similar to: names attribute of data.frames after rbind

Hi, I have come across the following problem which seems to be a scoping issue but I'm at a loss to see why this is so or to find a good workaround. Suppose I have a function to get a prediction after model selection using the step function. step.pred <- function(dat, x0) { fit.model <- step(lm(y~., data=dat), trace=F) predict(fit.model, x0, se.fit=T) } This function works

Hi, Suppose that I have a list where each component is a list of two matrices. I also have a vector of weights. How can I collapse my list of lists into a single list of two matrices where each matrix in the result is the weighted sum of the corresponding matrices. I could use a loop but this is a nested calculation so I was hoping there is a more efficient way to do this. To help clarify,

Hi, I'm using R 1.7.1 on a Windows 2K computer. For some reason erase.screen does not seem to be working correctly. That is it does not erase the requested screen. That is the old graphic is still visible and any subsequent graphics are superimposed over it making them impossible to read. What is worse is that they print out this way also. The same behaviour happens with a Windows XP

Hi, Using R1.9.1 for Windows with Windows 2000 (also XP), I have come across the following problem that I have not seen before and am at a loss to explain. If I create an object using rlm from library(MASS) and then save the workspace, I cannot reopen it using RGUI by double clicking on the .RData file as I normally do. If I double click on the icon, R starts and then gives the following

Dear All, Anyone has some idea how to implement 632 estimator and leave-one out bootstraping method by using boot package. I know the bootstrap package has this function, but it sounds not very flexible for my project. Thanks, Jim

Hello, I am trying to carry out a bootstrap analysis (using the boot package) on a table and cannot work out how to get the results I need! I have a table ("d2") with 4 columns: "ID_code", "Age", "Quarter" and "StomWt". Age (0-5) and Quarter (1-4) are my strata Therefore I wish to estimate the confidence intervals for the mean StomWt for each Age

Hello, This is the code that is giving me problems >library(boot) >data<-read.table("test",header=FALSE,sep="\t",row.names=1) >data V2 V3 V4 A 5 8 9 B 12 54 89 C 65 89 23 D 32 69 44 E 21 84 97 F 33 59 71 G 16 45 93 H 2 46 55 I 22 33 88 >resample <- function(x,index) { sample(data,replace=TRUE) } dist<-boot(data,resample,R=1000)

Hi, I just came across an error that I haven't seen before and hope someone can help me. When I try to save my current workspace (using save.image or on quitting) I get the error message Error in save.image() : recursive default argument reference Does anyone know what is going on and how I can quit R without losing the contents of my current workspace? I am running R2.0.1 on a windows

Question: How can I get access to the bias value of a boot statistic? Details: Boot function: boot(data, statistic, R, sim="ordinary", stype="i", strata=rep(1,n), L=NULL, m=0, weights=NULL, ran.gen=function(d, p) d, mle=NULL, ...) When I create an object, containing the bootstrap statistic (object <- boot (....))I can call it and will get an output

Hello, I downloaded R today because I was told it has very good bootstrapping abilities. What I need to do is to program it (or use an existing program) to bootstrapping test for multimodality using nonparametric kernel density estimates as proposed by Efron and Tibshirani (1993). If anyone can get me started, I will be immensely grateful. Thanks. Sangeeta

What in the world am I missing?? > x<-rnorm(20) > mean(x) [1] -0.2272851 > results<-boot(x,mean,R=5) > results[2] $t [,1] [1,] -0.2294562 [2,] -0.2294562 [3,] -0.2294562 [4,] -0.2294562 [5,] -0.2294562 Jeff Morris Ortho-Clinical Diagnostics A Johnson & Johnson Co. Rochester, NY Tel: (585) 453-5794 [[alternative HTML version deleted]]

I am trying to bootstrap the parameters for a model that is estimated through the optim() function and find that when I make the call to boot, it runs but returns the exact same estimate for all of the bootstrap estimates. I managed to replicate the same problem using a glm() model but was able to fix it when I made a call to the variables as data frame by their exact names. But no matter how I

I think I am doing something wrong when I try to bootstrap R square obtained from lm. My code is included below. No matter how many times I run the simulation, I always get exactly the same result, the bias and std.error are always zero. I would think that these values should be non-zero. I would appreciate any suggestions as to what I am doing wrong, or perhaps what I fail to understand. R 2.1.0

Hi, I using the boot package 1.2-20 on R 2.0.1. My statistics function estimates 6 parameters. In a small percentage of resampled data sets my statistics function doesn't produce an estimate for one parameter and the boot function stops with an error. I can write an ifelse(exists('parameter.estimate'), parameter.estimate, NA) statement within the statistic function to substitute

Hi All I am having 2 very long character strings (550chars) and I want to put them as expressions together with c(). The problem is that I also get these double-quotes, as seen below in 'fct'. How can I remove these double-quotes? I tried as.name() but it did not work (because of size?). These are creating trouble with subsequent programs, which I tested with strings that for some

Dear R list, I have the following data: set.seed(1) n <- 5 # number of subjects tt <- 3 # number of repeated observation per subject numco <- 2 # number of covariates x <- matrix(round(rnorm(n*numco),2), ncol=numco) # the actual covariates x > x [,1] [,2] [1,] -0.63 -0.82 [2,] 0.18 0.49 [3,] -0.84 0.74 [4,] 1.60 0.58 [5,] 0.33 -0.31 I need to form a matrix

Dear R-helpers, I am struggling with an optimization problem at the moment and decided to write the list looking for some help. I will use a very small example to explain what I would like to. Thanks in advance for your help. We would like to distribute resources from 4 warehouses to 3 destinations. The costs associated are as follows: Destination >From 1 2 3 Total

II am translating some gauss code into R, and gauss has a matrix product function called the horizontal direct product (*~), which is some sort of variant on the Kronecker product. For example if x is 2x2 and y is 2x2 the horizontal direct product, z, of x and y is defined (in the Gauss manual) as: row 1 = x11*y11 x11*y12 x12*y11 x12*y12 row 2 = x21*y21 x21*y22 x22*y21 x22*y22 Or in R

I have a dataset that is basically structureless. Its dimension varies from row to row and sep(s) are a mixture of tab and semi colon (;) and example is HEADER1 HEADER2 HEADER3 HEADER3 A1 B1 C1 X11;X12;X13 A2 B2 C2 X21;X22;X23;X24;X25 A3 B3 C3 A4 B4 C4 X41;X42;X43 A5 B5 C5 X51 etc., say. Note that a blank

Dear List, I have a theoretical question related to epidemiological data analysis: If the treatment status (tx = 0,1) changes over time for the patients in a non-randomized cohort, is there a way to estimate the treatment effect? (i.e., after joining the study, some patients may have to wait for a period of time before receiving the treatment, i.e., the situation of patient with id == 2 for the