Package 'tidyboot'

Confidence interval (lower 2.5%)

Description

Confidence interval (lower 2.5%)

Usage

ci_lower(x, na.rm = FALSE)

Arguments

x	A numeric vector
na.rm	A logical value indicating whether NA values should be stripped before the computation proceeds.

Value

2.5

Examples

x <- rnorm(1000, mean = 0, sd = 1)
ci_lower(x)

---

Confidence interval (upper 97.5%)

Description

Confidence interval (upper 97.5%)

Usage

ci_upper(x, na.rm = FALSE)

Arguments

x	A numeric vector
na.rm	A logical value indicating whether NA values should be stripped before the computation proceeds.

Value

97.5

Examples

x <- rnorm(1000, mean = 0, sd = 1)
ci_upper(x)

---

Non-parametric bootstrap with multiple sample statistics

Description

tidyboot is a generic function for bootstrapping on various data structures. The function invokes particular methods which depend on the class of the first argument.

Usage

tidyboot(data, ...)

Arguments

data	A data structure containing the data to bootstrap.
...	Additional arguments passed to particular methods.

Examples

## List of available methods
methods(tidyboot)

---

Non-parametric bootstrap and empirical central tendency for data frames Designed to make standard use of tidyboot.data.frame easier

Description

Computes arbitrary bootstrap statistics on univariate data. NOTE: Both empirical functions and bootstrapping functions will be computed over the grouping variables currently specified for the data frame.

Usage

tidyboot_mean(data, column, nboot = 1000, na.rm = FALSE)

Arguments

data	A data frame.
column	A column of data to bootstrap over.
nboot	The number of bootstrap samples to take (defaults to 1000).
na.rm	A logical value indicating whether NA values should be stripped before the computation proceeds.

Examples

## Mean and 95% confidence interval for 500 samples from two different normal distributions
require(dplyr)
gauss1 <- tibble(value = rnorm(500, mean = 0, sd = 1), condition = 1)
gauss2 <- tibble(value = rnorm(500, mean = 2, sd = 3), condition = 2)
df <- bind_rows(gauss1, gauss2)

df %>%
 group_by(condition) %>%
 tidyboot_mean(column = value)

---

Non-parametric bootstrap for data frames

Description

Computes arbitrary bootstrap statistics on univariate data.

Usage

## S3 method for class 'data.frame'
tidyboot(
  data,
  column = NULL,
  summary_function = mean,
  statistics_functions,
  nboot = 1000,
  ...
)

Arguments

data	A data frame.
column	A column of data to bootstrap over (if not supplied, summary_function and statistic_function must operate over the appropriate data frame).
summary_function	A function to be computed over each set of samples as a data frame, or a function to be computed over each set of samples as a single column of a data frame indicated by column (defaults to mean).
statistics_functions	A function to be computed over each set of samples as a data frame, or a named list of functions to be computed over each set of samples as a single column of a data frame indicated by column.
nboot	The number of bootstrap samples to take (defaults to 1000).
...	Other arguments passed from generic.

Examples

## Mean and 95% confidence interval for 500 samples from two different normal distributions
require(dplyr)
gauss1 <- tibble(value = rnorm(500, mean = 0, sd = 1), condition = 1)
gauss2 <- tibble(value = rnorm(500, mean = 2, sd = 3), condition = 2)
df <- bind_rows(gauss1, gauss2)

mean_ci_funs <- list("ci_lower" = ci_lower, "mean" = mean, "ci_upper" = ci_upper)
df %>% group_by(condition) %>%
  tidyboot(column = value, summary_function = mean, statistics_functions = mean_ci_funs)

df %>% group_by(condition) %>%
  tidyboot(summary_function = function(x) x %>% summarise(stat = mean(value)),
           statistics_functions = function(x) x %>%
             summarise(across(stat, mean_ci_funs, .names = "{.fn}")))

---

Non-parametric bootstrap for logical vector data

Description

Computes arbitrary bootstrap statistics on univariate data.

Usage

## S3 method for class 'logical'
tidyboot(
  data,
  summary_function = mean,
  statistics_functions,
  nboot = 1000,
  size = 1,
  replace = TRUE,
  ...
)

Arguments

data	A logical vector of data to bootstrap over.
summary_function	A function to be computed over each set of samples. This function needs to take a vector and return a single number (defaults to mean).
statistics_functions	A named list of functions to be computed over the set of summary values from all samples.
nboot	The number of bootstrap samples to take (defaults to 1000).
size	The fraction of items to sample (defaults to 1).
replace	Logical indicating whether to sample with replacement (defaults to TRUE).
...	Other arguments passed from generic.

Examples

## Mean and 95% confidence interval for 500 samples from a binomial distribution
x <- as.logical(rbinom(500, 1, 0.5))
tidyboot(x, statistics_functions = c(ci_lower, mean, ci_upper))

---

Non-parametric bootstrap for numeric vector data

Description

Computes arbitrary bootstrap statistics on univariate data.

Usage

## S3 method for class 'numeric'
tidyboot(
  data,
  summary_function = mean,
  statistics_functions,
  nboot = 1000,
  size = 1,
  replace = TRUE,
  ...
)

Arguments

data	A numeric vector of data to bootstrap over.
summary_function	A function to be computed over each set of samples. This function needs to take a vector and return a single number (defaults to mean).
statistics_functions	A named list of functions to be computed over the set of summary values from all samples.
nboot	The number of bootstrap samples to take (defaults to 1000).
size	The fraction of items to sample (defaults to 1).
replace	Logical indicating whether to sample with replacement (defaults to TRUE).
...	Other arguments passed from generic.

Examples

## Mean and 95% confidence interval for 500 samples from a normal distribution
x <- rnorm(500, mean = 0, sd = 1)
tidyboot(x, statistics_functions = list("ci_lower" = ci_lower,
                                        "mean" = mean,
                                        "ci_upper" = ci_upper))

---