similar to: R function which finds confidence interval for binomial variance

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 have a data frame: > head(df) Time Temp Conc Repl Log10 1 0 -20 H 1 6.406547 2 2 -20 H 1 5.738683 3 7 -20 H 1 5.796394 4 14 -20 H 1 4.413691 5 0 4 H 1 6.406547 7 7 4 H 1 5.705433 > str(df) 'data.frame': 177 obs. of 5 variables: $ Time : Factor w/ 4 levels

How does one ideally handle and display multidimenstional contingency tables in R v. 2.6.2? E.g.: > prob1<- data.frame(victim=c(rep('white',4),rep('black',4)), + perp=c(rep('white',2),rep('black',2),rep('white',2),rep('black',2)), + death=rep(c('yes','no'),4), count=c(19,132,11,52,0,9,6,97)) > prob1 victim perp

Are there any packages in R that reproduce the package "resample" of S-Plus? The sample() function in R doesn't provide equivalent flexibility of bootstrap() and bootstrap2(). ================================================================ Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: ral at lcfltd.com Least Cost Formulations, Ltd. URL: http://lcfltd.com/ 824

I have Vista Home with R-2.9.0, and installed and tried to test the package 'roxygen': > utils:::menuInstallPkgs() trying URL 'http://lib.stat.cmu.edu/R/CRAN/bin/windows/contrib/2.9/roxygen_0.1.zip' Content type 'application/zip' length 699474 bytes (683 Kb) opened URL downloaded 683 Kb package 'roxygen' successfully unpacked and MD5 sums checked The

I have R 2.6.2, and have tried downloading and installing the package "tseries". I get the same error when I use two different mirror sites: > utils:::menuInstallPkgs() trying URL 'http://cran.mirrors.hoobly.com/bin/windows/contrib/2.6/tseries_0.10-14.zip' Content type 'application/zip' length 400799 bytes (391 Kb) opened URL downloaded 391 Kb package

I'm using R 2.10.1 with the latest version of all packages (updated today). I'm confused as to why I'm getting a hard singularity in a simple set of experimental data: > blots ID Lot Age Conc 1 1 A 3 4.44 2 2 A 3 4.56 3 3 B 41 4.03 4 4 B 41 4.57 5 5 C 229 4.49 6 6 C 229 4.66 7 7 D 238 3.88 8 8 D 238 3.93 9 9 E 349 4.43 10 10 E 349

I'm trying to figure out how to carry out a Poisson regression fit to longitudinal data with a gamma distribution with unknown shape and scale parameters. I've tried the 'lmer4' package's glmer() function, which fits the Poisson regression using: library('lme4') fit5<- glmer(seizures ~ time + progabide + timeXprog + offset(lnPeriod) + (1|id), data=pdata,

Based on simulations, I've come up with a simple function to compute the confidence interval for the variance of the binomial variance, where the true variance is v = rho*(1-rho)/n where rho = true probability of success and n = # of trials. For x = # successes observed in n trials, p = x / n as usual. For p < 0.25 or p > 0.75, I use the proportion-based transformed confidence

Hello all! I am once again analyzing patient survival data with chronic liver disease. The severity of the liver disease is given by a number which is continuously variable. I have referred to this number as "meld"--model for end stage liver disease--which is the result of a mathematical calculation on underlying laboratory values. So, for example, I can generate a Kaplan-Meier plot

Given a set of data with a number of variables plus a response, I'd like to obtain a randomized subset of the rows such that the marginal proportions of each variable are maintained closely in the subset to that of the dataset, and possibly maintaining as well the two-factor interaction marginal proportions as well for some pairs. This must be a common problem in data mining, but I

I don't seem to be able to put any syntax into identify() that gets it to work with "lattice" cloud() graph: layout(1) require('lattice') cloud(g3 ~ g1 + g2, data=gapp, col = "blue", xlab='G1 Score', ylab='G2 Score', zlab='G3 Score', main='3D Plot for Applicants') identify(gapp[,2:4], labels=gapp$ID) Here g1 is gapp[,2], g2

As I was working through elementary examples, I was using dataset "plasma" of package "HSAUR". In performing a logistic regression of the data, and making the diagnostic plots (R-2.5.0) data(plasma,package='HSAUR') plasma_1<- glm(ESR ~ fibrinogen * globulin, data=plasma, family=binomial()) layout(matrix(1:4,nrow=2)) plot(plasma_1) I find that data points

Hi, Im carrying out some Bayesian analysis using a binomial response variable (proportion: 0 to 1), but most of my observations have a value of 0 and many have very small values (i.e. 0.001). I'm having troubles getting my MCMC algorithm to converge, so I have decided to try normalising my response variable to see if this helps. I want it to stay between 0 and 1 but to have a larger range

Hi, I have a dataset that has 2 groups of samples. For each sample, then response measured is the number of success (no.success) obatined with the number of trials (no.trials). So a porportion of success (prpop.success) can be computed as no.success/no.trials. Now the objective is to test if there is a statistical significant difference in the proportion of success between the 2 groups of

Hi everybody, somehow i dont get the shapiro wilk test for normality. i just can?t find what the H0 is . i tried : shapiro.test(rnorm(5000)) Shapiro-Wilk normality test data: rnorm(5000) W = 0.9997, p-value = 0.6205 If normality is the H0, the test says it?s probably not normal, doesn ?t it ? 5000 is the biggest n allowed by the test... are there any other test ? ( i know qqnorm

I need to be able to run a generalized linear model with a log() link and a Weibull family, or something similar to deal with an extreme value distribution. I actually have a large dataset where this is apparently necessary. It has to do with recovery of forensic samples from surfaces, where as much powder as possible is collected. This apparently causes the results to conform to some type

I am trying to compute bootstrap confidence intervals for a sample using R 2.5.1 for Windows. I can get "Normal", "Basic", "Percentile", "BCa" and "ABC" from boot.ci() and boot() in the Davison & Hinkley "boot" package. But I can't figure out how to use tilt.boot() to get the "tilting" confidence interval.

d = c(0L, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0L, 0L, 7375L, NA, NA, 17092L, 0L, 0L, 32390L, 2326L, 22672L, 13550L, 18285L) boot.out <-boot(d, mean, R=1000, sim="permutation") Error in mean.default(data, original, ...) : 'trim' must be numeric of length one I know that I am missing something but I can't figure it out. thanks stephen -- Let's not spend our

I'm using R 2.15.1 on a 64-bit machine with Windows 7 Home Premium. Sample problem (screwy subscripted syntax is a relic of edited down a more complex script): > N <- 25 > s <- rlnorm(N, 0, 1) > require("boot") Loading required package: boot > v <- NULL # hold sample variance estimates > i <- 1 > v[i] <- var(s) # get sample variance >