search for: jacknif

...igma) > > source("fsYUTH.R") #the fucntions Y,U,Tbbarran and H necessary to calcualte the hat{b} > > source("fMLEPFP.R") #the maximun likelihood profile estimation of IGLD(mu,sigma) > > source("f04P.R") #it to compute the mle, jacknife and bootstrap > > source("fprofileloglik06.R") #the log-likelihood profile of IGLD(mu,sigma) > > source("fmgfIGLD.R") #read the fmgfIGLD.R file defining the fmgfIGLD function. It is useful for the prox. > > source("f02.R") #here the...

...ed errors are accpetable but I need to use the actual tree and need a single tree application. I am using rpart for a classification tree but am interested in a more unbaised estimator of error in my tree. I lack sufficent data to train and test the tree and I'm hoping to bootstrap, or rather jacknife, an error estimate. I do not think the rpart.object can be applied to the jackknife function in bootstrap but can I do something as simple as: for(i in 1:number of samples){ remove i from the data run the tree compare sample[i] to the tree using predict create an error matrix} This woul...

...List I am using R with mcgv package to model spatial variation in density estimates of dorcas gazelle in Sinai. I have 59 points of data and 4 explanatory variables(distance from mountain edge, camel presence, Latitude & Longitude). I want to test the model fir via bootstraping. I have used the jacknife bootstraping but it have the limitation of allowing only 58 trials. I tried to use the bootstrap function in R but I couldn;t understand the help file description. Please forgive my novice with such advance statistics since I am a biologist. I would appreciate any help on advicing on how to use th...

...large scale survey data which requires weighting for accurate results. I attempted to create a weighted object to insert into the formulae of the ltm packages, however, the survey data only includes 30 replicate weights and a sampling weight. The svrepdesign requires additional information such as jacknife weights in an external object (for scale, rscale), however, I do not have these. I typically just use the sampling weight when doing analyses in other platforms such as MPlus, however there does not appear to a way of referring to this directly in the ltm formulae. My question is: am I able to in...

Dear R users, I?m student of Master in Statistic and Data analysis, in New University of Lisbon. And now i?m writting my dissertation in variance estimation.So i?m using Survey Package to compute the principal estimators and theirs variances. My data is from Incoming and Expendire Survey. This is stratified Multi-stage Survey care out by National Statistic Institute of Mozambique. My domain of

...much and wonder how R experts would optimize this. I think that the for (i in 1:N_B) part is bad because Rprof() said sum() is called very often but I have no idea how to optimize it. #O <- read.table("foo.dat")$V1 O <- runif(100000); k=100 # size of block to delete # the jacknife block has size N-k total_sum=sum(O); for (k in 1:2) { N_B = length(O) %/% k; N = N_B*k; # truncate data size to multiple of k # data beyond O[N] is not used #total_sum = sum(O[1:N]) # truncate data size N (which is a multiple of k) delete_block_sums = rep(0, N_B...

Hi dear R-helpers, Happy new year. I have estimated Gini index variance in the case of complex survey (stratify and with two stage sampling). I use the survey package. I use replication (bootstrap, and Jacknife) and linearisation according to Binder (1993) article David A. Binder and Milorad Kovaˇcevi´c(1993), « Estimating some measures of income inequality from survey data : An application of estimating equation approach », Proceedings of the ASA Survey Research Methods, 550-555. But I found...

...efficients <- fit$coef <- fit1$coefficients fit } The first fit1<-coxph() estimate the coefficient and second fit<-coxph() which seems a SelfPrentice method to estimate the variance. My question is why second coxph() estimate Prentice variance using SelfPrentice method. Should the jacknife variance of Prentice be implemented like: fit1 <- coxph(Surv(ent2,texit,cens)~X+cluster(id),eps=eps,x=TRUE) Th other question is unique id is not necessary in whole program, Can we just allow multiple ids without affecting the final result? [[alternative HTML version deleted]]

Hello, please excuse the simplicity of this question as I am not very good with stats. I am taking a class, using R which I am learning at the same time, and the questions asks us to "Estimate the average abundance using Poisson regression with a log link". I can estimate the abundance from "x", but I can seem to figure out how to get the average abundance in this method. Any