similar to: EM norm package (NA/NaN/Inf in foreign function call (arg 2))

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.

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

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, 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

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

Dear Professor Schaefer I am experiencing a technical difficulty with your mix package. I would appreciate it if you could help me with this problem. When I run the following code, R 2.5.1 and R 2.6.0 crashes. It's been tested on at least 2 windows machine and it is consistent. Execution code it's self was coped from the help file of imp.mix. Only thing I supplied was a fake dataset.

Hi Folks, I'm using the 'norm' package (based on Shafer's NORM) on some data. In outline, (X,Y) are bivariate normal, var(X)=0.29, var(Y)=24.4, cov(X,Y)=-0.277, there are some 900 cases, and some 170 values of Y have been set "missing" (NA). The puzzle is that, repeating the multiple imputation starting from the same random seed, I get different answers from the repeats

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]] :

Dear alltogether, I experience very strange behavior of imputation of NA's with the NORM library. I use R 2.2.0, win32. The code is below and the same dataset was also tried with MICE and aregImpute() from HMISC _without_ any problem. The problem is as follows: (1) using the whole dataset results in very strange imputations - values far beyond the maximum of the respective column, >

Hello, We use the mix package and we have a problem with the DA function. We aren't sure, but it's maybbe a memory problem. We have done: > Ent<--read.table("C:/.../File.txt") > attach(Ent) > Ent V1 V2 V3 V4 ... V16 V17 1 1 1 2 6 18 18 2 1 1 1 NA 14 17 3 1 1 2 1 16 14 .... 199 2 1 NA 7 19 18 200

Hello, i need some help concerning the library norm. i habe to impute some missing values using the em algorithm. The help offered for the library doesn't really help me, maybe somebody already worked on em algorithm or multiple imputation. some fictive Data x1 x2 50 60 24 . 26 20 87 . 21 . Problem: Em Algorithm in R calculating the missings in x2. Thanks in advance. -- View this

Dear all, I try to fit a glmmPQL on a huge data with 384189 individuals id=1:384189: working in 1520 establishments est:1:1516. The minimum number of individuals in every establishment is 30. This works for a subsample excluding establishemnet cells smaller than 100, but fail when we include smaller cells: R> summary(glmmPQL(count ~ + I( age-ave(age,est) )* ave(age,est) + + I(

Hi, I have a huge data table with several variables containing missing values. I want to make a simple scatter plot (Mass, HSI) and draw a smooth regression on it. Here are the summaries of these two variables: > summary(Mass) Min. 1st Qu. Median Mean 3rd Qu. Max. 46.0 205.9 272.5 282.6 348.3 610.1 > summary(HSI) Min. 1st Qu. Median Mean 3rd Qu. Max.

Dear R-helpers: I am trying to fit a multivariate Cox proportional hazards model, modelling survival outcome as a function of treatment and receptor status. The data look like below: # structure of the data str(sample.data) List of 4 $ survobj : Surv [1:129, 1:2] 0.8925+ 1.8836+ 2.1191+ 5.3744+ 1.6099+ 5.2567 0.2081+ 0.2108+ 0.2683+ 0.4873+ ... ..- attr(*, "dimnames")=List of 2

I am trying to use knn to do a nearest neighbor classification. I tried using my dataset and got an error message so I used a simple example to try and understand what I was doing wrong and got the same message. Here is what I typed into R: try [,1] [,2] [,3] [,4] r "A" "A" "T" "G" r "A" "A" "T" "G" f

Hi all, I'm new to R and I'm struggling to decipher an error message. Briefly, I am trying to use the pvclust package to do hierarchical clustering of some CGH data. The data is from the Progenetix CGH database. It is arranged as a table where each column is a single case and each row is a single chromosome band. The value in each cell is either 0, 1, 2, or -1. Corresponding to no change,

Dear R-helpers: I am trying to fit a multivariate Cox proportional hazards model, modelling survival outcome as a function of treatment and receptor status. The data look like below: # structure of the data str(sample.data) List of 4 $ survobj : Surv [1:129, 1:2] 0.8925+ 1.8836+ 2.1191+ 5.3744+ 1.6099+ 5.2567 0.2081+ 0.2108+ 0.2683+ 0.4873+ ... ..- attr(*, "dimnames")=List of 2

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

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