similar to: documentation for survival5?

I am new to R and would like to get some practice analyzing multilevel data. I wonder if anyone can point me to a sample data set and command lines that I might replicate for a sample session. I would then compare my output with HLM output. Any help is appreciated. ------ Harold C. Doran Director of Research and Evaluation New American Schools 675 N. Washington Street, Suite 220 Alexandria,

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]),

R Users I am a new user of R. I have sample weights that I would like to apply to some of the variables in my data set. Where can I go for information on how to do that? Richard

Dear R-help list members, I'm writing a package using tcltk. Thanks to Peter Dalgaard's excellent work on the tcltk package, almost everything has gone very smoothly. I'm stymied, however, by the following problem: I want to incorporate a bitmap image, stored as an xbm file, in a widget. To take a simple example, top <- tktoplevel() tkgrid(tklabel(top,

I've prepared preliminary versions of two packages that I plan eventually to contribute to CRAN: car (for "Companion to Applied Regression") is a package that provides a variety of functions in support of linear and generalized linear models, including regression diagnostics (e.g., studentized residuals, hat-values, Cook's distances, dfbeta, dfbetas, added-variable plots,

I've prepared preliminary versions of two packages that I plan eventually to contribute to CRAN: car (for "Companion to Applied Regression") is a package that provides a variety of functions in support of linear and generalized linear models, including regression diagnostics (e.g., studentized residuals, hat-values, Cook's distances, dfbeta, dfbetas, added-variable plots,

I've prepared preliminary versions of two packages that I plan eventually to contribute to CRAN: car (for "Companion to Applied Regression") is a package that provides a variety of functions in support of linear and generalized linear models, including regression diagnostics (e.g., studentized residuals, hat-values, Cook's distances, dfbeta, dfbetas, added-variable plots,

Dear r-devel list members, Dirk Eddelbuettel brought the following problem to my attention. The code is abstracted from the appendix on mixed models from my R and S-PLUS Companion: > set.seed(12345) # for reproducibility > library(nlme) Loading required package: lattice > data(MathAchieve) > data(MathAchSchool) > attach(MathAchieve) > mses <- tapply(SES, School,

Dear R list members, I'd like to produce rug plots at the bottom of panels in a trellis display (using the lattice package), but par("usr") doesn't return user coordinates for panels, and consequently rug fails, as the following example (suggested to me by Georges Monette) illustrates: > x <- rnorm(50) > y <- rnorm(50) > f <-

Hi All. I am quite a newbie to R. This is a next basic question. I have a matrix; > x <- matrix(1:10.,5) > x [,1] [,2] [1,] 1 6 [2,] 2 7 [3,] 3 8 [4,] 4 9 [5,] 5 10 I like to get a modified matrix as follows; [,1] [,2] [1,] 1 6 [2,] 2 7 [3,] 3 8 * 5 -> 40 [4,] 4 9 [5,] 5 10 The following expression does not work.

Hey, R-listers, I have a question about determining the orthogonal basis vectors. In the d-dimensinonal space, if I already know the first r orthogonal basis vectors, should I be able to determine the remaining d-r orthognal basis vectors automatically? Or the answer is not unique? Thanks for your attention. Fred

Dear Terry, Even the behaviour of lm() and glm() isn't entirely consistent. In both cases, singularity results in NA coefficients by default, and these are reported in the model summary and coefficient vector, but not in the coefficient covariance matrix: ---------------- > mod.lm <- lm(Employed ~ GNP + Population + I(GNP + Population), + data=longley) >

Dear R list members, I have a question about the proper use of the typsize and fscale arguments to nlm. I use nlm in my sem package to fit general structural-equation models, which entails maximizing a multinormal likelihood with respect to parameters that represent regression coefficients and covariances of variables. The magnitudes of these parameters can be very different. The

>>>>> Fox, John <jfox at mcmaster.ca> >>>>> on Thu, 14 Sep 2017 13:46:44 +0000 writes: > Dear Martin, I made three points which likely got lost > because of the way I presented them: > (1) Singularity is an unusual situation and should be made > more prominent. It typically reflects a problem with the > data or the

I can''t start r inside of emacs. When I try it says that it cannot find .Rhistory and then it says " This program ( pressumably Rterm) has performed an illegal operation and will be shut down.". How can I fix this problem? [[alternate HTML version deleted]]

Hallo Could anybody please help. I have a simple linear regression model with 5 predictors. I want to use "bootstrap residuals" to make inferences regarding beta(2)hat. After fitting the model y=b0+b1+b2+b3+b4+b5 I tried the following: mod <- lm(y ~ x1+x2+x3+x4+x5) res <- resid(mod) pred <- predict(mod) Now, I have tried boot(res, lm(res+pred ~ x1+x2+x3+x4+x5)$coef[3],

Dear Mr. Therneau, Mr. Fox, or to whoever, who has some time... I don't find a solution to use the "survfit" function (package: survival) for a defined pattern of covariates with a Cox-Model including a time dependent interaction term. Somehow the definition of my "newdata" argument seems to be erroneous. I already googled the problem, found many persons having the

Is there an eloquent solution to re-assign vector element values? I have a vector which contains chemical data, some of them are "flagged" as non-detected values by their negative values. I can find the statistics on the positive values in vector "v" simply by typing: >v<- c(5,5,-3,-3,7,8,10) > v[(v>0)] [1] 5 5 7 8 10 I can also convert to positive values by

I"ve got a data frame with a selection of columns I want to compute a rank-order correlation matrix from without disturbing the original data frame. foo[,c("a","b","d","f","g")] What I wanted to do, intuitively, was: > cor(rank(foo[,c("a","b","d","f","g")])) but rank in that context

Dear all can anybady tell me how to seperate R's input and output screen?I mean just like SAS or some others, the commands does not mixed with results. thank you