similar to: Problem accessing functions in package 'roxygen'

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

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

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 need to construct confidence intervals for the binomial variance. This is the usual estimate v = x*(n-x)/n or its unbiased counterpart v' = x*(n-x)/(n-1) where x = binomial number of successes observed in n Bernoulli trials from proportion p. The usual X^2 method for variance confidence intervals will not work, because of the strong non-normal character of the sampling

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 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 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

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,

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

Hello useRs, I'm trying to use the Roxygen package. Here my code file : #' A packge to check Roxygen's sanity #' @name helloRoxygen-package #' @docType package NA And my R code to generate the package : library(roxygen) package.skeleton("helloRoxygen", code_files = "roxy.r", force = T) roxygenize("helloRoxygen", "helloRoxygen",

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

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

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 designing an interlaboratory validation study for a presence/absence alternative method test kit vs. a presence/absence reference method test kit. There will be 10 laboratories conducting tests using both methods. In each laboratory, there will be 5 specimens tested, each of the 5 specimens twice by both methods (alternative, standard). The total number of data are 10 x 5 x 4 = 200.

I'm trying to convert a dataset from the time-independent analysis form > head(addicts) id clinic status survtime prison meth clinic01 1 1 1 1 428 0 50 1 2 2 1 1 275 1 55 1 3 3 1 1 262 0 55 1 into the "counting data format" necessary to perform extended Cox regression. I got

Another sample problem: In the Windows version of R-2.5.0, data(GHQ,package='HSAUR') layout(1) GHQ_glm_1<- glm(cbind(cases,non.cases) ~ GHQ, data=GHQ, family=binomial()) summary(GHQ_glm_1) yfit_glm_1<- predict(GHQ_glm_1, type='response') layout(1) plot(probs ~ GHQ,pch=1,col=1,ylab='Probability(Case)', data=GHQ) lines(yfit_glm_1 ~ GHQ, pch=3,col=3, data=GHQ)

[Corrected the model formula to include "method".] I am designing an interlaboratory validation study for a presence/absence alternative method test kit vs. a presence/absence reference method test kit. There will be 10 laboratories conducting tests using both methods. In each laboratory, there will be 5 specimens tested, each of the 5 specimens twice by both methods (alternative,

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.

Consider the following simple example using R-2.9.0 and 'perm' 2.9.1: > require('perm') > p<- c(15,21,26,32,39,45,52,60,70,82) > g<- c('y','n','y','y', rep('n',6)) #Patients ranked 1,3,4 receive treatment > permTS(p ~ g, alternative = 'two.sided', method='exact.ce') #find p-value by complete enumeration