similar to: Which system.time() component to use?

Hi, I have a right-censored (positive) random variable (e.g. failure times subject to right censoring) that is observed for N subjects: Y_i, I = 1, 2, ..., N. Note that Y_i = min(T_i, C_i), where T_i is the true failure time and C_i is the censored time. Let us assume that C_i is independent of T_i. Now, I would like to generate another random variable U_i, I = 1, 2, ..., N, which is

Hi, I would like to know how to capture the console output from running an algorithm for further analysis. I can capture this using capture.output() but that yields a character vector. I would like to extract the actual numeric values. Here is an example of what I am trying to do. fr <- function(x) { ## Rosenbrock Banana function on.exit(print(f)) x1 <- x[1] x2 <- x[2]

Dear R group, I have a data frame which contains data on preferences on 7 items (ranks 1 through 7) listed by each participant. I would like to tabulate this in a 7x7 table where the rows would be the items and the columns would be the number of times that item received a particular rank. I tried doing this by creating a matrix by "rbind"ing each vector obtained using

Hi, I would like to know whether there exist algorithms to compute the coefficients or, at least, the degree of the minimal polynomial of a square matrix A (over the field of complex numbers)? I don't know whether this would require symbolic computation. If not, has any of the algorithms been implemented in R? Thanks very much, Ravi. P.S. Just for the sake of completeness, a

Hi, Currently, the "base" has colSums, colMeans. It seems that it would be useful to extend this to also include colMin, colMax (of course, rowMin and rowMax, as well) in order to facilitate faster computations for large vectors (compared to using apply). Has this been considered before? Please forgive me if this has already been discussed before. Thanks, Ravi Ravi Varadhan, Ph.D.

Hi, I apologize for the previous posting, where the message was not formatted properly. Here is a better version: I have written the following function to check whether a vector has elements satisfying monotonicity. is.monotone <- function(vec, increase=T){ ans <- TRUE vec.nomis <- vec[!is.na(vec)] if (increase & any(diff(vec.nomis,1) < 0, na.rm=T)) ans <- FALSE

Hi, Please see the attached contour plot (I am sorry about the big file). This was created using the vis.gam() function in "mgcv" package. However, my question is somewhat broader. In generating this figure, I first created the contours using vis.gam() and then I plotted the points. These point are plotted on top of the contours so that some of the contour lines are only partially

Try this: Age_log_model = glm(Arthrose ~ Alter, data=x, start=c(-1, 0), family=quasibinomial(link = log)) Ravi Ravi Varadhan, Ph.D. Assistant Professor The Center on Aging and Health Division of Geriatric Medicine & Gerontology Johns Hopkins University rvaradhan@jhmi.edu<mailto:rvaradhan@jhmi.edu> 410-502-2619 [[alternative HTML version deleted]]

Hi Reuben, I am puzzled to note that the gradient check in "optimx" does not work for you. Can you send me a reproducible example so that I can figure this out? John - I think the best solution for now is to issue a "warning" rather than an error message, when the numerical gradient is not sufficiently close to the user-specified gradient. Best, Ravi.

Hi, How can I get information on how many times a particular package has been downloaded from CRAN? Thanks, Ravi. ------------------------------------------------------- Ravi Varadhan, Ph.D. Assistant Professor, Division of Geriatric Medicine and Gerontology School of Medicine Johns Hopkins University Ph. (410) 502-2619 email: rvaradhan@jhmi.edu<mailto:rvaradhan@jhmi.edu> [[alternative

Hi, I am running a simulation study with the survival::coxph. Some of the simulations result in problematic fits due to flat partial likelihood. So, you get the warning message: Warning message: In fitter(X, Y, strats, offset, init, control, weights = weights, ... : Loglik converged before variable 2 ; beta may be infinite. How can I keep track of the simulations which yield any kind of

Hi, I have a simple problem that I would appreciate getting some tips. I am using the "truehist" function within an "apply" call to plot multiple histograms. I can't figure out how to get truehist to use the column names of the matrix as the labels for the x-axis of the histograms. Here is a simple example: X <- matrix(runif(4000),ncol=4) colnames(X)

Hi, I would like to solve a system of coupled ordinary differential equations, where there is a delay (time lag) term. I would like to use the "lsoda" function "odesolve" package. However, I am not sure how to specify the delay term using the syntax allowed by odesolve. Here is an example of the kind of problem that I am trying to solve: > library(odesolve)

Hi, I am trying to do the following computation: p <- rep(0, n) coef <- runif(K+1) U <- matrix(runif(n*(2*K+1)), n, 2*K+1) for (i in 0:K){ for (j in 0:K){ p <- p + coef[i+1]* coef[j+1] * U[,i+j+1] } } I would appreciate any suggestions on how to perform this computation efficiently without the "for" loops? Thank

Hi, I have a data file, certain lines of which are character fields. I would like to skip these rows, and read the data file as a numeric data frame. I know that I can skip lines at the beginning with read.table and scan, but is there a way to skip a specified sequence of lines (e.g., 1, 2, 10, 11, 19, 20, 28, 29, etc.) ? If I read the entire data file, and then delete the character

Hi, Suppose I have a multivariate response Y (n x k) obtained at a set of predictors X (n x p). I would like to perform a linear regression taking into consideration the covariance structure of Y within each unit - this would be represented by a specified matrix V (k x k), assumed to be the same across units. How do I use "lm" to do this? One approach that I was thinking of

Dear UseRs!, Everything about UseR! 2010 was terrific! I really mean "everything" - the tutorials, invited talks, kaleidoscope sessions, focus sessions, breakfast, snacks, lunch, conference dinner, shuttle services, and the participants. The organization was fabulous. NIST were gracious hosts, and provided top notch facilities. The rousing speech by Antonio Possolo, who is the chief

Hi, I apologize for this repeat posting, which I first posted yesterday. I would appreciate any hints on solving this problem: I have two matrices A (m x 2) and B (n x 2), where m and n are large integers (on the order of 10^4). I am looking for an efficient way to create another matrix, W (m x n), which can be defined as follows: for (i in 1:m){ for (j in 1:n) { W[i,j] <-

Hi, I am computing the Nelson-Aalen (NA) estimate of baseline cumulative hazard in two different ways using the "survival" package. I am expecting that they should be identical. However, they are not. Their difference is a monotonically increasing with time. This difference is probably not large to make any impact in the application, but is annoyingly non-trivial for me to just

Hi, I have noticed that there is a change in the use of ellipses or . in R versions 2.6.1 and later. In versions 2.5.1 and earlier, the . were always at the end of the argument list, but in 2.6.1 they are placed after the main arguments and before method control arguments. This results in the user having to specify the exact (complete) names of the control arguments, i.e. partial matching is