{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 

# From CRAN
install.packages("confintr")

# Development version
devtools::install_github("mayer79/confintr")

Usage 

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)