The confintr package offers classic and/or bootstrap confidence intervals 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,
the odds ratio of a 2x2 table,
Pearson-, Spearman-, Kendall correlation coefficients,
mean, quantile and median differences.
Both one- and two-sided intervals are supported.
Different types of bootstrap intervals are possible through argument boot_type, see vignette.
From CRAN:
Latest version from github:
library(confintr)
# 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%
# 44.89255 56.31045
# 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%
# 2.757986 3.368160
# 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 = "boot", 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%
# -9.020367 7.040650
# 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", R = 999)
# Median difference
ci_median_diff(10:30, 1:15, R = 999)