similar to: Imputing missing values

I am using barplot, and barplot2 in the gregmisc bundle, in the following way: barplot2(sort(xtabs(expend / 1000 ~ theme)), col = c(mdg7, mdg8, mdg3, mdg1), horiz = T, las = 1, xlab = "$ '000", plot.grid = T) The problem is that the values of 'theme', which is a factor, are in some cases rather long, so that I would like to wrap/split them at a space once they

Dear R-users, I have a dataset as like below, and I want to subtotal the values of rice,wheat and maize by year for each fid. fid      year     rice     wheat      maize ------------------------------------------------ 1        1995      5        NA           NA 1        1995      NA        3           NA 1        1995      NA       NA           2 1        1996      4        NA           NA 1  

Hello, I'm trying to impute data below detection limit (with multiple detection limits) so i need just a method or a code for imputation and then extract the complete dataset to do the analyses. Is there any package which could do that simply as i'm a beginner in R Thank you -- View this message in context:

So I have a very big matrix of about 900 by 400 and there are a couple of NA in the list. I have used the following functions to impute the missing data data(pc) pc.na<-pc pc.roughfix <- na.roughfix(pc.na) pc.narf <- randomForest(pc.na, na.action=na.roughfix) yet it does not replace the NA in the list. Presently I want to replace the NA with maybe the mean of the rows or columns or

Hi folks, I have a dataset that consists of counts over a ~30 year period at multiple (>200) sites. Only one count is conducted at each site in each year; however, not all sites are surveyed in all years. I need to impute the missing values because I need an estimate of the total population size (i.e., sum of counts across all sites) in each year as input to another model. >

I have a data frame called "pose": DESCRIPTION QUANITY CLOSING.PRICE 1 WHEAT May/10 1 467.75 2 WHEAT May/10 2 467.75 3 WHEAT May/10 1 467.75 4 WHEAT May/10 1 467.75 5 COTTON NO.2 May/10 1 78.13 6 COTTON NO.2 May/10 3 78.13 7 COTTON NO.2 May/10 1 78.13

Hi R-experts, how can I apply a function to each numeric column of a data frame and return the whole data frame with changes in numeric columns only? In my case I want to do a median imputation of the numeric columns and retain the other columns. My dataframe (DF) contains factors, characters and numerics. I tried the following but that does not work: foo <- function(x){

Hello: I have been trying to impute missing values of a data frame which has both numerical and categorical values using the function transcan() with little luck. Would you be able to give me a simple example where a data frame is fed to transcan and it spits out a new data frame with the NA values filled up? Or is there any other function that i could use? Thank you avneet ===== I believe in

Hi all, I am trying to use nonlinear regression (nls) to analyze some seed germination data, but am having problems with error codes. The data that I have closely matches the germination dataset included in the drc package. Here is the head of the data temp species start end germinated TotSeeds TotGerminated Prop 1 10 wheat 0 1 0 20 0 0.0 2 10 wheat

I have this assignment to do and after ten hours of constant trying my eyes ache and i give up.. all i'm able to get is this plot please help me these are the commands i have used till now read.table(file.choose(), sep=";", header=T) read.table(file.choose(), sep=";", header=T)->areas melt(areas,id=c("Year","State"),m=c("Rice"))->

Folks, This must be a rather common problem with real life time series data but I don't see anything in the archive about how to deal with it. I have a time series of natural gas prices by flow date. Since gas is not traded on weekends and holidays, I have a lot of missing values, FDate Price 11/1/2006 6.28 11/2/2006 6.58 11/3/2006 6.586 11/4/2006 6.716 11/5/2006 NA 11/6/2006 NA 11/7/2006

Is there a good way in R to impute values which exist, but are less than the detection level for an assay? Thanks, Jonathan Williams OPTIMA Radcliffe Infirmary Woodstock Road OXFORD OX2 6HE Tel +1865 (2)24356

Hi Keep your messages in the list, you increase your chance to get some answer. I changed your data to groupedData object (see below), but I did not find any problem in it. plot(wlg) gives reasonable picture and I am not such expert to see any problem with data. Seems to me, that something has to be wrong with nlsList function. > wheat.list <- nlsList(Prop ~ SSlogis(end,Asym, xmid,

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

Art (and group), I'm doing this as a form of missing value analysis. Approximately 30% of the cases are missing data for one variable. To impute values for those cases, I'd like to match those cases that are missing the variable to all other cases and then take an average of those to infill. I realize there are many methods for imputing data. I'm not well versed on any in

Dear all, I'm trying to impute NAs by conditional medians using transcan() in conjunction with impute.transcan(). ... see and run attached example.. Everything works fine, however impute() returns saying Under WINDOWS > x.imputed <- impute(trans) Fehler in assign(nam, v, where = where.out) : unbenutzte(s) Argument(e) (where ...) Zus?tzlich: Warnmeldung: variable X1 does not

Hello! If I understand this listserve correctly, I can email this address to get help when I am struggling with code. If this is inaccurate, please let me know, and I will unsubscribe. I have been struggling with the same error message for a while, and I can't seem to get past it. Here is the issue: I am using a data set that uses -1:-9 to indicate various kinds of missing data. I changed

Hello! I get the following message when I run the mi() function from the mi package. Error while imputing variable: c3 , model: mi.polr Error in eval(expr, envir, enclos) : could not find function "c14ordered" Here's the situation: I am running R v. 2.9.2 on Mac OSX v. 10.5.8. I am trying to impute missing data in a data set that I've trimmed down to 302 variables.

I am trying to use the mi package to impute data, but am running into problems with the functions it calls. For instance, I am trying to impute a categorical variable called "min.func." The mi() function calls the mi.categorical() function to deal with this variable, which in turn calls the nnet.default() function, and passes it a fixed parameter MaxNWts=1500. However, as

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