similar to: bootstrap

Hi, I am trying to load into R a MATLAB format file (actually, as saved by octave). The file is about 300kB but R complains with a memory allocation error: > library(Rcompression) > library(R.matlab) Loading required package: R.oo Loading required package: R.methodsS3 R.methodsS3 v1.2.0 (2010-03-13) successfully loaded. See ?R.methodsS3 for help. R.oo v1.7.2 (2010-04-13) successfully

Hi, I am trying to convert a colleague from using SPSS to R, but am having trouble generating a result that is similar enough to a bootstrapped cox regression analysis that was run in SPSS. I tried unsuccessfully with bootcens, but have had some success with the bootcov function in the rms package, which at least generates confidence intervals similar to what is observed in SPSS. However, the

I am trying to work out a bootstrapped Tobit regression model. I get the coefficients all right, but they all have standard error zero. And I am unable to figure out why. I know the coefficients are correct because that's what I get when do a Tobit (without bootstrapping). Here's my code: # Bootstrap 95% CI for Tobit regression coefficients? library(boot) library(AER) # for the Affairs

Hello alls, I found in the literature a technique that has been evaluated as one of the more robust to assess statistically the significance of the loadings in a PCA: bootstrapping the eigenvector (Jackson, Ecology 1993, 74: 2204-2214; Peres-Neto and al. 2003. Ecology 84:2347-2363). However, I'm not able to transform by myself the following steps into a R program, yet? Can someone could help

I apologize for this post. I am new to R (two days) and I have tried and tried to calculated confidence intervals for medians. Can someone help me? Here is my data: institution1 0.21 0.16 0.32 0.69 1.15 0.9 0.87 0.87 0.73 The first four observations compose group 1 and observations 5 through 9 compose group 2. I would like to create a bootstrapped 90% confidence interval on the difference of

I have executed a Repeated Measures ANOVA with one DV (latency) and one within subject factor (acoustic condtion: 3 levels) by bootstrapping my sampling distribution of F from the empirical sample distribution. I chose to resample because the sample distribution deviates from normality a lot. The overall F is significant and now I wish to decompose this with contrasts to ask if latencies to

Dear list, In a mixed model I selected I find a non positive definite random effects variance-covariance matrix G, where some parameters are estimated close to zero, and related confidence intervals are incredibly large. Since simplification of the random portion is not an option, for both interest in the parameters and significant increase in the model fit, I would like to collect

Hi all, I am trying to bootstrap a small data set into 1000 "pseudodatasets" and then run an ANOVA on each one. Can anyone provide guidance on how I could do this? Thank you. -Dan Janes ************************************************ Dan Janes, Ph.D. Harvard University/OEB 26 Oxford St. Cambridge, MA 02138 Office: 617-496-2375 Fax: 617-495-5667 Email: djanes at oeb.harvard.edu

Dear Friends, I'm trying to learn to how to get Bootstrapped standard errors for estimated coefficients from a regression. For instance suppose I have the following model logitmodel <- glm (y~X1+X2+X3, family=binomial(link="logit")) beta <- logitmodel$coef can somebody please guide me on how to use the package boot to obtain bootstrapped SE's for the associated betas.

I like to bootstrap regression models, saving the entire set of bootstrapped regression coefficients for later use so that I can get confidence limits for a whole set of contrasts derived from the coefficients. I'm finding that ordinary bootstrap percentile confidence limits can provide poor coverage for odds ratios for binary logistic models with small N. So I'm exploring BCa confidence

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, the following questions will without doubt reveal some fundamental ignorance, but hopefully you can still help me out. I'd like to bootstrap a coefficient gained on the basis of the coefficients in a logistic regression model (the mean differences in the predicted probabilities between two groups, where each predict() operation uses as the newdata-argument a dataframe of equal size as

Dear R-users, I want to perform a One-Sample parametric bootstrapped Kolmogorov-Smirnov GoF test (note package "Matching" provides "ks.boot" which is a 2-sample non-parametric bootstrapped K-S version). So I wrote this code: ---[R Code] --- ks.test.bootnp <- function( x, dist, ..., alternative=c("two.sided", "less", "greater"), B = 1000 ) {

Hello all, I am trying to optimize my logistic regression model by using bootstrap. I was previously using SAS for this kind of tasks, but I am now switching to R. My data frame consists of 5 columns and has 109 rows. Each row is a single record composed of the following values: Subject_name, numeric1, numeric2, numeric3 and outcome (yes or no). All three numerics are used to predict

Hi R helpers, I was wondering if anybody knows if is possible to generate bootstrap values for a cluster analysis in R. What I am trying to do is obtain some confidence on the clusters formed by resampling the data set. A similar type of analysis is used in molecular taxonomy and the confidence values of each cluster are placed in the nodes of the dendogram. Any ideas on how to do this

On Fri, 18 Nov 2005, Jeff Cohen wrote: > If copyright is assigned to the FSF, would they change the license to GPL? We would work something out. That is a huge 'if'. > Also, would they be allergic to the VC++ suppport that currently exists? > After all, if they're not careful, going down this route may result in a gcc > that can be bootstrapped with VC++ :) I

G'Day R users! Following an ordination using prcomp, I'd like to test which variables singnificantly contribute to a principal component. There is a method suggested by Peres-Neto and al. 2003. Ecology 84:2347-2363 called "bootstrapped eigenvector". It was asked for that in this forum in January 2005 by J?r?me Lema?tre: "1) Resample 1000 times with replacement entire

Hello there, I have a problem concerning bootstrapping in R - especially focusing on the resampling part of it. I try to sum it up in a simplified way so that I would not confuse anybody. I have a small database consisting of 20 observations (basically numbers from 1 to 20, I mean: 1, 2, 3, 4, 5, ... 18, 19, 20). I would like to resample this database many times for the bootstrap process with

I quoted the relevant part of the documentation for you. Have you actually try to read what it says? Sure, you don't get any error, but have you checked whether any bootstrapping was actually done? Most of those functions are generics, thus having the "..." argument that can take anything. Doesn't mean they will be used. See if the following helps: > x <- 1:10 >

Dear R-list, I am wondering how to perform a bootstrap in R for the weighted time dependent Cox model? (Andersen?Gill format, with multiple observations from each patients) to obtain the bootstrap standard error of the treatment effect. Below is an example dataset. Would 'censboot' be appropriate to use in this context? Any suggestions/references/direction to R-package will be highly