similar to: turning labels into a vector

Dear all - I have followed the thread the reply to which was lead by Thomas Lumley about using pweibull to generate fitted survival curves for survreg models. http://tolstoy.newcastle.edu.au/R/help/04/11/7766.html Using the lung data set, data(lung) lung.wbs <- survreg( Surv(time, status)~ 1, data=lung, dist='weibull') curve(pweibull(x, scale=exp(coef(lung.wbs)),

Dear R users, Please can you help me with a relatively straightforward problem that I am struggling with? I am simply trying to plot a baseline survivor and hazard function for a simple data set of lung cancer survival where `futime' is follow up time in months and status is 1=dead and 0=alive. Using the survival package: lung.wbs <- survreg( Surv(futime, status)~ 1, data=lung,

I''ll step back to Xen stable, because of my network probs with Xen unstable. So I configured and compiled Xen 2.0.6, but I came to the following error after starting Xend: Exception starting xend: ''module'' object has no attribute ''VIRQ_DOM_EXC'' Traceback (most recent call last): File "/usr/lib/python/xen/xend/server/SrvDaemon.py", line 302,

Hi R-people, I am wanting to run Factorial ANOVA followed by Scheffe tests on some spatial subjective data. I'm comparing X-Y independent coordinates against x-y dependent coordinates. There are only four independent spatial coordinates that form a square. I am wondering whether I am doing the right thing, because there doesn't seem to be a simple way of doing this. I have attempted to

Let me modify my question about user-defined covariance structures for LME models: Can somebody tell me how I can see the code for the definition of the correlation structures that come with the NLME package. Specifically I like to see the code for the functions coef, corMatrix, and intialize for any of the pre-defined correlation structures, and use this as a template to define a new correlation

Hallo all, I`ve read a lot of things in this forum about an Excel export via R. It is no problem to export my data frames via write.table or write.xls (xls or csv), but some things are not very convenient for me: I always have to adjust the column with to see all the numbers or the text and there is no frame between the cells. And I missing the possibility to make some headers bold or coloured.

Hi All: the package "MuMIn" can be used to select the model based on AIC or AICc. The code is as follows: data(Cement) lm1 <- lm(y ~ ., data = Cement) dd <- dredge(lm1,rank="AIC") print(dd) If I want to select the model by BIC, what code do I need to use? And when to select the best model based on AIC, what the differences between the function "dredge" in

Hi, I would like to know how (if) I can extract some of the information from the summary of my nlme. at present, I get a summary looking something like this: > summary(fit.nlme) Nonlinear mixed-effects model fit by maximum likelihood Model: MLKYLD ~ W4(DIM, logA, B, C) Data: ADHIS.x0 AIC BIC logLik 265314 265401.6 -132647 Random effects: Formula: list(logA ~ 1 , B ~

Hello Instead of reading in group1.txt I want to read in groups1 for the first iteration of i, then groups2 for the second and so on. Obviously I can't use groups(i) but assume there is a way to do this. group<-read.table("C:/Data/April 2005/group1.txt",header=T) thanks in advance Meredith

I am interested in utilizing this so called "double hurdle" model in my study. We can write the model in the following way: if (z'a + u > 0 & x'b + e > 0) y = x'b + e, else y = 0 In the model, consumption y is the (left-) censored dependent variable. e and u are the normally distributed error terms. z'a is the participation equation and x'b is the

Hi I am working on corpora of automatically recognized utterances, looking for features that predict error in the hypothesis the recognizer is proposing. I am using the glm functions to do logistic regression. I do this type of thing: * logistic.model = glm(formula = similarity ~., family = binomial, data = data) and end up with a model: > summary(logistic.model) Call:

Hello, I've got an array defined as y <- rnorm(3000), dim(y) <- c(3, 1000). I'd like to produce a 1000-element vector z that is the mean of the corresponding elements of y (like z[1,1] <- mean(y[1,1], y[2,1], y[3,1])), but being new to R, I'm not sure how to do this for all elements at once (or, at least, simply). Any help is appreciated. Thanks, Tom

Hi all, If I run LDA on the same data (2-class classification) with default(no priors specified in the lda function) vs. "prior=c(0.5, 0.5)", the results are different. The (0.5, 0.5) priors give better 1-classify-to-1 rate, and the proportional priors(default, no priors specified in the lda function) give better 0-classify-to-0 rate, for both training and testing data sets. However,

I am fairly new at using R/programming in general so I apologize if I am leaving crucial parts of the puzzle out, but here goes. First and foremost this is the error I am receiving: Error in muPriors[priors[, 1:2]] <- priors[, 3] : NAs are not allowed in subscripted assignments This occurs only when I am using observational priors and some number of nominal variables, it does not

Dear R help, What am I doing wrong here? when I don't specify the priors it works just fine but when I specify the priors it breaks.? Does anyone know why and how I can fix it? ---- > N=20000 > ncontrol=ncases=50 > X <- as.matrix(rnorm(N,0,1)) > eta <- -5.3 + X * 1.7 > p <- exp(eta)/(1+exp(eta)) > Y <- rbinom(N,1,p) > controls <- sample(seq_len(N),

Hi everyone, I have 2 environments (2 different R sessions) as described below: Session 1: Name of the environment: "CrlmmInfo" Objects in the environment: index1: logical index - length 238304 index2: logical index - length 238304 priors: list of 4 - (matrix 6x6, 2 vectors of length 6, vector of length 2) - all num params: list of 4: centers [238304 x 3 x

Hi everyone, I have 2 environments (2 different R sessions) as described below: Session 1: Name of the environment: "CrlmmInfo" Objects in the environment: index1: logical index - length 238304 index2: logical index - length 238304 priors: list of 4 - (matrix 6x6, 2 vectors of length 6, vector of length 2) - all num params: list of 4: centers [238304 x 3 x

Hi, i installed rsync 3.0 on a Sun Solaris 10 Sparc Server and tried to Backup the Server via BackupPC the complete rsync command from the backup server is this: /usr/bin/ssh -q -x -l root IP /usr/bin/rsync --numeric-ids --perms --owner --group -D --links --hard-links --times --block-size=2048 --recursive --checksum-seed=32761 --log-file=/var/log/rsync.log --ignore-times . / in the log on

Dear all, I would like to add mixed effects in a multinomial model and I am trying to use MCMCglmm for that. The main problem I face: my data set consits of a trapping data set, where the observation at eah trap (1 or 0 for each species) have been aggregated per traplines. Therefore we have a proportion of presence/absence for each species per trapline. ex: ID_line mesh habitat Apsy Mygl

When I set this in master.conf: service imap { service_count = 5 } I see this error when two imap users log in: Nov 11 16:54:16 server dovecot[5432]: imap-login: Login: user=<user1>, method=PLAIN, rip=10.100.0.84, lip=10.80.0.163, pid=5573 Nov 11 16:54:31 server dovecot[5432]: imap-login: Login: user=<user2>, method=PLAIN, rip=::1, lip=::1, secured, pid=5573 Nov 11