similar to: imputation of sub-threshold values

Dear R helpers, I wish to use the "sum" operator for each row of a data frame. However, it appears that the operator acts on the entire data frame, over all columns. What is the best way to obtain row- wise operation? The following code shows my attempts so far, and their problems:- test1=array(rbinom(120,1,0.5),c(20,3)) test1[,3]=NA sum(test1[,1:2]) test1[,3][sum(test1[,1:2])>=2]=1

Dear R helpers, I would like to ask why polr occasionally generates results that look very odd. I have been trying to compare the power of proportional odds logistic regression with the Wilcoxon test. I generated random samples, applied both tests and extracted and compared the p-values, thus:- library(MASS) c1=rep(NA,100); c2=c1 for (run in 1:100) { dat=c(rbinom(20,12,0.65),rbinom(20,12,0.35))

Oh dear - I joined the R help mailing list last week, in order to ask a specific question. I did not realise that I would start to receive all e-mails to and from the mailing list. Is there a way of letting me receive only the answers to my own questions? If not, then can you remove me from the mailing list? Thanks, Jonathan Williams Jonathan Williams OPTIMA Radcliffe Infirmary Woodstock Road

I am trying to analyse recurrent failure times using survfitr from the survrec package. To do this, I need to "Create a survival recurrent object" using Survr. But, when I do this, I get an error "Error in Survr(r1d[, 1], r1d[, 5], r1d[, 6]) : data doesn't match". Here, r1d[,1] is the identifier for each case, r1d[,5] is the time of recurrence, r1d[,6] is the status

Hi Helpers, Does anyone know how to read jpeg, bmp or png files into R? I have some photos of brain scans and I want to quantify some aspects of their size. I might be able to do this with the 'locator' function, if I could figure out how to read the files in and make them into an image that I can display on the R Windows device. I have experimented with readBin, using a simple

Jonathan, Try making a list instead of an array. See ?list. Also, did you look into random forests? I'm not sure what you want to do, but there might be methods there to do some of the work for you. Sean On 3/19/04 1:12 PM, "Jonathan Williams" <jonathan.williams at pharmacology.oxford.ac.uk> wrote: > I would like to collect the trees grown by rpart fits in an array,

Dear R helpers, I have a curious problem, which is that a program I have written in R crashes R, unpredictably. When I say that the program crashes R, I mean that it causes R to terminate, completely. But, the operating system (Windows) continues OK. The program loops around a randomForest regression. I am trying to use randomForest to predict the diagnosis for a set of patients with dementia. I

Jonathan, Is there any possibility for you to send me the data, and/or the code? I and others have seen similar problems before, but not reproducible on the current version, at least on the NT and Linux boxes that I have access to. As Thomas said, it's difficult to nail these kinds of problems. Having reproducible data and code, at least, can help. (It's no help that I'm no expert

I have a time series of data from an electroencephalogram (EEG). I wish to filter the data to get rid of 50Hz mains 'hum'. I have 'designed' a combination bandpass and notch filter using a web- site. The site returns the filter in "ANSI C" source code. It is:- /* Digital filter designed by mkfilter/mkshape/gencode A.J. Fisher Command line:

Dear R-helpers, I want to try to extract residuals from a multi-level linear mixed effects model, to correlate with another variable. I need to know which residuals relate to which experimental units in the lme. I can show the labels that relate to the experimental units via the command ranef(fit0)$resid which gives: 604/1/0 -1.276971e-05 604/1/1 -1.078644e-03 606/1/0 -7.391706e-03 606/1/1

Dear R-helpers, I am having difficulty making R plot only integer labels on the x-axis of a simple graph. I want to plot the median values of a score on each of three occasions. Non-integer occasions are impossible. But, R keeps labelling the x-axis with half-occasions, despite my attempts to stop this using the "xaxs" and "xaxp" parameters of 'plot'. p1=c(1,2,3);

I am having great difficulty plotting what should be a simple graph. I have measured 1 'y' and 5 'x' variables in each of two groups. Linear regression shows significant differences in the slopes of the regression for each 'x' variable between the two groups. All that I want to do is to plot one graph that shows the scatterplot for the three groups (each group represented

Dear R-helpers, I have found a slightly annoying problem when trying to plot lines on graphs. I first created my data using tapply, thus:- y1=as.vector(fit1$coef$random$id) x1=tapply(o1,id,median,na.rm=T) x2=tapply(o2,id,median,na.rm=T) #then I plot the data, thus:- plot(x1[x2==0],y[x2==0]) #if I now try to fit the linear regression, R 'hangs up'

I'm looking for a package that has a start-of-the-art method of imputation of missing values in a data frame with both continuous and factor columns. I've found transcan() in 'Hmisc', which appears to be possibly suited to my needs, but I haven't been able to figure out how to get a new data frame with the imputed values replaced (I don't have Herrell's book). Any

I am a new user of R for Windows, enthusiast about the many functions of the Design and Hmisc libraries. I combined the results of a Cox regression model after multiple imputation (of missing values in some covariates). Now I got my vector of coefficients (and of standard errors). My question is: How could I use directly that vector to run programs such as 'nomogram', 'calibrate',

I am doing multiple imputation with Hmisc, and can't figure out how to replace the NA values with the imputed values. Here's a general ourline of the process: > set.seed(23) > library("mice") > library("Hmisc") > library("Design") > d <- read.table("DailyDataRaw_01.txt",header=T) > length(d);length(d[,1]) [1] 43 [1] 2666

Hi, I want to impute the missing values in my data set multiple times, and then combine the results (like multiple imputation, but manually) to get a mean of the parameter(s) from the multiple imputations. Does anyone know how to do this? I have the following script: y1 <- rnorm(20,0,3) y2 <- rnorm(20,3,3) y3 <- rnorm(20,3,3) y4 <- rnorm(20,6,3) y <- c(y1,y2,y3,y4) x1 <-

Hello, I have a general question about combining imputations as well as a question specific to the rms and Hmisc packages. The situation is multiple regression on a data set where multiple imputation has been used to give M imputed data sets. I know how to get the combined estimate of the covariance matrix of the estimated coefficients (average the M covariance matrices from the individual

I have always avoided missing data by keeping my distance from the real world. But I have a student who is doing a study of real patients. We're trying to test regression models using multiple imputation. We did the following (roughly): f <- aregImpute(~ [list of 32 variables, separated by + signs], n.impute=20, defaultLinear=T, data=t1) # I read that 20 is better than the default of

  I have timeseries data for some factors, and some missing values are there in those factors, I want impute those missing values without disturbing the distribution of that factor, and maintaining the correlation with other factors. Pl. suggest me some imputation methods. I tried some functions in R like aregImpute, transcan. After the imputation I am unable to retrive the data with imputed