using function gam from package mgcv to fit splines. ?When I try to make a prediction slightly beyond the original 'x' range, I get this error: > A = runif(50,1,149) > B = sqrt(A) + rnorm(50) > range(A) [1] 3.289136 145.342961 > > > fit1 = gam(B ~ s(A, bs="ps"), outer.ok=TRUE) > predict(fit1, newdata=data.frame(A=149.9), outer.ok=TRUE) Error

Dear useRs, I have used using the excellent mgcv package (version 1.7-12) to create a generalized additive model (gam) including random effects - represented with s(...,bs="re") - on the basis of dialect data. My model contains two random-effect factors (Word and Key - the latter representing a speaker) and I have added both random intercepts and various random slopes for these

Dear R-experts, Here below my R code. I get a NaN response for gam with mgcv library. How to solve that problem? Many thanks. ######################################################### library(mgcv) ? y=c(23,24,34,40,42,43,54,34,52,54,23,32,35,45,46,54,34,36,37,48) x1=c(0.1,0.3,0.5,0.7,0.8,0.9,0.1,0.7,0.67,0.98,0.56,0.54,0.34,0.12,0.47,0.52,0.87,0.56,0.71,0.6)

Hi, all I want to choose same knots in GAM for 10 different studies so that they has the same basis function. Even though I choose same knots and same dimensions of basis smoothing, the basis representations are still not same. My command is as follows: data.gam<-gam(y~s(age,bs='cr',k=10)+male,family=binomial,knots=list(age=seq(45,64,length=10))) What is my mistake for choice of