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https://github.com/mayer79/confintr
R package for calculation of standard and bootstrap confidence intervals
https://github.com/mayer79/confintr
bootstrap confidence-intervals r r-package rstats statistical-inference statistics
Last synced: 9 days ago
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R package for calculation of standard and bootstrap confidence intervals
- Host: GitHub
- URL: https://github.com/mayer79/confintr
- Owner: mayer79
- License: gpl-2.0
- Created: 2020-05-16T12:51:20.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2024-07-24T06:12:01.000Z (4 months ago)
- Last Synced: 2024-10-04T12:56:42.601Z (about 1 month ago)
- Topics: bootstrap, confidence-intervals, r, r-package, rstats, statistical-inference, statistics
- Language: R
- Homepage: https://mayer79.github.io/confintr/
- Size: 868 KB
- Stars: 13
- Watchers: 3
- Forks: 2
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- Changelog: NEWS.md
- License: LICENSE.md
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- jimsghstars - mayer79/confintr - R package for calculation of standard and bootstrap confidence intervals (R)
README
# {confintr} 
[](https://github.com/mayer79/confintr/actions/actions/workflows/R-CMD-check.yaml)
[](https://cran.r-project.org/package=confintr)
[](https://app.codecov.io/gh/mayer79/confintr?branch=main)
[](https://cran.r-project.org/package=confintr)
[](https://cran.r-project.org/package=confintr)
## Overview
{confintr} offers classic and/or bootstrap confidence intervals (CI) for the following parameters:
- mean,
- quantiles incl. median,
- proportion,
- variance and standard deviation,
- IQR and MAD,
- skewness and kurtosis,
- R-squared and the non-centrality parameter of the F distribution,
- Cramér's V and the non-centrality parameter of the chi-squared distribution,
- odds ratio of a 2x2 table,
- Pearson-, Spearman-, Kendall correlation coefficients,
- mean differences, quantile and median differences.
Both one- and two-sided intervals are supported.
Different types of bootstrap intervals are available via {boot}, see vignette.
## Installation
```r
# From CRAN
install.packages("confintr")
# Development version
devtools::install_github("mayer79/confintr")
```
## Usage
``` r
library(confintr)
set.seed(1)
# Mean
ci_mean(1:100)
# Two-sided 95% t confidence interval for the population mean
#
# Sample estimate: 50.5
# Confidence interval:
# 2.5% 97.5%
# 44.74349 56.25651
# Mean using the Bootstrap
ci_mean(1:100, type = "bootstrap")
# Two-sided 95% bootstrap confidence interval for the population mean
# based on 9999 bootstrap replications and the student method
#
# Sample estimate: 50.5
# Confidence interval:
# 2.5% 97.5%
# 44.72913 56.34685
# 95% value at risk
ci_quantile(rexp(1000), q = 0.95)
# Two-sided 95% binomial confidence interval for the population 95%
# quantile
#
# Sample estimate: 2.954119
# Confidence interval:
# 2.5% 97.5%
# 2.745526 3.499928
# Mean difference
ci_mean_diff(1:100, 2:101)
# Two-sided 95% t confidence interval for the population value of mean(x)-mean(y)
#
# Sample estimate: -1
# Confidence interval:
# 2.5% 97.5%
# -9.090881 7.090881
ci_mean_diff(1:100, 2:101, type = "bootstrap", seed = 1)
# Two-sided 95% bootstrap confidence interval for the population value of mean(x)-mean(y)
# based on 9999 bootstrap replications and the student method
#
# Sample estimate: -1
# Confidence interval:
# 2.5% 97.5%
# -9.057506 7.092050
# Further examples (without output)
# Correlation
ci_cor(iris[1:2], method = "spearman", type = "bootstrap")
# Proportions
ci_proportion(10, n = 100, type = "Wilson")
ci_proportion(10, n = 100, type = "Clopper-Pearson")
# R-squared
fit <- lm(Sepal.Length ~ ., data = iris)
ci_rsquared(fit, probs = c(0.05, 1))
# Kurtosis
ci_kurtosis(1:100)
# Mean difference
ci_mean_diff(10:30, 1:15)
ci_mean_diff(10:30, 1:15, type = "bootstrap")
# Median difference
ci_median_diff(10:30, 1:15)
```