Bootstrapped mean and its confidence interval

Function calculates bootstrapped mean (or other function) and its confidence interval for a vector x.

boot_ci_mean(x, conf = 0.95, R = 1000, sim = "balanced", type = c("norm"))

boot_ci_fun(
  x,
  FUN,
  conf = 0.95,
  R = 1000,
  sim = "balanced",
  type = c("norm"),
  label = as.character(match.call()$FUN)
)

boot_ci_corr(
  x,
  y = NULL,
  method = c("spearman", "kendall", "pearson")[1],
  use = "everything",
  conf = 0.95,
  R = 1000,
  sim = "balanced",
  type = c("norm"),
  label = "corr_coef"
)

Arguments

x

a vector.

conf

A scalar or vector containing the confidence level(s) of the required interval(s).

R

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.

sim

A character string indicating the type of simulation required. Possible values are "ordinary" (the default), "parametric", "balanced", "permutation", or "antithetic". Importance resampling is specified by including importance weights; the type of importance resampling must still be specified but may only be "ordinary" or "balanced" in this case.

type

A vector of character strings representing the type of intervals required. The value should be any subset of the values c("norm","basic", "stud", "perc", "bca") or simply "all" which will compute all five types of intervals.

FUN

a function, that takes a vector returns one number, e.g. mean, median, etc.

label

(string) a label for function to be used as column name.

y

a vector.

method

a character string indicating which correlation coefficient (or covariance) is to be computed. One of "pearson" (default), "kendall", or "spearman": can be abbreviated.

use

an optional character string giving a method for computing covariances in the presence of missing values. This must be (an abbreviation of) one of the strings "everything", "all.obs", "complete.obs", "na.or.complete", or "pairwise.complete.obs".

Value

A data frame with bootstrapped mean and its confidence interval.

Details

boot_ci_corr calculates confidence interval for correlation coefficient between vectors x and y.

Examples

set.seed(1)
x <- rnorm(1000, mean = .5, sd = .1)

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
set.seed(1)
boot_ci_mean(x)
#>    ci_lower      mean  ci_upper
#> 1 0.4923657 0.4988352 0.5053046

#       ci_lower  mean     ci_upper
#  1   0.4923028 0.4988352 0.5053676

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
set.seed(1)
boot_ci_fun(x, IQR)
#>   ci_lower       IQR  ci_upper
#> 1 0.131046 0.1385801 0.1483449

#     ci_lower       IQR   ci_upper
# 1   0.1307229 0.1385801 0.1486593

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
set.seed(1)

MeDiAn <- median
boot_ci_fun(x, MeDiAn, label = "m")
#>    ci_lower         m  ci_upper
#> 1 0.4896462 0.4964676 0.5026135

#     ci_lower    median ci_upper
# 1 0.4900485 0.4964676 0.502184
set.seed(1)
x <- rnorm(30)
y <- x - rnorm(30) + runif(30,-2,2)
plot(x,y)


set.seed(1)
boot_ci_corr(x, y)
#>    ci_lower corr_coef ci_upper
#> 1 0.4267658 0.6258065 0.860535

#    ci_lower   corr_coef  ci_upper
#   -0.1051065  0.243604   0.6067977

# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~


df <- data.frame(x, y)
set.seed(1)
boot_ci_corr(df)
#>    ci_lower corr_coef ci_upper
#> 1 0.4267658 0.6258065 0.860535