search for: bscoefsampl

.... The textbook mentioned using bootstrap samples of regression coefficients for assessing variability. I decided to show them reasonably effective ways of doing the resampling. The following is a function I wrote to create bootstrap samples of coefficients from a fitted linear regression model. bsCoefSample <- ## Construct a bootstrap sample of coefficients from a ## fitted regression model function(fittedModel, Nsampl, ...) { coef(fittedModel) + qr.coef(qr(model.matrix(fittedModel)), matrix(sample(resid(fittedModel), length(resid(fittedModel)) * Nsampl, repl =...

...fitted model, then taking the QR decomposition of that. I discovered that it was in fact easier to accomplish the bootstrapping in R because the QR decomposition of the model matrix is stored with the fitted model. So far, so good. Then I got fancy and returned the result as a data frame. "bsCoefSample" <- ## Construct a bootstrap sample of coefficients from a ## fitted regression model function(fittedModel, Nsampl) { value <- as.data.frame(t(coef(fittedModel) + qr.coef(fittedModel$qr, matrix(sample(resid(fittedModel), length(resid(fittedModel)) * Nsam...