similar to: Mult Samba incarnations on aliased IPs

Hola a todos! Tengo algunos problemas con el tamaño de la matriz de términos que obtengo. Los comandos que utilizo son los siguientes: # carga librerias library(tm) library(wordcloud) library(Rstem) library(Snowball) # lee el documento UTF-8 y lo convierte a ASCII txt <-

Martin, I have an objection in principle to anything that has the side-effect of clobbering something in the global environment, even something as innocuous looking as "old.par". I certainly object to putting something like that into a public library, however clever and useful the code might be. It just is not safe. As a quick way round this I suggest the following (R only) solution:

I have a largish dataset (1025) with around .15 of the data missing at random overall, but more like .25 in the dependent variable. I am interested in modelling the data using quantile regression, but do not know how to do this with multiply imputed data (which is what the dataset seems to need). The original plan was to use qr (or whatever) from the quantreg package as the 'fitter'

Hi all (and especially Frank), I'm trying to use x=T, y=T in order to run a validated stepwise cox regression in rsm, having multiply imputed using mice. I'm coding model.max<-fit.mult.impute(baseform,cph,miced2,dated.sexrisk2,x=T,y=T) baseform is baseform<-Surv(si.age,si=="Yes")~ peer.press + copy.press + excited + worried + intimate.friend + am.pill.times +

Hello, I would like to use the aregImpute and fit.mult.impute to impute missing values for my dataset and then conduct logistic regression analyses on the data, taking into account that we imputed values. I have no problems imputing the values using aregImpute, but I am getting an error at the fit.mult.impute stage. Here is some sample code (I actually have more observations and variables to

The enclosed zip has some test files for both LLVM and Clang, to go along with the last mult-alt patch I submitted to the list. You'll note that some of the code is commented out for various problems not directly related to the mult-alt stuff. Though I worked on some additional versions for platforms not included here in the LLVM tests, they have various problems with the lowering, but which

I have fitted a proportional odds model, but would like to compare it to a continuation ratio model. However, I am unable to fit the CR model _including_ imputated data. I guess my troubles start with settuping the data for the CR model. Any hint is appreciated! Christian library(Hmisc) library(rms) library(mice) ## simulating data (taken from rms::residuals.lrm) set.seed(1) n <- 400 age

Dear all, I used fit.mult.impute in Dr. Harrell's Design package to fit a cox ph regression model on five imputed datasets, where all missing predictors were filled by multiple imputation using R package Mice. Are there any functions able to do bootstrapping or cross-validation for the aggregated model? I tried function 'validate' and 'calibrate' in Design package, but

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

Hi! Does anyone know how to do the test for goodness of fit of a logistic model (in rms package) after running fit.mult.impute? I am using the rms and Hmisc packages to do a multiple imputation followed by a logistic regression model using lrm. Everything works fine until I try to run the test for goodness of fit: residuals(type=c("gof")) One needs to specify y=T and x=T in the fit. But

Sorry everybody, temporary dyslexia. Sent from my BlackBerry wireless smartphone

Hi (sorry I do this question again, but my computer is block and I lost all my e-mail) I will install samba 3.0.10 in solaris 9/10. I would like to know if this version of samba is multi task. Today I use the samba 2.2.2 in solaris 8 (server with 2 processor), bat the "sons process of samba" work only one processor (100%) and the other processor be empty (0%) [the "sons

Hello, Does asterisk func odbc support multi query? I'm executing stored procedure which returns two tables. With tsql command I can see both tables. But asterisk only shows the first. My database is MSSQL. Maybe there is workaround... Thanks -- Best Regards, Giedrius -------------- next part -------------- An HTML attachment was scrubbed... URL:

This has a stats question and a R question. I am sure there are many core statisticians here how would know the answer to this simple question. In determining the significant comparisons using the methods in multcomp, the ones that are designated as significant are the ones that do not intersect the zero line. What is the physical meaning of this and why are those considered significant? I can

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

I tried multiple imputation with aregImpute() and fit.mult.impute() in Hmisc 3.8-3 (June 2010) and R-2.12.1. The warning message below suggests that summary(f) of fit.mult.impute() would only use the last imputed data set. Thus, the whole imputation process is ignored. "Not using a Design fitting function; summary(fit) will use standard errors, t, P from last imputation only. Use

Greetings, I am attempting to run a function, which produces a vector and requires two input variables, across two nested factor levels. I can do this using by(X, list(factor1, factor2), function), however I haven't found a simple way to extract the list output into an organized vector form. I can do this using nested loops but it isn't exactly an optimal approach. Thank you

Hello Sirs, i have a problem with Samba + LDAP, and i haven't found any howto, doc, or faq that helped me to figure it out. How do i have user/group policies using Samba + LDAP, for example, i can have the policy Bad Lockout Attempt, using tdbsam, but i dont find the way to add/enable that policy using ldapsam, i expect you can help me, or told me where can i read, thanks a lot. Federico

I'm loading a nicely formatted csv file. ? ? #!/usr/bin/env Rscript ? ? kpi <- read.csv( ? ? ? # This is a dump of the username, date_joined and last_login columns ? ? ? # from the auth_user Django table. ? ? ? 'data/2012-04-23.csv', ? ? ? colClasses = c('character') ? ? ) ? ? print(kpi[sample(nrow(kpi), 3),2:3]) Here's what the three rows I printed look like. ? ? ? ?

There are three sets of changes that would benefit stable/7. Namely, there are 1. Improvements for the UFS unmount or rw->ro remount, that perform suspension during the operation. The changes depend on the the suspension mechanism path, that introduced the suspension owner, and added new VFS OP into the mount method table. This might also fix the hangs with gjournal or