similar to: Jackknife after bootstrap influence values in boot package?

Hi dear all, Can someone help me about detection of outliers using jackknife after bootstrap algorithm? -- View this message in context: http://r.789695.n4.nabble.com/jackknife-after-bootstrap-tp3041634p3041634.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]]

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

I'm using the lme function in nmle to estimate the variance components of a fully nested two-level model: Y_ijk = mu + a_i + b_j(i) + e_k(j(i)) lme computes estimates of the variances for a, b, and e, call them v_a, v_b, and v_e, and I can use the intervals function to get confidence intervals. My understanding is that these intervals are probably not that robust plus I need intervals on the

Hi R-experts, I want to use the jackknife function from the bootstrap package onto a linear model. I can't figure out how to do that. The manual says the following: # To jackknife functions of more complex data structures, # write theta so that its argument x # is the set of observation numbers # and simply pass as data to jackknife the vector 1,2,..n. # For example, to jackknife #

The Intel compiled version also fails the below test: > ###------------ Very big and very small > umach <- unlist(.Machine)[paste("double.x", c("min","max"), sep='')] > xmin <- umach[1] > xmax <- umach[2] > tx <- unique(outer(-1:1,c(.1,1e-3,1e-7)))# 7 values (out of 9) > tx <- unique(sort(c(outer(umach,1+tx))))# 11 values

Hi, First, thanks to those who helped me see my gross misunderstanding of randomForest. I worked through a baging tutorial and now understand the "many tree" approach. However, it is not what I want to do! My bagged 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

It appears that the maximum number of connections available in R is about 48. Can anyone tell me how to bump this number up? I've been perusing the source, but any info would speed things up. Is there a reason that it was set to such a low number? Thanks for any help. -Frank

I checked the help and the mailing list archives, but I can find no mention of a routine that calculates higher moments like skewness and kurtosis. Of course, these are easy enough to write myself, but I was thinking that they MUST be in here. Am I wrong? Thanks. -Frank

Hi dear all, Can aynone help me about delete-d jackknife usually normal jackknife code for my data is: n <- nrow(data) y <- data$y z <- data$z theta.hat <- mean(y) / mean(z) print (theta.hat) theta.jack <- numeric(n) for (i in 1:n) theta.jack[i] <- mean(y[-i]) / mean(z[-i]) bias <- (n - 1) * (mean(theta.jack) - theta.hat) print(bias) but how i can apply delete-d jackknife

Dear all I have used the lda and predict functions to classify a set of objects of unknown origin. I would like to use a jackknife reclassification to assess the degree to which the outcomes deviate from that expected by chance. However, I can't find any function that allows me to do this. Any suggestions of how to generate the jackknife reclassification to assess classification accuracy?

I'm using R 2.15.1 on a 64-bit machine with Windows 7 Home Premium and package 'boot'. I've found that using a number of bootstrap resamples in boot() that is less than the number of data results in a fatal error. Once the number of resamples meets or exceeds the number of data, the error disappears. Sample problem (screwy subscripted syntax is a relic of edited down a more

Hi All, I'm not able to figure out how to perform a Jackknife test for a 2-sample dispersion test in R. Is there a built-in function to perform this or do we have to take a step by step approach to calculate the test statistic? Any help would be awesome. Thanks! -- View this message in context: http://r.789695.n4.nabble.com/Jackknife-for-a-2-sample-dispersion-test-tp4444274p4444274.html

Hi, I have the following jackknife code which is much slower than my colleagues C code. Yet I like R very 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

I'm running R-2.0.0 (yesterday's snapshot)in its own directory, and everything works great, except: > .Random.seed Error: Object ".Random.seed" not found Does 2.0.0 not use .Random.seed for saving, etc, like it says in the help page? Thanks for any help. -Frank

Insightful are pleased to announce we are now taking bookings for our latest course on "Bootstrap methods and permutation tests" in the UK on 23rd - 24th October. This course will focus in particular on two resampling methods, bootstrapping and permutation tests, which have been applied successfully to areas of statistical modelling where "traditional" standard errors,

Folks, Thanks for the help with the hier.part analysis. All the problems stemmed from an import problem which was solved with file.chose(). Now that I have the variables that I'd like to use I need to run some GLM models. I think I have that part under control but I'd like to use a jackknife approach to model validation (I was using a hold out sample but this seems to have fallen out

Greetings, I?ve fit the following mixed effects model using the NLME package: hd.impute.lme <- lme(I(log(HEIGHT_M - 1.37)) ~ SPECIES + SPECIES:I(1/(DBH_CM + 2.54)), random = ~ I(1/(DBH_CM + 2.54)) | PLOTID, data = trees, na.action = na.exclude) I would now like to extract a jackknife estimate of model error. I tried the following code, however, the estimate produced seems too

Dear R friends I´m interested into apply a Jackknife analysis to in order to quantify the uncertainty of my coefficients estimated by the logistic regression. I´m using a glm(family=’binomial’) because my independent variable is in 0 - 1 format. My dataset has 76000 obs, and I´m using 7 independent variables plus an offset. The idea involves to split the data in let’s say 5 random subsets and

I'm using R 2.15.1 on a 64-bit machine with Windows 7 Home Premium. Sample problem (screwy subscripted syntax is a relic of edited down a more complex script): > N <- 25 > s <- rlnorm(N, 0, 1) > require("boot") Loading required package: boot > v <- NULL # hold sample variance estimates > i <- 1 > v[i] <- var(s) # get sample variance >

Is there a way to tell how much memory the computer running R has? -Frank -----Original Message----- From: David Smith [mailto:dsmith at insightful.com] Sent: Monday, February 09, 2004 1:32 PM To: Ross Boylan Cc: r-help Subject: RE: [R] R does in memory analysis only? Ross Boylan writes: > R works only on problems that fit into (real or virtual) memory. > ... does S-Plus have the same