similar to: Typo in x11.Rd

Hello, In trying to minimize the margin on the left hand side when using dotchart I found what may be a typo in the code. In the lines below from dotchart.R, should nmai[4L] be nmai[2L]? if (!(is.null(labels) && is.null(glabels))) { nmai <- par("mai") nmai[2L] <- nmai[4L] + max(linch + goffset, ginch) + 0.1 par(mai = nmai) } Thank you, Stephen Weigand --

I am connecting from a PC to a Linux system running CentOS release 5.5 (Final) and it is extremely slow to render plots to the X11 device. This is not R's fault but I wonder if anyone can offer guidance so I can help the system administrators address the problem. I can connect to the Linux server using a NoMachine NX client for Windows or using X-Win32. I also have access to R running

I just downloaded the file ftp://ftp.stat.math.ethz.ch/Software/R/R-devel.tar.gz and within proc.time.Rd, the second paragraph of the \value section contains a typo: The resolution of the times will be system-specific; it is common for them to be recorded to of the order of 1/100 second, and elapsed [...] ^^^^^ I'd say replacing "to

I wonder if the 'sep' argument in reshape() is being ignored unintentionally: ## From example(reshape) df <- data.frame(id=rep(1:4,rep(2,4)), visit=I(rep(c("Before","After"),4)), x=rnorm(4), y=runif(4)) reshape(df, timevar="visit", idvar="id", direction="wide", sep = "_") id x.Before

I love the new tiff(), jpeg(), and png() in R 2.7.0 but found an issue that I didn't see reported. When specifying 'units = "in"' but forgetting to change the default height and width (so the figure is unintentionally going to be 480 inches by 480 inches) I run into problems. Here's the reproducible example: tiff("a.tiff", units = "in", res = 1200,

On Wed, 24 Aug 2005 Weigand.Stephen at mayo.edu wrote: > I just downloaded the file > > ftp://ftp.stat.math.ethz.ch/Software/R/R-devel.tar.gz > > and within proc.time.Rd, the second paragraph of the \value > section contains a typo: I believe your understanding of the English language is different from the author here, who is English. (You on the other hand seem to think

Hello, The diffs below are based on revision 37445 and show some typo corrections for writeLines.Rd, readLines.Rd, and data.matrix.Rd that I'd like to bring to the list's attention. Sincerely, Stephen Weigand Rochester, Minnesota, USA --- ./src/library/base/man/writeLines.Rd Sun Feb 26 13:46:06 2006 +++ /tmp/writeLines.Rd Sun Feb 26 20:53:44 2006 @@ -14,8 +14,8 @@ each

Hi, I am fairly new to GAM and started using package mgcv. I like the fact that optimal smoothing is automatically used (i.e. df are not determined a priori but calculated by the gam procedure). But the mgcv manual warns that p-level for the smooth can be underestimated when df are estimated by the model. Most of the time my p-levels are so small that even doubling them would not result

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, My question is more algorithmic than prectical. What I am trying to determine is, are the GAM algorithms used in the mgcv package affected by nonnormally-distributed residuals? As I understand the theory of linear models the Gauss-Markov theorem guarantees that least-squares regression is optimal over all unbiased estimators iff the data meet the conditions linearity,

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

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

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).

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, I'd like to understand 'what' is predicting the response for library(mgcv) gam? For example: library(mgcv) fit <- gam(y~s(x),data=as.data.frame(l_yx),family=binomial) xx <- seq(min(l_yx[,2]),max(l_yx[,2]),len=101) plot(xx,predict(fit,data.frame(x=xx),type="response"),type="l") I want to see the generalized function(s) used to predict the response

Dear List, This may be related to this email thread initiated by Ben Bolker last month: https://stat.ethz.ch/pipermail/r-devel/2011-July/061630.html In answering this Question on StackOverflow http://stackoverflow.com/q/7195628/429846 I noticed that `methods()` was not listing some S3 methods for `plot()` provided by the mgcv package. At the time I wanted to check the development version of R as

Dear R-users: Im trying (using gam package) to develop a stepwise analysis. My gam object contains five pedictor variables (a,b,c,d,e,f). I define the step.gam: step.gam(gamobject, scope=list("a"= ~s(a,4), "b"= ~s(b,4), "c"= ~s(c,4), "d"= ~s(d,4), "e"= ~s(e,4), "f"= ~s(f,4))) However, the result shows a formula containing the whole

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

Dear all, I run the GAMs (generalized additive models) in gam(mgcv) using the following codes. m.gam <-gam(mark~s(x)+s(y)+s(lstday2004)+s(ndvi2004)+s(slope)+s(elevation)+disbinary,family=binomial(logit),data=point) And two repeated warnings appeared. Warnings$B!'(B 1: In gam.fit(G, family = G$family, control = control, gamma = gamma, ... : Algorithm did not converge 2: In gam.fit(G,

Dear all, I'm a fairly new R user having two questions regarding gam: 1. The prediction example on p. 38 in the mgcv manual. In order to get predictions based on the original data set, by leaving out the 'newdata' argument ("newd" in the example), I get an error message "Warning message: the condition has length > 1 and only the first element will be used in: if