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Convenience wrapper for calculating bootstrap confidence intervals for univariate and bivariate statistics.
BootCI(x, y = NULL, FUN, ..., bci.method = c("norm", "basic", "stud", "perc", "bca"),
conf.level = 0.95, sides = c("two.sided", "left", "right"), R = 999)a (non-empty) numeric vector of data values.
NULL (default) or a vector with compatible dimensions to x, when a bivariate statistic is used.
the function to be used
A vector of character strings representing the type of intervals required. The value should be any subset of the values "norm", "basic", "stud", "perc", "bca", as it is passed on as method to boot.ci.
confidence level of the interval.
a character string specifying the side of the confidence interval, must be one of "two.sided" (default), "left" or "right". You can specify just the initial letter. "left" would be analogue to a hypothesis of "greater" in a t.test.
further arguments are passed to the function FUN.
The number of bootstrap replicates. Usually this will be a single positive integer. For importance resampling,
some resamples may use one set of weights and others use a different set of weights. In this case R would be a vector
of integers where each component gives the number of resamples from each of the rows of weights.
a named numeric vector with 3 elements:
the specific estimate, as calculated by FUN
lower bound of the confidence interval
upper bound of the confidence interval
# NOT RUN {
set.seed(1984)
BootCI(d.pizza$temperature, FUN=mean, na.rm=TRUE, bci.method="basic")
BootCI(d.pizza$temperature, FUN=mean, trim=0.1, na.rm=TRUE, bci.method="basic")
BootCI(d.pizza$temperature, FUN=Skew, na.rm=TRUE, bci.method="basic")
BootCI(d.pizza$operator, d.pizza$area, FUN=CramerV)
spearman <- function(x,y) cor(x, y, method="spearman", use="p")
BootCI(d.pizza$temperature, d.pizza$delivery_min, FUN=spearman)
# }
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