similar to: Replacing missing values with values before it

I'm using the survey api. I am taking 1000 samples of size of 100 and replacing 20 of those values with missing values. Im trying to use sequential hot deck imputation, and thus I am trying to figure out how to replace missing values with the value before it. Other things I have to keep in mind is if there are two missing values side by side, how do I replace both those values with the

So I have to use this table of min, max, and mean temps for certain years http://www.stat.berkeley.edu/classes/s133/data/january.tab. I am supposed to figure out which year had the hottest January and which had the coldest. But I dont know how to! Nick Manginelli [[alternative HTML version deleted]]

Hi all, I'm looking for a contributed package that can provide a detailed account of missing data patterns and perhaps also provide imputation procedures, such as mean imputation or hot deck imputation and the like. Is there anything out there? Thanks in advance, David -- =========================================================================== David Kaplan, Ph.D. Professor

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

Hi! Is there a possibilty in R to carry out LOCF (Last Observation Carried Forward) analysis or to create a new data frame (array, matrix) with LOCF? Or some helpful functions, packages? Karl --------------------------------- Gesendet von http://mail.yahoo.de Schneller als Mail - der neue Yahoo! Messenger. [[alternative HTML version deleted]]

Hi everybody, I'm a new R french user. Sorry if my english is not perfect. Hope you'll understand my problem ;) I have to work on temperature data (35000 lines in one file) containing some missing data (N/A). Sometimes I have only 2 or 3 N/A following each other, but I have also sometimes 100 or 200 N/A following each other. Here's an example of my data, when I have only small gaps

I have a dataset where I have missing times (11:00 and 16:00). I would like the outputs to include the missing time so that the final time vector looks like "realt" and has the previous time's value. Ex. If meas at time 15:30 is 0.45, then the meas for time 16:00 will also be 0.45. meas are the measurements and times are the times at which they were taken. meas<-runif(18)

Hi, This is not exactly an R request, but does anyone know of a good dataset that contains missing and missing data that have been completed later (like from persistent in-person interview attempts)? (want it for some Bayesian regression analysis) Thanks!! -Raphael [[alternative HTML version deleted]]

I have table "decks" with three fields: "id", "created_at" and "cards" which is a 264-character string field. I have modified the model with a constructor, as follows: class Deck < ActiveRecord::Base attr_reader :cards def initialize @cards = "12345" end end If I call Deck.new from my controller, I get no errors and an object with the

I have a Deck object and a Card object with their corresponding tables. (You know a deck of cards.) When I destory a Deck it leaves orphaned cards in the database. Is there a way to set up the objects with ActiveRecord so that when a parent object is destoryed the child objects are destroyed as well? My code below. class Deck < ActiveRecord::Base has_many :cards end class Card <

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,

Hey all, I'm relatively new to the R-environment. I'm having a bit of trouble with encapsulation. I have a globally declared variable that doesn't update it when I change it in a function. For example when I run the following function >deckn<-NULL >deck1<-1 #52 card deck >deck<-function() { #Creating a standard deck deck1<-c(1:52) deckn<-deck1 #Creating n

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 am working with a stratified survey dataset with sampling weights and I want to use multiple imputation to help with missingness. 1. Is there a way to run an ordered logistic regression using both a multiply imputed dataset (i.e. from mice) and adjust for the survey characteristics using the weight variable? The Zelig package is able to do binary logistic regressions for survey

Dear List Members I used to play around with R to answer the following question by simulation (I am aware there is an easy explicit solution, but this is intended to serve as instructional example). Suppose you have a poker game with 6 players and a deck of 52 cards. Compute the empirical frequencies of having a single-suit hand. The way I want the result structured is a boolean nosimulation

Hi, I am a long time SPSS user but new to R, so please bear with me if my questions seem to be too basic for you guys. I am trying to figure out how to analyze survey data using logistic regression with multiple imputation. I have a survey data of about 200,000 cases and I am trying to predict the odds ratio of a dependent variable using 6 categorical independent variables (dummy-coded).

I'm looking for a package that has a start-of-the-art method of imputation of missing values in a data frame with both continuous and factor columns. I've found transcan() in 'Hmisc', which appears to be possibly suited to my needs, but I haven't been able to figure out how to get a new data frame with the imputed values replaced (I don't have Herrell's book). Any

Hello, I have a general question about combining imputations as well as a question specific to the rms and Hmisc packages. The situation is multiple regression on a data set where multiple imputation has been used to give M imputed data sets. I know how to get the combined estimate of the covariance matrix of the estimated coefficients (average the M covariance matrices from the individual

Hi, I want to impute the missing values in my data set multiple times, and then combine the results (like multiple imputation, but manually) to get a mean of the parameter(s) from the multiple imputations. Does anyone know how to do this? I have the following script: y1 <- rnorm(20,0,3) y2 <- rnorm(20,3,3) y3 <- rnorm(20,3,3) y4 <- rnorm(20,6,3) y <- c(y1,y2,y3,y4) x1 <-

I'm discovering R (very impressive), and didn't find in the docs a simple method for replacing, in a data frame, missing values (NA) with the column's mean (or any other method for reconstructing missing values when needed). Thanks in advance for your help. -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read