statistics-bootstrap

I am trying following code to produce confidence intervals of principal component analysis using resampling with replacement (like bootstrap). I am using first 4 columns of iris dataset: The prcomp function produces following output: > mydf = iris[1:4] > print(prcomp(mydf)) Standard deviations: [1] 2.0562689 0.4926162 0.2796596 0.1543862 Rotation: PC1 PC2 PC3 PC4 Sepal.Length 0.36138659 -0.65658877 0.58202985 0.3154872 Sepal.Width -0.08452251 -0.73016143 -0.59791083 -0.3197231 Petal.Length 0.85667061 0.17337266 -0.07623608 -0.4798390 Petal.Width 0.35828920 0.07548102 -0.54583143 0.7536574

This question was migrated from Cross Validated because it can be answered on Stack Overflow. Migrated 6 years ago . I would estimate the coverage of the bootstrap interval for the mean knowing that the true average is 895.0385 . I have my vector b<-c(300,300,200,250,600...) and I make bootstrap and output interval: mean.fun <- function(dat, idx) mean(dat[idx], na.rm = TRUE) boot.out <- boot(b, mean.fun, R=999) boot.ci(boot.out) But how I can replicate this in order to obtain the coverage probability (how many times it contained the true average)? I was trying to do something a bit like this a

I need to run a bootstrap on a time series with non-standard dependence. So to do this I need to create a function that simulates the time series by making time by time adjustments. testing<-function(){ sampleData<-as.zoo(data.frame(index=1:1000,vol=(rnorm(1000))^2,x=NA)) sampleData[,"x"]<-sampleData[,"vol"]+rnorm(1000) #treat this is completely exognenous and unknown in connection to vol sampleData<-cbind(sampleData,mean=rollmean(sampleData[,"vol"],k=3,align="right")) sampleData<-cbind(sampleData,vol1=lag(sampleData[,"vol"],k=-1),x1=lag(sampleData[,"x"],k=-1),mean1=lag(sampleData[,"mean"],k=

First some background info, which is probably more interesting on stats.stackexchange: In my data analysis I try to compare the performance of different machine learning methods on time series data (regression, not classification). So for example I have trained a Boosting trained model and compare this with a Random Forest trained model (R package randomForest). I use time series data where the explanatory variables are lagged values of other data and the dependent variable. For some reason the Random Forest severely underperforms. One of the problems I could think of is that the Random Forest

I am attempting to use boot.ci from R's boot package to calculate bias- and skew-corrected bootstrap confidence intervals from a parametric bootstrap. From my reading of the man pages and experimentation, I've concluded that I have to compute the jackknife estimates myself and feed them into boot.ci , but this isn't stated explicitly anywhere. I haven't been able to find other documentation, although to be fair I haven't looked at the original Davison and Hinkley book on which the code is based ... If I naively run b1 <- boot(...,sim="parametric") and then boot.ci(b1) , I get the error

I would like to get 95% confidence intervals for the regression coefficients of a quantile regression. You can calculate quantile regressions using the rq function of the quantreg package in R (compared to an OLS model): library(quantreg) LM<-lm(mpg~disp, data = mtcars) QR<-rq(mpg~disp, data = mtcars, tau=0.5) I am able to get 95% confidence intervals for the linear model using the confint function: confint(LM) When I use quantile regression I understand that the following code produces bootstrapped standard errors: summary.rq(QR,se="boot") But actually I would like something like 95%