search for: showits

...2 1 1 1 NA 14 17 3 1 1 2 1 16 14 .... 199 2 1 NA 7 19 18 200 2 1 3 2 14 17 > EntPrelim<-prelim.mix(as.matrix(Ent),9) > EntEM<-em.mix(EntPrelim,maxits=500) > rngseed(1234567) > EntDA<-da.mix(EntPrelim, EntEM, steps=100, showits=TRUE) Steps of data Augmentation: 1... Error in da.mix(EntPrelim, EntEM, steps=100; showits=TRUE): Improper posterior--empty cells If you know what is the matter, please help us.

...a$ideol,data$prtyid, data$wage1992) colnames(x)<-c("gender","ipip", "ideol", "prtyid","wage") # start imputation s <- prelim.mix(x,4) thetahat <- em.mix(s) And here comes the error message: > newtheta <- da.mix(s,thetahat, steps=100,showits=TRUE) Steps of Data Augmentation: 1...Error in da.mix(s, thetahat, steps = 100, showits = TRUE) : Improper posterior--empty cells > imp.mix(s, newtheta, x)

...message means, so my experiments with mice were shortlived :-) I then tried the package "norm". I got some ways with the experiment, following the help file: s <- prelim.norm(as.matrix(myDataSet)) thetahat <- em.norm(s) rngseed(1234567) theta <- da.norm(s, thetahat, steps=20, showits=TRUE) At this stage however I get the following error: Steps of Data Augmentation: 1...2...Error: NA/NaN/Inf in foreign function call (arg 2) This seems strange to me, since the whole purpose of this routine is to work with NA values. So why is it complaining about NA values? After this I got i...

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. [[alternative HTML version deleted]]

...---------------------- A 3*2 matrix raw<-matrix(c(28.571,14.286,23.809,4.762,9.523,19.049),3, 2,byrow=TRUE) the sum of margin (the value I am setting as the target) m<-c(33.4,50,0,33.3,50,0,33.3,50) then call ipf function: fit1<-ipf(table, margins=m,start=raw,eps = 1e-04, maxits = 50, showits = TRUE) I could calculate it by hand with 7 iterations, but end by I am hoping to get R build in ipf function to get it done, what should I put "table" here? Thanks in advance! Mandy [[alternative HTML version deleted]]

...<-50 p<-5 prob.miss<-.5 #generate complete data x<-matrix(rnorm(n*p),nrow=n) #create missing data for (j in 1:p) {miss<-rbinom(n,1,prob.miss) for (i in 1:n){if (miss[i]==1) x[i,j]<-NA}} #create imputations seed<-1234 rngseed(seed) s <- prelim.norm(x) thetahat <- em.norm(s,showits=F) da.norm(s,thetahat,steps=1000,showits=F,return.ymis=TRUE)

....fas.harvard.edu/~corr/tc.csv","C:/R") #RUN NORM tc <- read.csv("tc.csv", header = TRUE) rngseed(1234567) #set random number generator seed s <- prelim.norm(tc) thetahat <- em.norm(s) #find the MLE for a starting value theta <- da.norm(s,thetahat,steps=20,showits=TRUE,return.ymis=TRUE) #take 20 steps ximp <- imp.norm(s,thetahat,tc) #impute missing data under the MLE

...stufe,mmit,vorai,vorap,voras,vorkf,vorsg,vorvb)) s1 <- prelim.norm(dframe) s1$nmis # re-check of NAs should be identical to above s2 <- prelim.norm(dframe[,1:32])# see below -> still NAs are available - _not_ imputed thetahat1 <- em.norm(s1) theta1 <- da.norm(s1,thetahat1,steps=20,showits=TRUE) # error: # Steps of Data Augmentation: # 1...2...3...4...5...6...7...8...Fehler: NA/NaN/Inf in externem Funktionsaufruf (arg 2) thetahat2 <- em.norm(s2) ( imputed1 <- imp.no...

...- I have two categorical variables, if I had three, the margin=c(1,2,3) design<-matrix(c(1,1,1,1,0,1,0,1,0,0,1,1),ncol=3) for (i in 1:5){ cat("Doing imputation ",i,"\n") thetahat <- ecm.mix(s,margins,design) newtheta <- dabipf.mix(s, margins,design,thetahat, steps=2000, showits=TRUE) MI[[i]] <- imp.mix(s, newtheta) } Steps of ECM: 1...2...3...4...5...6...7...8...9...10...11...12...13...14...15...16...17... 18...19...20...21...22...23...24...25...26... Steps of Data Augmentation-Bayesian IPF: 1...2...Error: NA/NaN/Inf in foreign function call (arg 7) Does anyone kno...

...data set I want to impute. As best as I can tell this is not the case. For instance, I create a 'model object' (a general location parameter list) from the data set trainSet using em.mix as follows: sTrain <- prelim.mix(trainSet,nCategorical) thetaTrain <- em.mix(sTrain,maxits=1000,showits=TRUE,eps=0.0001) I then attempt to use this model to impute a missing field (TC) in the data set workSet as follows: workSet$TC <- NA sWork <- prelim.mix(workSet,nCategorical) imputedWork <- imp.mix(sWork,thetaTrain,workSet) This does not give realistic values for TC (they are around 0)...

...: aucun argument pour max ; -Inf est renvoy? 5: NAs introduits lors de la conversion automatique 6: In max(w[!is.na(w[, j]), j]) : aucun argument pour max ; -Inf est renvoy? 7: NAs introduits lors de la conversion automatique > # Parameter estimation > system.time(thetahat<-em.mix(s,showits=TRUE)) Erreur dans rep(prior, s$ncells) : argument 'times' incorrect Timing stopped at: 0 0 0 ### indeed : s$ncells is NA ! ### Therefore, the rest crashes : > # Data augmentation proper > system.time(newtheta <- da.mix(s, thetahat, steps=100, showits=TRUE)) Erreur dans rep(prior,...

...quot;,10) #<--vector of complete data after MI fit.model.mi<-vector("list",10) rngseed(1234567) #<-- set random number generator seed for (i in 1:10){ cat("Doing imputation ",i,"\n") thetahat <- em.mix(s) newtheta <- da.mix(s, thetahat, steps=2000, showits=TRUE) MI[[i]] <- imp.mix(s, newtheta) } Here is help file about mix Help files with alias or concept or title matching 'mix' using regular expression matching: glmmPQL(MASS) Fit Generalized Linear Mixed Models via PQL gamm(mgcv) Generalized Additive Mix...

Hi all, I am newbie to Ruby and web programming as well. My show.rhtml is like, . .. ... <% require ''win32ole'' %> <% ie = WIN32OLE.new(''InternetExplorer.Application'') %> <% ie.visible = true %> <% ie.gohome %> ... .. . At server, when I access http://.../show it''ll open Iexplorer.exe as expected, but when I access same from