similar to: Re: Re: Find Closest 5 Cases?

Hi all, I've got some functioning code that I've literally taken hours to write. My 'R' coding is getting better...it used to take days :) I know I've done a poor job of optimizing the code. In addition, I'm missing an important step and don't know where to put it. So, three questions: 1) I'd like the resulting output to be sorted on distance (ascending) and

I've only begun investigating R as a substitute for SPSS. I have a need to identify for each CASE the closest (or most similar) 5 other CASES (not including itself as it is automatically the closest). I have a fairly large matrix (50000 cases by 50 vars). In SPSS, I can use Correlate > Distances to generate a matrix of similarity, but only on a small sample. The entire matrix can not

Hi all, I'm running the following code to generate 40 different jpegs based on the resulting data. I'd like the file names to be 'Cluster1.jpeg', however the code write filenames like 'Cluster 1 .jpeg'. How can I get rid of the unwanted spaces? I've looked at ?format and it doesn't seem to work - at least in this context. ################### ClusCount <- 40

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

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:

I have data in i,j,r format, where r is the value in location A[i,j] for some imaginary matrix A. I need to build this matrix A, but given the sizes of i and j, I believe that using a sparse format would be most adequate. Hopefully this will allow me to perform some basic matrix manipulation such as multiplication, addition, rowsums, transpositions, subsetting etc etc. Is there any way

Dear all, Apologies for this beginner's question. I have a variable Price, which is associated with factors Season and Crop, each of which have several levels. The Price variable contains missing values (NA), which I want to substitute by the mean of the remaining (non-NA) Price values of the same Season-Crop combination of levels. Price Crop Season 10 Rice Summer 12

Hi all, I've been struggling learning R and need to turn to the list again. I've got a dataset (comma-delimited file) with the following fields: recid, latitude, longitude, population, dwelling and age. For each observation, I'd like to calculate the total number of people and dwellings and average age within 2 k.m. Distance could be Euclidean, however, a proper distance

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

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 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.

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 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. >

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

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

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, 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

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

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, 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]