similar to: R crashes when packages 'impute' and 'GeneMeta' are used together.

Hello all, It seems that the GeneMeta passage returns NA in the respective row if any gene in the data sets to be synthesised are missing. Do you know of a way to overcome this problem? I tried using the 'impute' package to fill-in the missing values, but R crashes if 'impute' and 'GeneMeta' packages are used together. I have asked a separate question for that. Thanks

I have to remove missing data both in character and numeric datatype.I tried using NA condition but it is not working ,please help me to solve this. -- View this message in context: http://www.nabble.com/Handling-missing-data-tp25530192p25530192.html Sent from the R help mailing list archive at Nabble.com.

Hi, I am trying to install the “impute” package in unix. but I get the following error message. I followed the following steps. Do you know what is causing this and how I can solve this problem? source("http://www.bioconductor.org/biocLite.R") biocLite("impute") Using R version 2.11.1, biocinstall version 2.6.10. Installing Bioconductor version 2.6 packages: [1]

hi, i am getting an error when calling the impute.knn function (see the screenshot below). what is the problem here and how can it be solved? screenshot: ################## *** caught segfault *** address 0x513c7b84, cause 'memory not mapped' Traceback: 1: .Fortran("knnimp", x, ximp = x, p, n, imiss = imiss, irmiss, as.integer(k), double(p), double(n), integer(p),

ear all, This is my first time using this listserv and I am seeking help from the expert. OK, here is my question, I am trying to use impute.knn function in impute library and when I tested the sample code, I got the error as followingt: Here is the sample code: library(impute) data(khanmiss) khan.expr <- khanmiss[-1, -(1:2)] ## ## First example ## if(exists(".Random.seed"))

Hi again, > On Sep 8, 2016, at 10:05 AM, Vassilis Virvilis <vasvir at iit.demokritos.gr> > wrote: >> Now that's a problem. I am not sure that I can find >> >> 1) the original software >> 2) a (physical) windows machine in the vicinity of the UPS >> Ha! I jumped several hooks but I think I got it. 01. I contacted the store that sold us the UPS and

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

Dear list, This must be an easy one. I have a data frame like this one: test.df <- data.frame(x1=c(2,3,5), x2=c(5, 3, 4), w=c(0.8, 0.3, 0.5)) and I want to construct a weighted mean of the first two columns using the third column for weighting; i.e. y[1] = x1[1]*w[1] + x2[1]*(1-w[1]) y[2] = ... One way to do this is to use a loop like test.df$y <-numeric(3) with(test.df, for(i in

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

Hello, I'm using: platform i386-pc-mingw32 arch i386 os mingw32 system i386, mingw32 status major 1 minor 8.1 year 2003 month 11 day 21 language R and I get the following error with: library(Design) df <- list(pre=c(0,, 0, 1, 1, 1, 0, 1, 1, 1, 1, 1,

I would like to de-mean the 'vector' column of the following dataframe by factor: set.seed(5444) vector <- rnorm(1:10) factor <- rep(1:2,5) test.df <- data.frame(factor, vector) which is: factor vector 1 1 -0.4963935 2 2 -2.0768182 3 1 -1.5822224 4 2 0.8025474 5 1 0.3504199 6 2 0.2358464 7 1 -0.3989443 8 2 -0.3692544 9

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 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'

Hello R experts I am using Hmisc aregImpute and Impute (following example on page 105 of The Hmisc and Design Libraries). *My end goal is to have NAs physically replaced in my dataframe. I have read the help pages and example in above sited pdf file, but to no avail. Here is example of what I did. Ph, my data frame, is attached. > xt <- aregImpute (~ q5 + q22rev02 + q28a, n.impute=10,

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

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 +

> You could also try adding "-x usb_set_altinterface" to the command line > (or adding it to ups.conf). The lsusb output implies that the only valid > setting is 0 (bAlternateSetting), but it might need to be set explicitly. I checked with -x usb_set_altinterface and it says nut_usb_set_altinterface: usb_set_altinterface() should not be necessary - please email the

Hi, I have a survey dataset of about 20000 observations where for 2 factor variables I have about 200 missing values each. I want to impute these using 10 possibly explanatory variables which are a mixture of integers and factors. Since I was quite intrigued by the concept of rrp I wanted to use it but it takes ages and terminates with an error. First time it stopped complaining about too little

Hello everybody, I am trying to setup asterisk to redirect international calls via a carrier which uses a fixed price tel number. The scenario is Dial 087..something (UK number) Pause for answer at the other end Send required telephone number 003..etc followed by # What is the easiest way of doing this? I have trouble with both the pause and adding the # at the end of the number. Best

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