confintr

NEWS code{white-space: pre-wrap;} span.smallcaps{font-variant: small-caps;} span.underline{text-decoration: underline;} div.column{display: inline-block; vertical-align: top; width: 50%;} div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} ul.task-list{list-style: none;} confintr 1.0.2 Maintenance Fix Latex problem in MacOS help files. Slight corrections in the documentation. confintr 1.0.1 Maintenance Less redundancies in help files Using Latex formulas in help files confintr 1.0.0 This is a large maintenance update, bumping the package to stable version 1.0.0. User visible changes Replaced the term “symmetric” by the better “equal-tailed”. Similarly, we now output “unequal-tailed” instead of “asymmetric”. By “equal-tailed”, we mean that the upper and lower error probabilies agree, not that the interval is symmetric around the estimate. This has no impact on the resulting numbers, only on the text (if you ever used unequal-tailed intervals). Maintenance Reorganisation of code files More compact help files Greatly improved unit tests Modern code formatting style Using package::function() notation instead of importFrom package function Introduction of Github actions New Gitpage confintr 0.2.0 Bug fix Fixes a mistake in the calculation of studentized bootstrap CIs, impacting ci_mean() , ci_mean_diff() , ci_var() , ci_sd() , and ci_proportion() when used together with the options type = "bootstrap" and boot_type = "stud" . The studentized bootstrap is the default boot_type for ci_mean() and ci_mean_diff() . The mistake happened in calculating the pivotal quantity, not in the statistic itself. Thus, the affected confidence intervals will usually only be slightly off. Explanation “confintr” uses the “boot” package as backend for calculating bootstrap confidence intervals. To calculate studentized confidence bootstrap intervals, boot() requires a function that provides two values: the statistic of interest and its variance . The “confintr” package passed the standard deviation instead of the variance. confintr 0.1.2 This is a maintenance release only, getting rid of the CRAN note on LazyData, updating to testthat v3, and using a more elegant way to generate/update the package. confintr 0.1.1 Added confidence intervals for the odds ratio via stats::fisher.test. Fixed wrong VignetteIndexEntry. confintr 0.1.0 This is the initial CRAN release.

README code{white-space: pre-wrap;} span.smallcaps{font-variant: small-caps;} span.underline{text-decoration: underline;} div.column{display: inline-block; vertical-align: top; width: 50%;} div.hanging-indent{margin-left: 1.5em; text-indent: -1.5em;} ul.task-list{list-style: none;} pre > code.sourceCode { white-space: pre; position: relative; } pre > code.sourceCode > span { display: inline-block; line-height: 1.25; } pre > code.sourceCode > span:empty { height: 1.2em; } .sourceCode { overflow: visible; } code.sourceCode > span { color: inherit; text-decoration: inherit; } div.sourceCode { margin: 1em 0; } pre.sourceCode { margin: 0; } @media screen { div.sourceCode { overflow: auto; } } @media print { pre > code.sourceCode { white-space: pre-wrap; } pre > code.sourceCode > span { text-indent: -5em; padding-left: 5em; } } pre.numberSource code { counter-reset: source-line 0; } pre.numberSource code > span { position: relative; left: -4em; counter-increment: source-line; } pre.numberSource code > span > a:first-child::before { content: counter(source-line); position: relative; left: -1em; text-align: right; vertical-align: baseline; border: none; display: inline-block; -webkit-touch-callout: none; -webkit-user-select: none; -khtml-user-select: none; -moz-user-select: none; -ms-user-select: none; user-select: none; padding: 0 4px; width: 4em; color: #aaaaaa; } pre.numberSource { margin-left: 3em; border-left: 1px solid #aaaaaa; padding-left: 4px; } div.sourceCode { } @media screen { pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; } } code span.al { color: #ff0000; font-weight: bold; } /* Alert */ code span.an { color: #60a0b0; font-weight: bold; font-style: italic; } /* Annotation */ code span.at { color: #7d9029; } /* Attribute */ code span.bn { color: #40a070; } /* BaseN */ code span.bu { color: #008000; } /* BuiltIn */ code span.cf { color: #007020; font-weight: bold; } /* ControlFlow */ code span.ch { color: #4070a0; } /* Char */ code span.cn { color: #880000; } /* Constant */ code span.co { color: #60a0b0; font-style: italic; } /* Comment */ code span.cv { color: #60a0b0; font-weight: bold; font-style: italic; } /* CommentVar */ code span.do { color: #ba2121; font-style: italic; } /* Documentation */ code span.dt { color: #902000; } /* DataType */ code span.dv { color: #40a070; } /* DecVal */ code span.er { color: #ff0000; font-weight: bold; } /* Error */ code span.ex { } /* Extension */ code span.fl { color: #40a070; } /* Float */ code span.fu { color: #06287e; } /* Function */ code span.im { color: #008000; font-weight: bold; } /* Import */ code span.in { color: #60a0b0; font-weight: bold; font-style: italic; } /* Information */ code span.kw { color: #007020; font-weight: bold; } /* Keyword */ code span.op { color: #666666; } /* Operator */ code span.ot { color: #007020; } /* Other */ code span.pp { color: #bc7a00; } /* Preprocessor */ code span.sc { color: #4070a0; } /* SpecialChar */ code span.ss { color: #bb6688; } /* SpecialString */ code span.st { color: #4070a0; } /* String */ code span.va { color: #19177c; } /* Variable */ code span.vs { color: #4070a0; } /* VerbatimString */ code span.wa { color: #60a0b0; font-weight: bold; font-style: italic; } /* Warning */ {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 )

Help for package confintr const macros = { "\\R": "\\textsf{R}", "\\mbox": "\\text", "\\code": "\\texttt"}; function processMathHTML() { var l = document.getElementsByClassName('reqn'); for (let e of l) { katex.render(e.textContent, e, { throwOnError: false, macros }); } return; } Package {confintr} Contents ci_IQR ci_chisq_ncp ci_cor ci_cramersv ci_f_ncp ci_kurtosis ci_mad ci_mean ci_mean_diff ci_median ci_median_diff ci_oddsratio ci_proportion ci_quantile ci_quantile_diff ci_rsquared ci_sd ci_skewness ci_var cramersv is.cint kurtosis moment oddsratio print.cint se skewness Title: Confidence Intervals Version: 1.0.2 Description: Calculates classic and/or bootstrap confidence intervals for many parameters such as the population mean, variance, interquartile range (IQR), median absolute deviation (MAD), skewness, kurtosis, Cramer's V, odds ratio, R-squared, quantiles (incl. median), proportions, different types of correlation measures, difference in means, quantiles and medians. Many of the classic confidence intervals are described in Smithson, M. (2003, ISBN: 978-0761924999). Bootstrap confidence intervals are calculated with the R package 'boot'. Both one- and two-sided intervals are supported. License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] Depends: R (≥ 3.1.0) Encoding: UTF-8 RoxygenNote: 7.2.3 Imports: boot, stats Suggests: knitr, rmarkdown, testthat (≥ 3.0.0) VignetteBuilder: knitr Config/testthat/edition: 3 URL: https://github.com/mayer79/confintr BugReports: https://github.com/mayer79/confintr/issues NeedsCompilation: no Packaged: 2023-06-04 17:59:31 UTC; Michael Author: Michael Mayer [aut, cre] Maintainer: Michael Mayer <mayermichael79@gmail.com> Repository: CRAN Date/Publication: 2023-06-04 18:40:02 UTC CI for the IQR Description This function calculates bootstrap CIs (by default "bca") for the population interquartile range (IQR), i.e., the difference between first and third quartile. Usage ci_IQR( x, probs = c(0.025, 0.975), type = "bootstrap", boot_type = c("bca", "perc", "norm", "basic"), R = 9999L, seed = NULL, ... ) Arguments x A numeric vector. probs Lower and upper probabilities, by default c(0.025, 0.975) . type Type of CI. Currently not used as the only type is "bootstrap" . boot_type Type of bootstrap CI c("bca", "perc", "norm", "basic"). R The number of bootstrap resamples. Only used for type = "bootstrap" . seed An integer random seed. Only used for type = "bootstrap" . ... Further arguments passed to boot::boot() . Value An object of class "cint", see ci_mean() for details. Examples x <- rnorm(100) ci_IQR(x, R = 999) # Use larger R CI for the NCP of the Chi-Squared Distribution Description This function calculates CIs for the non-centrality parameter (NCP) of the \chi^2 -distribution. A positive lower (1 - \alpha) \cdot 100\% -confidence limit for the NCP goes hand-in-hand with a significant association test at level \alpha . Usage ci_chisq_ncp( x, probs = c(0.025, 0.975), correct = TRUE, type = c("chi-squared", "bootstrap"), boot_type = c("bca", "perc", "norm", "basic"), R = 9999L, seed = NULL, ... ) Arguments x The result of stats::chisq.test() , a matrix/table of counts, or a data.frame with exactly two columns representing the two variables. probs Lower and upper probabilities, by default c(0.025, 0.975) . correct Should Yates continuity correction be applied to the 2x2 case? The default is TRUE (also used in the bootstrap), which differs from ci_cramersv() . type Type of CI. One of "chi-squared" (default) or "bootstrap". boot_type Type of bootstrap CI. Only used for type = "bootstrap" . R The number of bootstrap resamples. Only used for type = "bootstrap" . seed An integer random seed. Only used for type = "bootstrap" . ... Further arguments passed to boot::boot() . Details By default, CIs are computed by Chi-squared test inversion. This can be unreliable for very large test statistics. The default bootstrap type is "bca". Value An object of class "cint", see ci_mean() for details. References Smithson, M. (2003). Confidence intervals. Series: Quantitative Applications in the Social Sciences. New York, NY: Sage Publications. See Also ci_cramersv() Examples ci_chisq_ncp(mtcars[c("am", "vs")]) ci_chisq_ncp(mtcars[c("am", "vs")], type = "bootstrap", R = 999) # Use larger R CI for Correlation Coefficients Description This function calculates CIs for a population correlation coefficient. For Pearson correlation, "normal" CIs are available (by stats::cor.test() ). Also bootstrap CIs are supported (by default "bca", and the only option for rank correlations). Usage ci_cor( x, y = NULL, probs = c(0.025, 0.975), method = c("pearson", "kendall", "spearman"), type = c("normal", "bootstrap"), boot_type = c("bca", "perc", "norm", "basic"), R = 9999L, seed = NULL, ... ) Arguments x A numeric vector or a matrix / data.frame with exactly two numeric columns. y A numeric vector (only used if x is a vector). probs Lower and upper probabilities, by default c(0.025, 0.975) . method Type of correlation coefficient, one of "pearson" (default), "kendall", or "spearman". For the latter two, only bootstrap CIs are supported. type Type of CI. One of "normal" (the default) or "bootstrap" (the only option for rank-correlations). boot_type Type of bootstrap CI. Only used for type = "bootstrap" . R The number of bootstrap resamples. Only used for type = "bootstrap" . seed An integer random seed. Only used for type = "bootstrap" . ... Further arguments passed to boot::boot() . Value An object of class "cint", see ci_mean() for details. Examples ci_cor(iris[1:2]) ci_cor(iris[1:2], type = "bootstrap", R = 999) # Use larger R ci_cor(iris[1:2], method = "spearman", type = "bootstrap", R = 999) # Use larger R CI for the Population Cramer's V Description This function calculates CIs for the population Cramer's V. By default, a parametric approach based on the non-centrality parameter (NCP) of the chi-squared distribution is utilized. Alternatively, bootstrap CIs are available (default "bca"), also by boostrapping CIs for the NCP and then mapping the result back to Cramer's V. Usage ci_cramersv( x, probs = c(0.025, 0.975), type = c("chi-squared", "bootstrap"), boot_type = c("bca", "perc", "norm", "basic"), R = 9999L, seed = NULL, test_adjustment = TRUE, ... ) Arguments x The result of stats::chisq.test() , a matrix/table of counts, or a data.frame with exactly two columns representing the two variables. probs Lower and upper probabilities, by default c(0.025, 0.975) . type Type of CI. One of "chi-squared" (default) or "bootstrap". boot_type Type of bootstrap CI. Only used for type = "bootstrap" . R The number of bootstrap resamples. Only used for type = "bootstrap" . seed An integer random seed. Only used for type = "bootstrap" . test_adjustment Adjustment to allow for test of association, see Details. The default is TRUE . ... Further arguments passed to boot::boot() . Details A positive lower (1 - \alpha) \cdot 100\% -confidence limit for the NCP goes hand-in-hand with a significant association test at level \alpha . In order to allow such test approach also with Cramer's V, if the lower bound for the NCP is 0, we round down to 0 the lower bound for Cramer's V as well. Without this slightly conservative adjustment, the lower limit for V would always be positive since the CI for V is found by \sqrt{(\textrm{CI for NCP} + \textrm{df})/(n \cdot (k - 1))} , where k is the smaller number of levels in the two variables (see Smithson, p.40). Use test_adjustment = FALSE to switch off this behaviour. Note that this is also a reason to bootstrap V via NCP instead of directly bootstrapping V. Further note that no continuity correction is applied for 2x2 tables, and that large chi-squared test statistics might provide unreliable results with method "chi-squared", see stats::pchisq() . Value An object of class "cint", see ci_mean() for details. References Smithson, M. (2003). Confidence intervals. Series: Quantitative Applications in the Social Sciences. New Yo