similar to: double bootstrap

Hi, I have a problem in programming for bootstrapping. I don't know why it show the error message. Please see my code below: #'st' is my original dataset. #functions of 'fml.mlogl','pcopula.fam4','ltd','invltd' are already defined boot.OR<-function(data,i) { E=data[i,] ml1<-glm(c_VAsex90_bf ~ trt,family=binomial,data=E) ml2<-glm(c_VAsex90_bm ~

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

Hi R-users, I have a problem for updating the estimates of correlation coefficient in simulation loop. I want to get the matrix of correlation coefficients (matrix, name: est) from geese by using loop(500 times) . I used following code to update, nsim<-500 est<-matrix(ncol=2, nrow=nsim) for(i in 1:nsim){ fit <- geese(x ~ trt, id=subject, data=data_gee, family=binomial,

Dear Faiz, Bootstrapping R^2 using Boot() is straightforward: Simply write a function that returns R^2, possibly in a vector with the regression coefficients, and use it as the f argument to Boot(). That will get you, e.g., bootstrapped confidence intervals for R^2. (Why you want that is another question.) See the example in ?Boot that shows how to bootstrap the estimated error variance (without

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 am using the boot package for some bootstrap calculations in place of anovas. one reason is that my dependent variable is distributed bimodally, but i would also like to learn about bootstrapping in general (i have ordered books but they have not yet arrived). i get the general idea of bootstrapping but sometimes i do not know how to define suitable statistics to test specific hypotheses.

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,

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:

I want to use boot.ci to generate confidence intervals over the bootstrapped mean(s) of a group of observations (i.e. I have 10 observations and I want to know how confident I can be on the value for the mean). I don't know (or want to know) the details of bootstrapping - I just have the simplistic idea of taking samples, measuring a statistic on the sample, and getting some confidence in 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

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, 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

Dear Friends and Colleagues, I'm puzzling over how to interpret or use some bootstrap intervals. I think that I know what I should do, but I want to check with knowledgeable people first! I'm using a studentized non-parametric bootstrap to estimate 95% confidence intervals for three parameters. I estimate the variance of the bootstrap replicates using another bootstrap. The script

I have an advanced question about bootstrapping. There are two datasets. In each bootstrap iteration, I would like to sample One observation per cluster from the first dataset. N observations with replacement from the second dataset. Right now I am using dplyr::sample_n() for first dataset, with this sampling embedded in the program that boot() from the boot package is running to sample the

Hi, Let me start my saying that I am new to R hence my grasp of the appropriate used of R coding is undoubtedly way behind many on this forum. I am trying to use boostrapping to derive errors around my parameter estimate for the fixed effects in the following model. It is simply estimating the number of times an animal might cross a road based on the road's distance from a stream. I have

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

Good Morning: I posted an initial question a few days ago and I received some good advice from two R experts. I have re-examined the Davison-Hinkley text paying close attention to the examples of the boot() and boot.ci() in that text and the single example of a similar process in the MASS book (not the MASS package manual as I initially misunderstood). I think I understand how the stratified

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,

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

Hi, this is a question about bootstrapping, it relates more to the concept than to the R package boot. But I wonder below if boot can help me. I have the below to calculate a certain point estimate: estimate= (0.9 * 0.03 * 700000 * (((77 * (76 / 76.0)) / 83107) - ((174 * (154 / 154.0)) / 376354))) = 8.77311 Now I want to calculate confidence intervals around that estimate so I thought that