similar to: Bootstrapping nlme models

Hi Gang, I realize this post is not directly related to programing issues with R, however, it appears this may be the best place to ask my question. I am putting forward a request that R be considered "approved" software in my organization. Never an easy task, this is made much more difficult given that it is open source software (sends the IT gang into little fits). So, I am

Hi, I was wondering whether this query was addressed on how to perform validation through boostrapping. I am currently trying to implement a boostrapping approach to validation but don't know where to start. Help please. Thank you and Regards, Vivienne Ozohili Risk Model Validation Manager Group Risk Independent Control Unit A5, Bank of Ireland Head Office Lower Baggot Street Dublin 2 Tel:

Hello, I'm new to boostrapping and I'd need some help to understand the error message that pops up when I run my script. I have a data.frame with 73 lines and 21 column. I am running a stepwise regression to find the best model using the R function "step". I apply bootstrapping to obtain model coefficients. This is my script: # "datare80" is the name of the

I'm using /parApply/() function in "snow" package for parallel computing (boostrapping repeating calculation), as the follows: MyBoostrapping <- function( i ){ ..... } cl <- makeCluster( ncluster, type = "SOCK") i.circle <- as.matrix( 1:128, , 1) parApply( cl, i.circle, 1, FUN = MyBoostrapping) It works. However, I want to set more than one arguments to

Hi all, I need to estimate S.E. of a certain indicator. The function to compute the value of indicator contains two arguments. Can anybody tell me how to do it? Example: We have data: a <- c(1:10) b <- c(11:20) data <- data.frame(a, b) Function to compute value of the indicator: indicator <- function(X, Y) sum(X)/(sum(Y)*2) Next I need to do the

I realize questions about packages should go to the package maintainer, but perhaps I have an old email address (suzuki3 at is.titech.ac.jp) Also I have both a general, and a specific, question. 1) General question: i've used pvclust before to assess significance of clusters and got reasonable results. However, on a new data set (see below) the results seem odd. I wonder if pvclust is a

Hi All, I would like to do double boostrapping to estimate 95% CI coverage. So, I can only need to estimate 95% confidence interval from each bootstrapped sample. Since we don't have a closed form of 95% CI, in order to get 95% CI for each sample, we need to use bootstrapping. For outer bootstrapping, I used boot function in library(boot). How can I add double bootstrap inside the function.

Hi, I'd like some advice on bootstrapping in R. I have a species x with 20 individuals and a factor containing 0 and 1's (in this case 5 zeros and 15 ones). I want to compare the frequency of the occurrence of 1 with a probability value. This code seems to work to do this in R. attach(test) p <- c(0.5272, (1-0.5272)) sp1_1 <- length(subset(x, x==1)) sp1_0 <- length(subset(x,

Hello, I've started using R few months ago and I really like it. I need to estimate standard deviation of certain statistics (some measures of poverty). I found a really simple program, and I just need to check whether it's OK and really calculates what it's supposed to. Let's suppose e. g. head-count index (as one of the simplest measures of poverty) which is calculated as a

On Mon, Oct 17, 2016 at 11:28 AM, Renato Golin via llvm-dev < llvm-dev at lists.llvm.org> wrote: > On 17 October 2016 at 19:09, Flamedoge via llvm-dev > <llvm-dev at lists.llvm.org> wrote: > > Just for the interest of discussion, I find it completely weird and > > interesting that GCC needs to build itself 3 times to fully bootstrap. > Has > > there been any

Dear all, I would like to Boostrap the stepAIC() procedure from package MASS for variety of model objects, i.e., fn <- function(object, data, B = 2){ n <- nrow(data) res <- vector(mode = "list", length = B) index <- sample(n, n * B, replace = TRUE) dim(index) <- c(n, B) for (i in 1:B) { up.obj <- update(object, data = data[index[, i], ])

>>>>> Kevin Ushey <kevinushey at gmail.com> >>>>> on Fri, 29 Jul 2016 11:46:19 -0700 writes: > I should add one more item that may be related here -- > calling 'methods:::.requirePackage' returns a different > result based on whether the package namespace is already > loaded or not. > If the package namespace is

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, I want to permutate the following matrix and replace permutated columns. Is it possible to control the number of columns permutated. Let's say I only want to permute two columns. Can i do that with the sample method or should i any bootstrapping method ?? I'm not sure this is the best statisticaly way of doing it...?? So the idea behind is to ramdonly generate 1000 permutated

Dear R-Users, we are using a linux-cluster with RMPI and the snow package. We would like to do a double boostrap. We have a general function that implements the first boostrap (the outer) and we are wondering if we can include another bootstrap (the inner) in the same general function including another clusterApplyLB. For example: general function = function(...) { clusterApplyLB(cl,

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 >

Hi Martin, John, Thanks for the responses! I've tidied up some of the notes from this mailing list thread and posted them on the bug tracker. John, in this case, I think namespaces are relevant because for non-exported S4 classes, the class information is made available through the '.__C__<package>' symbol in the package's namespace, but not the package environment that

Hello. I've got a dataset that may have outliers in both x and y. While I am not at all familiar with robust regression, it looked like the function lmrob in package robustbase should handle this situation. When I try to use it, I get: Too many singular resamples Aborting fast_s_w_mem() Looking into it further, it appears that for an indicator variable in one of my interaction terms, 98%

Dear all, Does someone know an R function implementing the method of Sison and Glaz (1995) (see full ref below) for computing Simultaneous confidence intervals for multinomial proportions? As alternative method, I think to boostrap the mean of each proportion and get in that way confidence interval of the mean. I observed 21 times a response that could be one out of 8 categories

Say I train a model in caret, e.g.: RFmodel <- train(X,Y,method='rf',trControl=myCtrl,tuneLength=1) How can I save this to disk and load it later in R? How about an object of the class "resamples"? resamps <- resamples( list( RF = RFmodel, SVM = SVMmodel, KNN = KNNmodel, NN = NNmodel )) Thanks,