boot_ci {quest}

Bootstrapped Confidence Intervals from a Matrix of Coefficients

Description

boot_ci computes bootstrapped confidence intervals from a matrix of coefficients (or any statistical information of interest). The function is an alternative to confint2.boot for when the user does not have an object of class boot, but rather creates their own matrix of coefficients. It has limited types of bootstrapped confidence intervals at the moment, but future versions are expected to have more options.

Usage

boot_ci(coef, est = colMeans(coef), boot.ci.type = "perc2", level = 0.95)

Arguments

coef

numeric matrix (or data.frame of numeric columns) of coefficients. The rows correspond to each bootstrapped resample and the columns to different coefficients. This is the equivalent of the "t" element in a boot object.

est

numeric vector of observed coefficients from the full sample. This is the equivalent of the "t0" element in a boot object. The default takes the mean of each coefficient across bootstrapped resamples; however, this usually results in small amount of bias in the coefficients.

boot.ci.type

character vector of length 1 specifying the type of bootstrapped confidence interval to compute. The options are 1) "perc2" for the naive percentile method using quantile, and 2) "norm2" for the normal method that uses the bootstrapped standard error to construct symmetrical confidence intervals with the classic formula around the estimate, The options have a "2" after them because, although they are conceptually similar to the "perc" and "norm" methods in the boot.ci function, they are slightly different mathematically.

level

double vector of length 1 specifying the confidence level. Must be between 0 and 1.

Value

data.frame will be returned with nrow equal to the number of coefficients bootstrapped and columns specified below. The rownames are the colnames in the coef argument or the names in the est argument (default data.frame rownames if neither have any names). The columns are the following:

est

original parameter estimates

se

bootstrapped standard errors (does not differ by boot.ci.type)

lwr

lower bound of the bootstrapped confidence intervals

upr

upper bound of the bootstrapped confidence intervals

See Also

boot.ci for the confidence interval function in the boot package, confint.boot for an alternative function with boot objects

Examples

tmp <- replicate(n = 100, expr = {
   i <- sample.int(nrow(attitude), replace = TRUE)
   colMeans(attitude[i, ])
}, simplify = FALSE)
mat <- str2str::lv2m(tmp, along = 1)
boot_ci(mat, est = colMeans(attitude))