similar to: se from predict.glm

Dear R-helpers, I don't see a difference between the following two plots of effect objects, which I understand should be different. What am I missing? require(doBy) require(effects) data(budworm) m1 <- glm(ndead/20 ~ sex + log(dose), data=budworm, weight=ntotal, family=binomial) m1.eff <- all.effects(m1) plot(m1.eff, rescale.axis = FALSE, selection = 2, main = 'rescale =

> From: Ross Darnell > > Liaw, Andy wrote: > >>From: Liaw, Andy > >> > >> > >>>From: Ross Darnell > >>> > >>>A good point but what is the value of storing a large set of > >>>predicted > >>>values when the values of the explanatory variables are lost > >>>(predicted >

Dear R Wizards, After much frustration and days of confusion I have finally broken down and am asking for help, which I don’t like doing, but I just can’t figure this one out on my own. I’ve conducted a laboratory experiment testing the effects of temperature and salinity on whether or not a biological event will occur (Go or NoGo). I’ve coded the factors temperature and salinity as factors for

It was a real surprise, but a student in my class found that the function eigen is buggy. He traced to the problem from his inability of getting principal component analysis to work on his data. Chong Gu Here is a matrix I generated through X'X, where X is 2x3. > jj [,1] [,2] [,3] [1,] 0.8288469 -1.269783 -0.7533517 [2,] -1.2697829 2.162132 2.0262917 [3,]

Folks, I am not sure if it's a feature or a "bug". The same is observed in Splus. Suppose I have Poisson counts, and I would like to estimate the parameter using glm. I would assume I can feed it the individual counts, or I can feed it the distinctive counts with the frequency as the weights, and I would get the same results. I do, but the deviance df are returned differently.

survexp in survival5 doesn't seem to work for me. see below: > library(survival5) Attaching Package "package:survival5": The following object(s) are masked from package:base : sort.list > library(chron) > data(ratetables) > survexp(~ratetable(year=julian(6,1,1991), + sex=1,age=35*365.24),times=(0:30)/6*365.24) Error in as.character(as.date(c(min(R[, 3]),

Dear R-Listers, to measure the psychometric curve of pitch discrimination, one sequentially presents two tones of slightly different pitch to an observer (animal will do), and asks "which is higher". The pschometric curve is the fraction of correct responses plotted against the pitch difference. It passes through 50% (pure guessing) at zero and normally approaches 100% at large

I was trying to add points to filled.contour plots, but the points(...) function apparently uses a different coordinate system than the filled.contour(...) function; it appears that points(...) paints points where they would be if the contours were stretched over the whole plotting frame including the legend bar. Am I missing something or is it a "misalignment" in the code? BTW, I have

I am using R to teach, and here are a couple of things that I thought would work didn't work. 1. I noticed the utility data(***,package=***) recently and like it very much, but unless I type in the whole word "package" I'll get an error in 1.4.0. For example, data(cats,package=MASS) works fine but data(cats,pac=MASS) doesn't. 2. drop1 doesn't seem to be as smart as

I'd like to create a data frame with components > jk$x1 [1] 2 > jk$x2 [,1] [,2] [1,] 0 0 I used to be able to do it with > jk <- data.frame(x1=2,x2=I(matrix(0,1,2))) But now I get a error message. Can I still do what I want? Thanks for any help. Chong Gu -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list --

I was hoping that the following specification postscript(file="fig.ps",height=2,width=4) par(mar=c(5,5,2,2)+.1,mfrow=c(1,2),mex=.6) would yield the same scaling effect as postscript(file="fig.ps",height=2,width=6) par(mar=c(5,5,2,2)+.1,mfrow=c(1,3),mex=.6) but the fact is that the 'mfrow=c(1,2)' yields smaller plotting frame and bigger font size.

I needed to generate some data from the Inverse Gaussian distribution but it is not in R. A google search took me to http://www.maths.uq.edu.au/~gks/s/invgauss.html which contains the documentation for the d, p, r, q functions with a download link of the functions. The functions work fine in R. I am curious why the facilities are not included in R. Is it for legal reasons or just the

I am not sure if I should call this a bug or just a design imperfection. I have noticed that the data.frame function does not preserve the integrity of multivariate components. Here is a simple illustration: > x <- matrix(1:6,3,2) > y <- 1:3 > z <- data.frame(x=x,y=y) > z$x # NULL > z$y # [1] 1 2 3 One can however get by that by using model.frame: > zz <-

- introduce local variables (shortcuts for frequently used <dom>->grant_table) - adjust first parameter of mapcount() - drop lock acquisition from gnttab_get_version() - remove hard tabs and adjust formatting Signed-off-by: Jan Beulich <jbeulich@suse.com> Tested-by: Andrew Thomas <andrew.thomas@oracle.com> --- a/xen/common/grant_table.c +++ b/xen/common/grant_table.c @@

I am trying to get a prediction interval from a glm regression. With newdat being my set of values to be fitted, and glmreg the name of my regression, I am using the following code. predict(glmreg, newdat, se.fit = TRUE, interval = "confidence", level = 0.90) The problem is that I am only getting the standard error and the fitted value, not a prediction interval. Any help would be

Hello: I sent this question the other day with the wrong subject heading and couple typos, with no response. So, here I go again, having made those corrections. I would like to estimate, for lack of a better description, a partially additive non-parametric model with the following structure: z~ f(x,y):w1 + g(x,y):w2 + e In other words, I'd like to estimate the marginals with respect to

Can someone confirm that the EISPACK routines for eigenvalues of symmetric matrix are in base R. They seem to be, but I can't seem to locate where they are in the src tree. Thanks. Chong Gu -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or

I was reading Chapter 2 of the R manual that came with 64.1, but couldn't get the behavior out of expressions as described in the manual. Is there any example on the subject? For example, "x sub i" is supposed to be produced by "x[i]", but where to put it? I tried mtext(text=x[i]) and R simply interprets that as a regular expression. Thanks in advance for any clue that

Hi Once more I have doubts about references. I want to give a reference of the packages I've used (gss, MASS, geoR). For the gss package I'm using @TechReport{gss00, Author = {Chong Gu}, Title = {The gss package}, Institution = {CRAN}, year = 2000, month = {22 } # dec, } and something similar for the others, but this doesn't look

Two questions: 1. inverse.gaussian is up there as one of the glm families, but do people ever use it? There is no inverse.gaussian in the R distribution family, and when I checked McCullagh & Nelder, it only appeared twice in the book (according to subject index), once in the table on p. 30 and once on p. 38 in a passing sentence. Is there a good reference on this distribution? 2. When I