search for: cstats

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...with algname. algname is nominal and there are 7 possibilities. There are two more nominal independents, "task" and "id", so my model is: dur ~ algname+task+id From the research I've done, a TukeyHSD seems to be what I need, so I do: TukeyHSD(aov(dur ~ algname+task+id, cstats), ordered=T) But I get this back: Error in rep.int(n, length(means)) : unimplemented type 'NULL' in 'rep' In addition: Warning messages: 1: non-factors ignored: task in: replications(paste("~", xx), data = mf) 2: non-factors ignored: id in: replications(paste("~&quo...

Dear R-listers, I am writing a function that will generate a simple plot for the user . The size of the window (that contains the plot) may not be what the user wants. Supposing the user adjusts the window to a size of her/his choice, I would like to be able to save the new window size and specify the plot to be of this new window size when the user calls my function again. My question is

Hello everyone, (This is my second question posted today on the R list). I am carrying out a competing risks analysis using the cuminc function...this takes the form: cuminc(ftime,fstatus,group) In my study, fstatus has 3 different causes of failure (1,2,3) there are also censored cases (0). "group" has two levels (0 and 1). I therefore have 6 different cumulative incidence curves:

...=50) |(N=50) | | +------------+-----------------+-----------------+-----------------+ |Sepal.Length|4.800/5.000/5.200|5.600/5.900/6.300|6.225/6.500/6.900| +------------+-----------------+-----------------+-----------------+ > system("cat a.tex") % latex.default(cstats, title = title, caption = caption, rowlabel = rowlabel, col.just = col.just, numeric.dollar = FALSE, insert.bottom = legend, rowname = lab, dcolumn = dcolumn, extracolheads = extracolheads, extracolsize = Nsize, ...) % \begin{table}[!tbp] \begin{center} \caption{Descriptive Statistics...

...imary Symptoms') table (Symptoms) table(symptom1,symptom2) f <- summary(treatment ~ age + sex + Symptoms, method="reverse", test=TRUE) g <- summary(treatment ~ age + sex + symptom1, method="reverse", test=TRUE) latex(g) > latex(g,file="") % latex.default(cstats, title = title, caption = caption, rowlabel = rowlabel, col.just = col.just, numeric.dollar = FALSE, insert.bottom = legend, rowname = lab, dcolumn = dcolumn, extracolheads = extracolheads, extracolsize = Nsize, ...) % \begin{table}[!tbp] \caption{Descriptive Statistics by treatment...

The attached patch is an updated version of my quota-rquotad patch for the quota plugin. Like quota-fs, it only does quota reporting, not enforcing or changing. It also only works on FreeBSD, and possibly other *BSD's. It lacks any and all configure-time tests to see if RPC is even supported, and hardcodes the (Free)BSD way of getting at the NFS server of an NFS mount. I'm mostly mailing

Dear R-listers, I have been using Snowfall (version 1.84) for parallel computing on a quad-PC running Windows 7 for a month or so without much problem. I started having problems runnnig R with Snowfall over our network when a new Novell client was installed for Windows 7. I experinenced network mapping problem with this new client. Our network colleague provided a fix which worked but a little

Dear R-listers, I am fitting bivariate mixed models for cost-effectiveness data of cluster randomized trials using lme in R. So I have individuals nested within clusters. My response variable is a vector with bivariate response (individual level costs and effects) stacked into a single column. The covariates in my models are a constant and a treatment term. They are response-specific, e.g. a

Dear R users lqmm version 1.02 is now on CRAN. lqmm provides commands for estimation of linear quantile regression models with random effects. Commands for independent and count data are also available. A few bugs have been fixed since lqmm 1.0. Updates of this package are (usually) made available on https://r-forge.r-project.org/R/?group_id=1396 prior to submission to CRAN. -- Marco Geraci

Dear R users lqmm version 1.02 is now on CRAN. lqmm provides commands for estimation of linear quantile regression models with random effects. Commands for independent and count data are also available. A few bugs have been fixed since lqmm 1.0. Updates of this package are (usually) made available on https://r-forge.r-project.org/R/?group_id=1396 prior to submission to CRAN. -- Marco Geraci

...y(Hmisc) test <- data.frame(a=sample(1:30,10),b=sample(c("good","bad","ugly"),10,replace=T)) base <- latex(summary(b~a,data=test,method="reverse"),ctable=T,file="test.tex") > readLines("test.tex")[1:5] [1] "% latex.default(cstats, title = title, caption = caption, rowlabel = rowlabel, col.just = col.just, numeric.dollar = FALSE, insert.bottom = legend, rowname = lab, dcolumn = dcolumn, extracolheads = extracolheads, extracolsize = Nsize, ...) "...

Dear All, I am a long time S user and now a convert to R. As part of my general work in time series I occasionally assist groups of econometricians and others in the finance fraternity. In particular, that community has invested a large amount of time and effort in writing specialised code in Gauss. I am unfamiliar with Gauss (although I have used Matlab which is, I understand, a comparable

...---------------schnapp------------------------->8--- Here's the resulting tex file (sorry for the long lines): --8<------------------------schnipp------------------------->8--- \documentclass{article} \usepackage{/usr/local/lib/R/share/texmf/Sweave} \begin{document} % latex.default(cstats, title = title, caption = caption, rowlabel = rowlabel, col.just = col.just, numeric.dollar = FALSE, insert.bottom = legend, rowname = lab, dcolumn = dcolumn, extracolheads = extracolheads, extracolsize = Nsize, ...) % \begin{table}[!tbp] \caption{foo bar\label{tab:foobar}} \begi...

Dear all, The variable selection in regression is usually determined by the training data using AIC or F value, such as stepAIC. Is there some R package that can consider both the training and test dataset? For example, I have two separate training data and test data. Firstly, a regression model is obtained by using training data, and then this model is tested by using test data. This process

Hello everyone, These are some questions about the 'pec' function in R. These questions deal with prediction error curves and their derivation. Prediction error curves are documented in, for example, "Efron-type measures of prediction error for survival analysis" by Gerds and Schumacher. I have detailed some syntax that I have used at the bottom of this email. The associated

Full_Name: Ruskin Chow Version: R 2.0.1 OS: Windows 2000 Submission from: (NULL) (203.169.154.66) Data imported from SAS using read.xport() in package foreign are converted to <NA> when the SAS data field consists of character strings that are only one character long. This is apparently a previously reported bug and perhaps fixed in some platform other than Windows (rw2001).Some