statistics-bootstrap

问题 I would like to do a large analysis using bootstrapping. I saw that the speed of bootstrapping is increased using parallel computing as in the following code: Parallel computing # detect number of cpu library(parallel) detectCores() library(boot) # boot function --> mean bt.mean <- function(dat, d){ x <- dat[d] m <- mean(x) return(m) } # obtain confidence intervals # use parallel computing with 4 cpus x <- mtcars$mpg bt <- boot(x, bt.mean, R = 1000, parallel = "snow", ncpus = 4) quantile(bt$t

问题 I would like to do a large analysis using bootstrapping. I saw that the speed of bootstrapping is increased using parallel computing as in the following code: Parallel computing # detect number of cpu library(parallel) detectCores() library(boot) # boot function --> mean bt.mean <- function(dat, d){ x <- dat[d] m <- mean(x) return(m) } # obtain confidence intervals # use parallel computing with 4 cpus x <- mtcars$mpg bt <- boot(x, bt.mean, R = 1000, parallel = "snow", ncpus = 4) quantile(bt$t

问题 I would like to do a large analysis using bootstrapping. I saw that the speed of bootstrapping is increased using parallel computing as in the following code: Parallel computing # detect number of cpu library(parallel) detectCores() library(boot) # boot function --> mean bt.mean <- function(dat, d){ x <- dat[d] m <- mean(x) return(m) } # obtain confidence intervals # use parallel computing with 4 cpus x <- mtcars$mpg bt <- boot(x, bt.mean, R = 1000, parallel = "snow", ncpus = 4) quantile(bt$t

问题 |Hi guys, I am asking you for help as I am stucked with bootstrapping... The task is: Use the nonparametric bootstrap to compute bootstrap standard error of CAPM beta estimate based on 1000 bootstrap replications and bootstrap sample size equal to the size of the original sample. If I understand it correctly, I am supposed to run my regression model 1000 times to estimate different estimates of the beta and its standard error. However, I am not able to put my thoughts into an actual R code.

问题 |Hi guys, I am asking you for help as I am stucked with bootstrapping... The task is: Use the nonparametric bootstrap to compute bootstrap standard error of CAPM beta estimate based on 1000 bootstrap replications and bootstrap sample size equal to the size of the original sample. If I understand it correctly, I am supposed to run my regression model 1000 times to estimate different estimates of the beta and its standard error. However, I am not able to put my thoughts into an actual R code.

问题 |Hi guys, I am asking you for help as I am stucked with bootstrapping... The task is: Use the nonparametric bootstrap to compute bootstrap standard error of CAPM beta estimate based on 1000 bootstrap replications and bootstrap sample size equal to the size of the original sample. If I understand it correctly, I am supposed to run my regression model 1000 times to estimate different estimates of the beta and its standard error. However, I am not able to put my thoughts into an actual R code.

问题 (I'm new to this so now editing my question as a reproducible example). I've reviewed bootstrapping,loop and replicate functions and can't figure out how to repeat a series of steps (not just a single function) in R and store the result in dataframe. I need to randomly select 2 values from a pool of 22 values, 9 times. Then conduct a spearman rank correlation test on that dataset (2 columns, 9 rows) 10,000 times and store the value of each of those iterations. So I need to repeat these steps

问题 I have a very unbalanced dataset ,, thousands of healthy participants and 21 patients (16 male and 5 female) ,, I want to use bootstrapping to define a new sampler but with control of age and gender. this is the method i'm using parametric_bootstrap_boot <- function(x) { # Perform bootstrap using boot package # Estimate mean mu <- boot(x, samplemean, R=1000)$t0 #Estimate sd sd <- boot(x, samplesd, R=1000)$t0 # Sample 21 observations set.seed(1) samples <- rnorm(21,mu,sd) return(samples) } how

问题 I have insufficient knolwedge on the use of tryCatch() but have found it hard to find a good solution in the ongoing discussions. I have a self-programmed function which returns an object. It is actually a list object, but for simplicity assume it is a scalar. I am using a for() loop to bootstrap this function. My loop is of the following form: boot<-1000 for(i in 1:boot){ bootstrap_data<-data[sample(nrow(data),nrow(data),replace=T),] out[i]<-myfunction(bootstrap_data,X,...) } myfunction()

问题 I have performed a bootstrapping with 2.000 resamples of the Lee Carter model for mortality projection. The question is not specific for mortality studies, but on more general dimensions in R. After performing the bootstrapping I get a list with 2000 elements, each for every of 2.000 re-estimations of the model. For each model, there are estimates of my 3 variables: a_x, b_x and k_t. Both a_x and b_x are age-specific, so the "x" denotes an age in the interval [0:95]. I would now like to plot