| Type: | Package |
| Title: | Tidyverse-Compatible Bootstrapping |
| Version: | 0.1.1 |
| Description: | Compute arbitrary non-parametric bootstrap statistics on data in tidy data frames. |
| Depends: | R (≥ 3.4.0) |
| License: | GPL-3 |
| URL: | https://github.com/langcog/tidyboot |
| BugReports: | http://github.com/langcog/tidyboot/issues |
| Encoding: | UTF-8 |
| LazyData: | true |
| RoxygenNote: | 6.0.1 |
| Imports: | dplyr (≥ 0.7.4), modelr (≥ 0.1.1), purrr (≥ 0.2.4), rlang (≥ 0.1.6), tidyr (≥ 0.7.2) |
| NeedsCompilation: | no |
| Packaged: | 2018-03-14 00:52:14 UTC; mikabr |
| Author: | Mika Braginsky [aut, cre], Daniel Yurovsky [aut] |
| Maintainer: | Mika Braginsky <mika.br@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2018-03-14 04:13:49 UTC |
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 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 |
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 |
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
|
nboot |
The number of bootstrap samples to take (defaults to
|
... |
Other arguments passed from generic. |
Examples
## Mean and 95% confidence interval for 500 samples from two different normal distributions
require(dplyr)
gauss1 <- data_frame(value = rnorm(500, mean = 0, sd = 1), condition = 1)
gauss2 <- data_frame(value = rnorm(500, mean = 2, sd = 3), condition = 2)
df <- bind_rows(gauss1, gauss2)
df %>% group_by(condition) %>%
tidyboot(summary_function = function(x) x %>% summarise(mean = mean(value)),
statistics_functions = function(x) x %>%
summarise_at(vars(mean), funs(ci_upper, mean, ci_lower)))
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 |
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
|
size |
The fraction of items to sample (defaults to 1). |
replace |
Logical indicating whether to sample with replacement
(defaults to |
... |
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 |
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
|
size |
The fraction of items to sample (defaults to 1). |
replace |
Logical indicating whether to sample with replacement
(defaults to |
... |
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))
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 |
nboot |
The number of bootstrap samples to take (defaults to
|
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 <- data_frame(value = rnorm(500, mean = 0, sd = 1), condition = 1)
gauss2 <- data_frame(value = rnorm(500, mean = 2, sd = 3), condition = 2)
df <- bind_rows(gauss1, gauss2)
df %>%
group_by(condition) %>%
tidyboot_mean(column = value)