similar to: Multiple imputation with plausible values already in the data

Hello, I have a data of Patents from OECD in delimited text format with IPC being one column, I want to filter the data by selecting only certain IPC in that column and delete other rows which do not have my required IPCs. Please, can anybody guide me doing it, also the IPC codes are string variables. The data is somewhat like below, but its a huge dataset containing more than 11 million rows

The need comes from the PISA data. (http://www.pisa.oecd.org) In the data there are many cases and each of them carries a numeric variable that signifies it's weight. In SPSS the command would be "WEIGHT BY" In simpler words here is an R sample ( What is get VS what i want to get ) > data.recieved <- data.frame( + kindergarten_attendance = factor(c(2,1,1,1), labels =

Hello, I'm running Wine 0.9.10 on a Fedora Core 5 at work. I'd like to use the Socks5 proxy we've got with application run under Wine. The application I'm using in Wine works fine using direct connection to the internet but this application has no settings I can change to inform it of the socks proxy. So I'm using Sockscap32 2.38 from www.permeo.com . It works fine under

Dear R Users - R is a wonderful software package. CRAN provides a variety of tools to work on your data. But R is not apt to utilize all the public databases in an efficient manner. I observed the most tedious part with R is searching and downloading the data from public databases and putting it into the right format. I could not find a package on CRAN which offers exactly this fundamental

Try the code below: df <- read_delim("C:/Users/lruan1/Desktop/1112.csv", "|", escape_double = FALSE, trim_ws = TRUE) df_new <- subset(df,df$IPC == 'H04M001/02'| df$IPC == 'C07K016/26' ) You can add more condition with "|" in the subset function. Good luck! On Wed, Jan 3, 2018 at 2:53 PM, Saptorshee Kanto Chakraborty < chkstr at

Hi, I want to read some Times Series of GDP from OECD-Countrys. First I call: > oecd96<-ts(read.csv("oecd96.csv",header=T,sep=";"),start=1950,freq=1) > summary(oecd96) gdpcausb gdpcautb gdpcbelb gdpccanb Min. : 8567 Min. : 4533 Min. : 6616 Min. : 8966 1st Qu.:10771 1st Qu.: 8717 1st Qu.: 9440 1st Qu.:11694 Median

Hello samba people, I have samba 2.0.3 compiled from source on a Linux box, in order to setup SMB sharenames to access the Linux box from an NT client. I have swat set up in order to configure Samba, and my conf file is as follows: # Samba config file created using SWAT # from 10.65.8.20 (10.65.8.20) # Date: 1999/03/19 15:31:59 # Global parameters workgroup = [xxx] netbios name

ERMX Grabs Edge Of US Trade With China And Moves Into Nitride Devices! EntreMetrix Inc. (ERMX) $0.16 Congress's push to increase trade agreements with China gives ERMX huge advantage as they enter joint venture to manufacture Nitride Devices for military, energy and technological solutions in China. This is huge. Get on ERMX Monday! The sharp rises in royalty fees could be "fatal"

ERMX Grabs Edge Of US Trade With China And Moves Into Nitride Devices! EntreMetrix Inc. (ERMX) $0.16 Congress's push to increase trade agreements with China gives ERMX huge advantage as they enter joint venture to manufacture Nitride Devices for military, energy and technological solutions in China. This is huge. Get on ERMX Monday! The sharp rises in royalty fees could be "fatal"

Please tell me what can't work about this idea. - Introduce concept of "batches", through some kind of marker (tied to fences). - Request buffers up front, userspace does all relocation (avoid teaching the kernel about tiling, etc). - Driver stores state for each batch. - FIRE_RING becomes an ioctl that can fail (imagine backgrounded app holding pinned memory for too long). ==>

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 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

Hi, I'm relatively new to R and it'll be great if someone can help me with what I'm doing here. I am trying to do multiple imputation on my dataset, but I'm not quite sure which function to use as my dataset contains dichotomous variables. Here's an outline of what i've done so far, and i'm not sure if i'm doing it right, and where to go from here. It'll be

Hi, all, I'm running multiple imputation to handle missing data and I'm running into a problem. I can generate the MI data sets in both amelia and the mi package (they look fine), but I can't figure out how to get pooled results. The examples from the mi package, zelig, etc., all seem to go right to something like a regression, though all I want are the mean and SE for all the

Dear all, I am trying to impute data for a range of variables in my data set, of which unfortunately most variables have missing values, and some have quite a few. So I set up the predictor matrix to exclude certain variables (setting the relevant elements to zero) and then I run the imputation. This works fine if I use predictive mean matching for the continous variables in the data set. When I

Hi all, How can I calculate the mean from several imputed data sets with the package mice? I know you can estimate regression parameters with, for example, lm and subsequently pool those parameters to get a point estimate using functions included in mice. But if I want to calculate the mean value of a variable over my multiple imputed data sets with fit <- with(data=imp, expr=mean(y)) and

Dear R experts, I have a large database made up of mixed data types (numeric, character, factor, ordinal factor) with missing values, and I am looking for a package that would help me impute the missing values using ?either the mean if numerical or the mode if character/factor. I maybe could use replace like this: df$var[is.na(df$var)] <- mean(df$var, na.rm = TRUE) And go through all the many

Dear all; I am trying to use HMISC imputation function to perform multiple imputations on my data and I keep on getting errors for the code given in the help files. When using "aregImpute" the error is; >f <- aregImpute(~y + x1 + x2 + x3, n.impute=100) Loading required package: acepack Iteration:1 Error in .Fortran("wclosepw", as.double(w), as.double(x),

Hi I found on google that there is an implementation of hot-deck imputation in SAS: http://ideas.repec.org/c/boc/bocode/s366901.html Is there anything similar in R? Many Thanks Eleni Rapsomaniki

Hello all, I am working on a longitudinal study of children in the UK and trying the PAN package for imputation of missing data, since it fulfils the critical criteria of taking into account individual subject trend over time as well as population trend over time. In order to validate the procedure I have started by deleting some known values ?we have 6 annual measures of height on 300 children