similar to: mix package causes R to crash

Dear R-help, I am trying to impute missing data for the first time using R. The norm package seems to work for me, but the missing values that it returns seem odd at times -- for example it returns negative values for a variable that should only be positive. Does this matter in data analysis, and/or is there a way to limit the imputed values to be within the minimum and maximum of the actual

I am desperately trying to impute missing data using 'imp.mix' but always run into this yucky error message to which I cannot find the solution. It's the first time I am using mix and I'm trying really hard to understand, but there's just this one step I don't get...perhaps someone knows the answer? Thanks! Jens My code runs:

Hi all, When using package MIX, I often get the error: NA/NaN/Inf in foreign function call (arg 1). For example, > s<-prelim.mix(Y,6) Error in prelim.mix(Y, 6) : NA/NaN/Inf in foreign function call (arg 1) or when I ran: MI<-vector("list",5) #<--vector of complete data after MI fit.model.mi<-vector("list",5) rngseed(1234567) #<-- set random number

Hello all, When I ran my R program in R version 2.1.1 in windows XP and 2000, and I downloaded a package call "MIX", I got an application error: RGui:Rgui.exe - Application Error: The instruction at "0x1009d8a1" referenced memory at "0xbde48f58". The memory could not be "read". Click on ok to terminate the program click on cancel to debug the program

-----BEGIN PGP SIGNED MESSAGE----- Hash: SHA1 While I'm not an R expert, I have used R on Windows XP. Now I've moved to Ubuntu (Intrepid), and I'm trying to configure R to work with the Gelman and Hill _Data Analysis Using Regression and Multilevel/Hierarchical Models_. So far, it's not working. I start by following the instructions for installing arm and BRugs at

I am conducting a simulation study generating multivariate normal data, deleting observations to create a data set with missing values and then using multiple imputation via da.norm in Schafer's norm package. >From da.norm, I get the following error message: "Error: NA/NaN/Inf in foreign function call (arg 2)" The frequency of the error message seems to depend on the ratio of n

Dear list, I'd like to use multiple imputations to try and save a somewhat badly mangled dataset (lousy data collection, worse than lousy monitoring, you know that drill... especially when I am consulted for the first time about one year *after* data collection). My dataset has 231 observations of 53 variables, of which only a very few has no missing data. Most variables have 5-10% of

Hi all, A short while ago I asked a question about multiple imputation and I got several helpful replies, thanks! I have untill now tried to use the packages mice and norm but both give me errors however. mice does not even run to start with and gives me the following error right away: iter imp variable 1 1 Liquidity.ratioError in chol((v + t(v))/2) : the leading minor of order 1 is not

Hello! Is it possible to compile kernel without '-O' option? When my COPTFLAGS=-O -pipe, it compiles fine. When I change COPTFLAGS=-pipe, it breaks: cc -c -pipe -Wall -Wredundant-decls -Wnested-externs -Wstrict-prototypes -Wmis sing-prototypes -Wpointer-arith -Winline -Wcast-qual -fformat-extensions -ansi -g -nostdinc -I- -I. -I../.. -I../../../include -I../../contrib/ipfilter

I am doing a simulation with a relatively large data set (20,000 observations) for which I want to calculate the area under the Receiver Operator Curve (AUC) for many parameter combinations. I am using the ROC library and the following commands to generate each AUC: rocobj=rocdemo.sca(truth = ymis, data = model$fitted.values, rule = dxrule.sca) #generation of observed ROC object

Greetings! I am bootstrapping and I am using EM in the norm package to fill in missing data for a financial time series with each step of the loop. For the most part EM works fine for me, but the following error message is guaranteed before I hit the 200th scenario: Iterations of EM: 1...2...3........348...349...Error: NA/NaN/Inf in foreign function call (arg 2) The following code should

Dear R-users I need to calibrate kappa, rho, eta, theta, v0 in the following code, see below. However when I run it, I get: y <- function(kappahat, rhohat, etahat, thetahat, v0hat) {sum(difference(k, t, S0, X, r, implvol, q, kappahat, rhohat, etahat, thetahat, v0hat)^2)} > nlminb(start=list(kappa, rho, eta, theta, v0), objective = y, lower =lb, > upper =ub) Error in dots[[1L]][[1L]] :

Hello all I'm running da.norm function in R for climate data rngseed(1234567) theta1=da.norm(mydata, thetahat, steps=1000,showits=T) param1=getparam.norm(mydata,theta1) As I understand the 1000 steps represent the markov chain values. Is there a way to plot them? Something like plot(1:1000, param1$mu[]). I just can't find a way to extract them out of my theta1. Thank you, Andrey.

Dear All, I am currently using the MI package (Su, Gelman, Hill and Yajima) to make multiple Imputations of my dataset with missing values. After fitting a model, I can use display(model) to visualize the pooled estimates as well as estimates of each imputed dataset. I can visualize these also by typing print(model). However I would like to know how I can extract estimates of single imputed

Anyone used jags.parallel in latest version of R2jags? Ubuntu Oneiric, just ran updates in R to get latest R2jags package which supposedly has added jags.parallel command to send chains to multiple cores ... but when I submit, I get: test <- jags.parallel(d, inits, param, model.file="rats.bug", n.chains=3, n.iter=2000) Error: could not find function

Is there a way to add covariates to the system equation in a time-varying approach: Y[t] = F'[t]theta[t] + v[t], v[t] ~ N[0,V] #observation equation theta[t] = theta[t-1] + psi*Z[t] + w[t], w[t] ~ N[0,W] #system equation While F[t] is a matrix of regressors to capture the short term effect on the response series Y, Z[t] measures the long-term effect of either (1) two policies by a step

Hello all, I am trying to optimize my logistic regression model by using bootstrap. I was previously using SAS for this kind of tasks, but I am now switching to R. My data frame consists of 5 columns and has 109 rows. Each row is a single record composed of the following values: Subject_name, numeric1, numeric2, numeric3 and outcome (yes or no). All three numerics are used to predict

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),

dear list members, there seems to be a problem with the prelim.norm function (package norm) as number of items in the dataset increases. the output of prelim.norm() is a list with different summary statistics, one of them is the missingness indicator matrix "r". it lists all patterns of missing data and a count of how often each pattern occured in the dataset. as the number of items and

Hey eveyone! I hope someone can help wiht this question. I have a matirux of all zeros and ones and I would like to indentify all unique patterns in the rows andthe number of times the pattern occurs. I changed all zeros to NA tried to use prelim.norm to identify all patterns of missing data in the rows. I got the message Warning message: NAs introduced by coercion Any ideas of how