search for: p2s

Hello, I am trying to make a data frame from many elements after running a function which creates many elements, some of which may not end up being real elements due to errors or missing data. For example, I have the following three elements p1s, p2s, and p3s. p9s did not generate the same data as there was an error in the function for some reason. I currently have to delete p9s from the data.frame() command to get the data.frame to work.? How can I make a data frame by somehow ignoring elements (e.g., p9s) that do not exist, without having to...

...(year, month, measurements, standard deviation of the measurement). Of course, there are some random missing values on columns 3 and 4. The measurements are an almost periodic time-series contaminated by noise. I did some tests such as: a.out <- amelia(x, m = 5, ts = "YEAR", p2s = 0, idvars=c("DEV")); > a.out <- amelia(x, m = 5, ts = "YEAR", p2s = 0, idvars=c("MON","DEV")); Error in matrix(1, AMn, 1) : non-numeric matrix extent a.out <- amelia(x, m = 5, ts = "YEAR", p2s = 0); a.out <- amelia(x, m = 5,...

...3,4,5,6,4,3,4,2, 4,1,1.2,.3,.4,.3,.4,.3,.2,.3,.4,.3,.3,.3,.2,.3,.2,.3,.3,.1,.1,.2,.2,.3,.3,.1,.1,.2,.2,.3,.3,.1,.1,.2,.2,.3) newishtrade = cbind(newtrade,newtradeMean, newtradeSD) newishtrade a.out = amelia(freetrade, m=5, ts = "year", cs = "country", priors = newishtrade, p2s=2, noms="signed") a.out = amelia(freetrade, m=5, ts = "year", cs = "country", priors = newishtrade, p2s=2, ords="signed") I followed the trail back through the source code to try and see what was occurring. I learned some debugging techniques and tried...

..."jeff", ng ) , rep( levels( players ), np ) ) , levels=players ) p2 <- factor( c( rep( "mike", ng ) , rep( levels( players ), each=np ) ) , levels=players ) p1s <- c( rnorm( n=ng, mean=-0.5, sd=2 ), rep( 1, np^2 ) ) p2s <- c( rnorm( n=ng, mean=0.5, sd=2 ), rep( 0, np^2 ) ) results <- data.frame( winner=factor( levels(players)[ ifelse(p1s<p2s,p2,p1)], levels=players ) , loser=factor( levels(players)[ ifelse(p1s<p2s,p1,p2) ], levels=players) , Freq=c( rep( 1, ng ), rep( 1, np^2) ) ) results &l...

...))}) 6: try(am.inv(a = g11), silent = TRUE) 7: amsweep(thetareal, c(FALSE, o[ss, ])) 8: emfred(x, thetaold, indx$o, indx$m, indx$ivector, indx$icap, indx$AMr1, indx$AMr2, AM1stln = AM1stln, returntype = "theta", priors = priors, empri = empri, collect = collect) 9: emarch(x.stacked$x, p2s = p2s, thetaold = NULL, tolerance = tolerance, startvals = startvals, x.stacked$priors, empri = empri, frontend = frontend, collect = collect, autopri = prepped$autopri, emburn = emburn) 10: amelia.default(datM, m = 5, idvars = "group") 11: amelia(datM, m = 5, idvars = "group...

...;: 1 1 2 1 1 2 1 1 1 2 ... $ os : num 0 1 0 0 1 1 1 0 0 1 ... $ css : num 0 1 0 0 1 1 1 0 0 1 ... $ rfs : num 0 1 1 0 1 1 1 0 0 1 ... $ comp : num 0 1 1 0 1 1 1 0 0 1 ... > set.seed(200) > M <- 50 # Number of imputations > am.imp <- amelia(utt.mi, m=M, p2s=1, startvals=1, write.out=F, + idvars=c('os','css','rfs','comp'), + noms=c('gender','symptoms','site','multifoc','ctnm','prebca','precystec' , + 'smk','surgery','ptnm.t','nodes',...

...clus) detach(package: Hmisc) --------------- After examining the missing data patterns, impute 10 datasets using the amelia function from the Amelia package. Check the densities of the continuous variables to make sure they make sense. --------------- library(Amelia) am.imp <- amelia(d, m=10, p2s=1, startvals=1, write.out=F, noms=c('status','sex','hepmeg','trt')) compare.density(data=d, output=am.imp, var='trig') compare.density(data=d, output=am.imp, var='platelet') --------------- Since everything looks ok, fit Cox models to each of the 10...

...0,NA),c(15,85,900)), n1=rep(1000,1000), n2=rep(100,1000) ) table(ss1$ph1.y, ss1$ph2.x) > Phase1.x >Phase2.y 0 1 > 0 85 0 > 1 5 10 p2 <- twophase(id=list(~id,~id), strata=list(NULL,NULL), data=ss1, subset=~subset, fpc=list(~n1,~n2)) svymean (~ph2.y, design=p2s) > mean SE >ph2.y 0.15 0 However, taking into consideration the 2nd sample, the estimator should be: ph1.x.bar (phase1)=80/1000=0.08 and ph2.y.bar (phase2)=15/100=0.15 defect boards, that means y.est=1.5*0.08=0.12 defect boards, since the RATIO ESTIMATOR equals 15/10=1.5 defect p...

...quot;whostage","t0rhfa","vlsupp","t0rwfh","t0rvl","t0rcd4pc","postrantb","resistance","id")] round(apply(apply(impdat,c(1,2),is.na),2,mean),digits=3) # percentage of missing values myimp <- amelia(impdat, m=M, p2s=0, noms=c("postrantb","vlsupp","whostage","resistance"),cs=c("id"),ts=c("X_t0"),bounds=matrix(c(3,0,70, 4,0,100, 11,0,400, 12,0,50 ),ncol=3,nrow=4,byrow=T),logs="nvp",polytime=3,splinetime=3,empri=1000,incheck=TRUE,tolerance=0.00...

hello everyone. i guess this has been asked before, but haven't found it in the faq. i have the following issue... it is not uncommon nowadays to have desktops with filesystems in the order of 500gb/1tb. now, my kubuntu (but other distros do the same) forces a fsck on ext3 every so often, no matter what. in the past it wasn't a big issue. but with sizes increasing so much, users are

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