diff --git a/DESCRIPTION b/DESCRIPTION index 79bae46..93ec860 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,6 +1,6 @@ Package: ICIKendallTau Title: Calculates information-content-informed Kendall-tau -Version: 0.3.21 +Version: 0.3.22 Date: 2024-01-26 Authors@R: c(person(given = c("Robert", "M"), family = "Flight", role = c("aut", "cre"), email = "rflight79@gmail.com", comment = diff --git a/docs/404.html b/docs/404.html index 66c9ac1..397fa0b 100644 --- a/docs/404.html +++ b/docs/404.html @@ -24,7 +24,7 @@ ICIKendallTau - 0.2.10 + 0.3.21 + + + + + +
+
+
+

convert matrix to data.frame

+ Source: R/reshaping.R +
cor_matrix_2_long_df.Rd
+
+ +
+

Given a square correlation matrix, converts it to a long data.frame, with three columns.

+
+ +
+

Usage

+ 
cor_matrix_2_long_df(in_matrix)
+
+ +
+

Arguments

+
cor_matrix
+

the correlation matrix

+ +
+
+

Value

+ + +

data.frame

+
+
+

Details

+

The data.frame contains three columns:

  • s1: the first entry of comparison

    • +

  • s2: the second entry of comparison

    • +

  • cor: the correlation value

    • +
+ +
+ + +
+ + + + + + + diff --git a/docs/reference/disable_logging.html b/docs/reference/disable_logging.html new file mode 100644 index 0000000..f3591c7 --- /dev/null +++ b/docs/reference/disable_logging.html @@ -0,0 +1,85 @@ + +turn logging off — disable_logging • ICIKendallTau + Skip to contents + + +
+
+
+

turn logging off

+ Source: R/logging.R +
disable_logging.Rd
+
+ +
+

There may be good reasons to turn the logging off after it's been turned on. This +basically tells the package that the logger isn't available.

+
+ +
+

Usage

+ 
disable_logging()
+
+ + +
+ + +
+ + + + + + + diff --git a/docs/reference/enable_logging.html b/docs/reference/enable_logging.html new file mode 100644 index 0000000..7be6849 --- /dev/null +++ b/docs/reference/enable_logging.html @@ -0,0 +1,103 @@ + +turn logging on — enable_logging • ICIKendallTau + Skip to contents + + +
+
+
+

turn logging on

+ Source: R/logging.R +
enable_logging.Rd
+
+ +
+

Choose to enable logging, to a specific file if desired.

+
+ +
+

Usage

+ 
enable_logging(log_file = NULL, memory = FALSE)
+
+ +
+

Arguments

+
log_file
+

the file to log to

+ + +
memory
+

provide memory logging too? Only available on Linux and MacOS

+ +
+
+

Details

+

Uses the logger package under the hood, which is suggested in the dependencies. +Having logging enabled is nice to see when things are starting and stopping, and what exactly +has been done, without needing to write messages to the console. It is especially +useful if you are getting errors, but can't really see them, then you can add +"memory" logging to see if you are running out of memory.

+

Default log file has the pattern:

+

YYYY.MM.DD.HH.MM.SS_ICIKendallTau_run.log

+
+ +
+ + +
+ + + + + + + diff --git a/docs/reference/ici_kendalltau.html b/docs/reference/ici_kendalltau.html index 948ec76..245dd41 100644 --- a/docs/reference/ici_kendalltau.html +++ b/docs/reference/ici_kendalltau.html @@ -12,7 +12,7 @@ ICIKendallTau - 0.2.10 + 0.3.21 + + + + + +
+
+
+

log memory usage

+ Source: R/logging.R +
log_memory.Rd
+
+ +
+

Logs the amount of memory being used to a log file if it is available, and +generating warnings if the amount of RAM hits zero.

+
+ +
+

Usage

+ 
log_memory()
+
+ + +
+ + +
+ + + + + + + diff --git a/docs/reference/log_message.html b/docs/reference/log_message.html new file mode 100644 index 0000000..c786af8 --- /dev/null +++ b/docs/reference/log_message.html @@ -0,0 +1,89 @@ + +log messages — log_message • ICIKendallTau + Skip to contents + + +
+
+
+

log messages

+ Source: R/logging.R +
log_message.Rd
+
+ +
+

If a log_appender is available, logs the given message at the info level.

+
+ +
+

Usage

+ 
log_message(message_string)
+
+ +
+

Arguments

+
message_string
+

the string to put in the message

+ +
+ +
+ + +
+ + + + + + + diff --git a/docs/reference/long_df_2_cor_matrix.html b/docs/reference/long_df_2_cor_matrix.html new file mode 100644 index 0000000..230dcab --- /dev/null +++ b/docs/reference/long_df_2_cor_matrix.html @@ -0,0 +1,95 @@ + +convert data.frame to matrix — long_df_2_cor_matrix • ICIKendallTau + Skip to contents + + +
+
+
+

convert data.frame to matrix

+ Source: R/reshaping.R +
long_df_2_cor_matrix.Rd
+
+ +
+

Given a long data.frame, converts it to a possibly square correlation matrix

+
+ +
+

Usage

+ 
long_df_2_cor_matrix(long_df, is_square = TRUE)
+
+ +
+

Arguments

+
long_df
+

the long data.frame

+ +
+
+

Value

+ + +

matrix

+
+ +
+ + +
+ + + + + + + diff --git a/docs/reference/pairwise_completeness.html b/docs/reference/pairwise_completeness.html index 95cb733..b630704 100644 --- a/docs/reference/pairwise_completeness.html +++ b/docs/reference/pairwise_completeness.html @@ -12,7 +12,7 @@ ICIKendallTau - 0.2.10 + 0.3.21 + + + + + +
+
+
+

turn progress on off

+ Source: R/logging.R +
show_progress.Rd
+
+ +
+

Allow the user to turn progress messages to the console and off. Default +is to provide messages to the console.

+
+ +
+

Usage

+ 
show_progress(progress = TRUE)
+
+ +
+

Arguments

+
progress
+

logical to have it on or off

+ +
+ +
+ + +
+ + + + + + + diff --git a/docs/search.json b/docs/search.json index c94db5d..48b57b6 100644 --- a/docs/search.json +++ b/docs/search.json @@ -1 +1 @@ -[{"path":[]},{"path":"/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement rflight79@gmail.com. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. Correction","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Use inappropriate language behavior deemed unprofessional unwelcome community. Consequence: private, written warning community leaders, providing clarity around nature violation explanation behavior inappropriate. public apology may requested.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"id_2-warning","dir":"","previous_headings":"Enforcement Guidelines","what":"2. Warning","title":"Contributor Covenant Code of Conduct","text":"Community Impact: violation single incident series actions. Consequence: warning consequences continued behavior. interaction people involved, including unsolicited interaction enforcing Code Conduct, specified period time. includes avoiding interactions community spaces well external channels like social media. Violating terms may lead temporary permanent ban.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"id_3-temporary-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"3. Temporary Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: serious violation community standards, including sustained inappropriate behavior. Consequence: temporary ban sort interaction public communication community specified period time. public private interaction people involved, including unsolicited interaction enforcing Code Conduct, allowed period. Violating terms may lead permanent ban.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"id_4-permanent-ban","dir":"","previous_headings":"Enforcement Guidelines","what":"4. Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.0, available https://www.contributor-covenant.org/version/2/0/ code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. Translations available https:// www.contributor-covenant.org/translations.","code":""},{"path":"/LICENSE.html","id":null,"dir":"","previous_headings":"","what":"MIT License","title":"MIT License","text":"Copyright (c) 2021 Robert M Flight & Hunter NB Moseley Permission hereby granted, free charge, person obtaining copy software associated documentation files (“Software”), deal Software without restriction, including without limitation rights use, copy, modify, merge, publish, distribute, sublicense, /sell copies Software, permit persons Software furnished , subject following conditions: copyright notice permission notice shall included copies substantial portions Software. SOFTWARE PROVIDED “”, WITHOUT WARRANTY KIND, EXPRESS IMPLIED, INCLUDING LIMITED WARRANTIES MERCHANTABILITY, FITNESS PARTICULAR PURPOSE NONINFRINGEMENT. EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"/articles/ici-kendalltau.html","id":"problem","dir":"Articles","previous_headings":"","what":"Problem","title":"Information-Content-Informed Kendall Tau Correlation","text":"handle missing data (.e. NA’s) calculating correlation two variables. However, whether observation made semi-quantitative information many analytical measurements sensitivity limits. .e. many cases, missing observations “missing--random”, “missing---random” due falling detection limit. cases, NA informative.","code":""},{"path":"/articles/ici-kendalltau.html","id":"approach","dir":"Articles","previous_headings":"","what":"Approach","title":"Information-Content-Informed Kendall Tau Correlation","text":"Kendall Tau Correlation coefficient calculates correlation based number concordant discordant pairs: \\(\\tau = \\frac{ | pairs_{concordant} | - | pairs_{discordant} |}{\\binom{n}{2}}\\) pair two x-y data points. \\(x_i > x_j\\) \\(y_i > y_j\\) \\(x_i < x_j\\) \\(y_i < y_j\\) \\(x_i > x_j\\) \\(y_i < y_j\\) \\(x_i < x_j\\) \\(y_i > y_j\\) definitions can expanded handle missing observations: \\(x_i > x_j\\) \\(y_i > y_j\\) \\(x_i < x_j\\) \\(y_i < y_j\\) \\(x_i > x_j\\) \\(y_i \\& !y_j\\) \\(x_i < x_j\\) \\(!y_i \\& y_j\\) \\(x_i \\& !x_j\\) \\(y_i > y_j\\) \\(!x_i \\& x_j\\) \\(y_i < y_j\\) \\(x_i \\& !x_j\\) \\(y_i \\& !y_j\\) (used local perspective version) \\(x_i \\& x_j\\) \\(!y_i \\& y_j\\) (used local perspective version) \\(x_i > x_j\\) \\(y_i < y_j\\) \\(x_i < x_j\\) \\(y_i > y_j\\) \\(x_i > x_j\\) \\(!y_i \\& y_j\\) \\(x_i < x_j\\) \\(y_i \\& !y_j\\) \\(x_i \\& !x_j\\) \\(y_i < y_j\\) \\(!x_i \\& x_j\\) \\(y_i > y_j\\) \\(x_i \\& !x_j\\) \\(!y_i \\& y_j\\) (used local perspective version) \\(!x_i \\& x_j\\) \\(y_i \\& !y_j\\) (used local perspective version) Also data points missing x y values naturally reduce strength correlation value, since can neither concordant discordant another (NA, NA) pair, impact denominator. equivalent removing last two concordant discordant pair tests.","code":""},{"path":"/articles/ici-kendalltau.html","id":"handling-tied-values","dir":"Articles","previous_headings":"","what":"Handling Tied Values","title":"Information-Content-Informed Kendall Tau Correlation","text":"base Kendall tau correlation must adjusted handle tied values, ie. tau-b version equation. \\[\\tau = \\frac{ | pairs_{concordant} | - | pairs_{discordant} |}{\\sqrt{ ( n_0 - n_{xtie} ) ( n_0 - n_{ytie} )}} \\] : \\(n_0 = \\frac{n \\left ( n - 1 \\right )}{2}\\) \\(n_{xtie} = \\sum_{}^{} \\frac{t_{xi} \\left ( t_{xi} - 1 \\right )}{2}\\) \\(n_{ytie} = \\sum_{}^{} \\frac{t_{yi} \\left ( t_{yi} - 1 \\right )}{2}\\) \\(t_{xi}\\) - size ith group tied x values. \\(t_{yi}\\) - size ith group tied y values. local perspective, number NAs x y can treated group tied values calculation \\(n_{xtie}\\) \\(n_{ytie}\\), respectively.","code":""},{"path":"/articles/ici-kendalltau.html","id":"scaling-by-the-correlation-with-the-highest-information-content","dir":"Articles","previous_headings":"","what":"Scaling by the correlation with the highest information content","title":"Information-Content-Informed Kendall Tau Correlation","text":"generating correlation matrix (heatmap) large analytical datasets, number observations common can become quite low two variables. becomes advantageous scale pair variables highest information content. One objective scaling factor highest possible absolute correlation maximum information content observed across dataset, dividing maximum possible absolute correlation scale whole dataset appropriately. \\[maxcorr = \\frac{\\binom{n-m}{2} + n * m}{\\binom{n-m}{2} + n * m + \\binom{m}{2}}\\] : Choose two variables least number missing values across dataset. n length variables. formula based perfect correlation given number (NA,NA) pairs added.","code":""},{"path":"/articles/ici-kendalltau.html","id":"functions","dir":"Articles","previous_headings":"","what":"Functions","title":"Information-Content-Informed Kendall Tau Correlation","text":"functions implement include: option perspective control NA values influence ties. comparing multiple samples, likely want use perspective = \"global\". Rows samples, columns features. library(furrr) plan(multiprocess) Otherwise use single core.","code":""},{"path":"/articles/ici-kendalltau.html","id":"implementation","dir":"Articles","previous_headings":"","what":"Implementation","title":"Information-Content-Informed Kendall Tau Correlation","text":"turns , think really hard, truly necessary replace missing values vector value smaller minimum value one. local version, first remove common missing values vector. C++ implementation explicitly can speed, instead wrapping {stats::cor} function. also use double merge-sort algorithm, translating {scipy:: ::stats::kendalltau} function C++ using {Rcpp}.","code":""},{"path":"/articles/ici-kendalltau.html","id":"speed","dir":"Articles","previous_headings":"","what":"Speed","title":"Information-Content-Informed Kendall Tau Correlation","text":"","code":"x = rnorm(1000) y = rnorm(1000) library(microbenchmark) microbenchmark( cor(x, y, method = \"kendall\"), ici_kt(x, y, \"global\"), times = 5 ) #> Unit: microseconds #> expr min lq mean median #> cor(x, y, method = \"kendall\") 18637.617 18752.860 19886.3726 19757.098 #> ici_kt(x, y, \"global\") 373.718 384.331 447.4892 402.748 #> uq max neval #> 19792.41 22491.880 5 #> 430.23 646.419 5 all.equal(ici_kt(x, y, \"global\")[[1]], cor(x, y, method = \"kendall\")) #> [1] TRUE"},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Robert M Flight. Author, maintainer. Hunter NB Moseley. Author.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Flight RM, Moseley HN (2023). ICIKendallTau: Calculates information-content-informed Kendall-tau. R package version 0.2.10, https://moseleybioinformaticslab.github.io/ICIKendallTauhttps://github.com/moseleybioinformaticslab/ICIKendallTau.","code":"@Manual{, title = {ICIKendallTau: Calculates information-content-informed Kendall-tau}, author = {Robert M Flight and Hunter NB Moseley}, year = {2023}, note = {R package version 0.2.10}, url = {https://moseleybioinformaticslab.github.io/ICIKendallTau https://github.com/moseleybioinformaticslab/ICIKendallTau}, }"},{"path":"/index.html","id":"icikendalltau","dir":"","previous_headings":"","what":"Calculates information-content-informed Kendall-tau","title":"Calculates information-content-informed Kendall-tau","text":"can see pkgdown site .","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Calculates information-content-informed Kendall-tau","text":"can install current version ICIKendallTau via GitHub: can also install Windows Mac binaries using r-universe:","code":"remotes::install_github(\"MoseleyBioinformaticsLab/ICIKendallTau\") options(repos = c( moseleybioinformaticslab = 'https://moseleybioinformaticslab.r-universe.dev', CRAN = \"https://cloud.r-project.org\")) install.packages(\"ICIKendallTau\")"},{"path":"/index.html","id":"problem","dir":"","previous_headings":"","what":"Problem","title":"Calculates information-content-informed Kendall-tau","text":"handle missing data (.e. NA’s) calculating correlation two variables. However, whether observation present missing semi-quantitative information many analytical measurements sensitivity limits. .e. many cases, missing observations “missing--random”, “missing---random” due falling detection limit. cases, NA informative. Therefore, analytical measurements (gene expression, proteomics, metabolomics), missing measurements included, contribute correlation. want read solve problem, see package vignette.","code":""},{"path":"/index.html","id":"package-functions","dir":"","previous_headings":"","what":"Package Functions","title":"Calculates information-content-informed Kendall-tau","text":"functions implement include: option perspective control NA values influence ties. comparing samples, likely want use perspective = \"global\". Rows samples, columns features. library(furrr) plan(multiprocess) Otherwise use single core.","code":""},{"path":"/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Calculates information-content-informed Kendall-tau","text":"common case large matrix independent samples (columns) measured features samples (.e. gene expression). make artificial data show correlation changes introduce missingness. Now introduce missing values low end one. just simplest thing introduce NA values bottom set.","code":"set.seed(1234) library(ICIKendallTau) s1 = sort(rnorm(1000, mean = 100, sd = 10)) s2 = s1 + 10 matrix_1 = cbind(s1, s2) r_1 = ici_kendalltau(t(matrix_1)) r_1$cor #> s1 s2 #> s1 1 1 #> s2 1 1 s3 = s1 s3[sample(100, 50)] = NA s4 = s2 s4[sample(100, 50)] = NA matrix_2 = cbind(s3, s4) r_2 = ici_kendalltau(t(matrix_2)) r_2$cor #> s3 s4 #> s3 1.0000000 0.9944616 #> s4 0.9944616 1.0000000"},{"path":"/index.html","id":"is-it-fast","dir":"","previous_headings":"","what":"Is It Fast?","title":"Calculates information-content-informed Kendall-tau","text":"C++ code implementation (thanks {Rcpp}!) based SciPy implementation, uses two merge sorts ranks vector, looks differences . fastest method know , complexity O(nlogn). naive way computing , explicitly examines pairs, complexity n^2. implementation compared {pcaPP::cov.fk} function, use {Rcpp} inefficient copying vectors makes 3X slower one. honestly isn’t bad. case 40,000 features, average time modern CPU 14 milliseconds. course, want use calculate Kendall-tau-b without incorporating missingness, can just fine well. also provide kt_fast function, want something treats NA values similarly stats::cor.","code":"library(microbenchmark) x = rnorm(1000) y = rnorm(1000) x2 = rnorm(40000) y2 = rnorm(40000) library(microbenchmark) microbenchmark( cor(x, y, method = \"kendall\"), ici_kt(x, y, \"global\"), ici_kt(x2, y2, \"global\"), times = 5 ) #> Unit: microseconds #> expr min lq mean median #> cor(x, y, method = \"kendall\") 18558.69 18681.412 19152.0542 18779.652 #> ici_kt(x, y, \"global\") 365.38 374.834 446.7822 395.693 #> ici_kt(x2, y2, \"global\") 21398.15 21479.065 22949.1066 21877.923 #> uq max neval #> 19067.742 20672.774 5 #> 421.672 676.332 5 #> 23802.068 26188.331 5 k_tau = ici_kt(x, y, \"global\") all.equal(k_tau[[1]] ,cor(x, y, method = \"kendall\")) #> [1] TRUE k_tau_fast = kt_fast(x, y) k_tau_fast #> tau pvalue #> -0.003411411 0.871672260"},{"path":"/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Calculates information-content-informed Kendall-tau","text":"Please note ICIKendallTau project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"/reference/ici_kendalltau.html","id":null,"dir":"Reference","previous_headings":"","what":"information-content-informed kendall tau — ici_kendalltau","title":"information-content-informed kendall tau — ici_kendalltau","text":"Given data-matrix, computes information-theoretic Kendall-tau-b samples.","code":""},{"path":"/reference/ici_kendalltau.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"information-content-informed kendall tau — ici_kendalltau","text":"","code":"ici_kendalltau( data_matrix, global_na = c(NA, Inf, 0), perspective = \"global\", scale_max = TRUE, diag_good = TRUE, include_only = NULL, check_timing = FALSE )"},{"path":"/reference/ici_kendalltau.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"information-content-informed kendall tau — ici_kendalltau","text":"data_matrix samples rows, features columns global_na globally, treated NA? perspective treat missing data denominator ties, see details scale_max everything scaled compared maximum correlation? diag_good diagonal entries reflect many entries sample \"good\"? include_only run correlations include members (vector) combinations (list data.frame) check_timing try estimate run time full dataset? (default FALSE)","code":""},{"path":"/reference/ici_kendalltau.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"information-content-informed kendall tau — ici_kendalltau","text":"list cor, raw, pval, taumax","code":""},{"path":"/reference/ici_kendalltau.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"information-content-informed kendall tau — ici_kendalltau","text":"details, see ICI-Kendall-tau vignette browseVignettes(\"ICIKendallTau\") default global_na includes values data replace NA Kendall-tau calculation. default global_na = c(NA, Inf, 0). want replace something 0, example, might use global_na = c(NA, Inf, -2), values -2 replaced instead 0. check_timing = TRUE, 5 random pairwise comparisons run generate timings single core, estimates long full set take calculated. data returned data.frame, low side, provide good idea long data take. Returned list containing matrices : cor: scaled correlations raw: raw kendall-tau correlations pval: p-values taumax: theoretical maximum kendall-tau value possible Eventually, plan provide two parameters replacing values, feature_na feature specific NA values sample_na sample specific NA values.","code":""},{"path":"/reference/ici_kendalltau.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"information-content-informed kendall tau — ici_kendalltau","text":"","code":"if (FALSE) { # not run set.seed(1234) s1 = sort(rnorm(1000, mean = 100, sd = 10)) s2 = s1 + 10 matrix_1 = cbind(s1, s2) r_1 = ici_kendalltau(t(matrix_1)) r_1$cor # s1 s2 # s1 1 1 # s2 1 1 names(r_1) # \"cor\", \"raw\", \"pval\", \"taumax\", \"keep\", \"run_time\" s3 = s1 s3[sample(100, 50)] = NA s4 = s2 s4[sample(100, 50)] = NA matrix_2 = cbind(s3, s4) r_2 = ici_kendalltau(t(matrix_2)) r_2$cor # s3 s4 # s3 1.0000000 0.9944616 # s4 0.9944616 1.0000000 # using include_only set.seed(1234) x = matrix(rnorm(5000), nrow = 100, ncol = 50) rownames(x) = paste0(\"s\", seq(1, nrow(x))) # only calculate correlations of other columns with \"s1\" include_s1 = \"s1\" s1_only = ici_kendalltau(x, include_only = include_s1) # include s1 and s3 things both include_s1s3 = c(\"s1\", \"s3\") s1s3_only = ici_kendalltau(x, include_only = include_s1s3) # only specify certain pairs either as a list include_pairs = list(g1 = \"s1\", g2 = c(\"s2\", \"s3\")) s1_other = ici_kendalltau(x, include_only = include_pairs) # or a data.frame include_df = as.data.frame(list(g1 = \"s1\", g2 = c(\"s2\", \"s3\"))) s1_df = ici_kendalltau(x, include_only = include_df) }"},{"path":"/reference/ici_kendalltau_ref.html","id":null,"dir":"Reference","previous_headings":"","what":"information-content-informed kendall tau — ici_kendalltau_ref","title":"information-content-informed kendall tau — ici_kendalltau_ref","text":"Given data-matrix, computes information-content-informed (ICI) Kendall-tau-b samples.","code":""},{"path":"/reference/ici_kendalltau_ref.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"information-content-informed kendall tau — ici_kendalltau_ref","text":"","code":"ici_kendalltau_ref( data_matrix, global_na = c(NA, Inf, 0), zero_value = 0, perspective = \"global\", scale_max = TRUE, diag_good = TRUE, progress = FALSE )"},{"path":"/reference/ici_kendalltau_ref.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"information-content-informed kendall tau — ici_kendalltau_ref","text":"data_matrix samples rows, features columns global_na values treated missing (NA)? zero_value actual zero value? perspective treat missing data denominator ties, see details scale_max everything scaled compared maximum correlation? diag_good diagonal entries reflect many entries sample \"good\"? progress progress displayed.","code":""},{"path":"/reference/ici_kendalltau_ref.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"information-content-informed kendall tau — ici_kendalltau_ref","text":"numeric","code":""},{"path":"/reference/ici_kendalltau_ref.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"information-content-informed kendall tau — ici_kendalltau_ref","text":"details, see ICI-Kendall-tau vignette: vignette(\"ici-kendalltau\", package = \"ICIKendallTau\")","code":""},{"path":"/reference/ici_kt.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates ici-kendall-tau — ici_kt","title":"Calculates ici-kendall-tau — ici_kt","text":"Calculates ici-kendall-tau","code":""},{"path":"/reference/ici_kt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates ici-kendall-tau — ici_kt","text":"","code":"ici_kt( x, y, perspective = \"local\", alternative = \"two.sided\", continuity = FALSE, output = \"simple\" )"},{"path":"/reference/ici_kt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates ici-kendall-tau — ici_kt","text":"x numeric vector y numeric vector perspective consider \"local\" \"global\" perspective? alternative alternative p-value test? continuity logical: true, continuity correction used output used control reporting values debugging","code":""},{"path":"/reference/ici_kt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates ici-kendall-tau — ici_kt","text":"kendall tau correlation, p-value, max-correlation","code":""},{"path":"/reference/ici_kt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculates ici-kendall-tau — ici_kt","text":"Calculates information-content-informed Kendall-tau correlation measure. correlation based concordant discordant ranked pairs, like Kendall-tau, also includes missing values (NA). Missing values assumed primarily due lack detection due instrumental sensitivity, therefore encode information. details see ICI-Kendall-tau vignette: browseVignettes(\"ICIKendallTau\")","code":""},{"path":"/reference/ici_kt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculates ici-kendall-tau — ici_kt","text":"","code":"x = sort(rnorm(100)) y = x + 1 y2 = y y2[1:10] = NA ici_kt(x, y) #> tau pvalue tau_max #> 1.000000e+00 3.480281e-49 1.000000e+00 ici_kt(x, y2, \"global\") #> tau pvalue tau_max #> 9.954442e-01 2.228308e-48 9.954442e-01 ici_kt(x, y2) #> tau pvalue tau_max #> 9.954442e-01 2.228308e-48 9.954442e-01"},{"path":"/reference/kt_fast.html","id":null,"dir":"Reference","previous_headings":"","what":"fast kendall tau — kt_fast","title":"fast kendall tau — kt_fast","text":"Uses underlying c++ implementation ici_kt provide fast version Kendall-tau correlation.","code":""},{"path":"/reference/kt_fast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"fast kendall tau — kt_fast","text":"","code":"kt_fast(x, y = NULL, use = \"everything\")"},{"path":"/reference/kt_fast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"fast kendall tau — kt_fast","text":"x numeric vector, matrix, data frame. y NULL (default) vector. use optional character string giving method computing covariances presence missing values. must (abbreviation ) one strings \"everything\", \".obs\", \"complete.obs\", \"pairwise.complete.obs\".","code":""},{"path":"/reference/kt_fast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"fast kendall tau — kt_fast","text":"named vector list matrices.","code":""},{"path":"/reference/kt_fast.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"fast kendall tau — kt_fast","text":"Although interface mostly identical built-stats::cor method, differences. providing x y, assumed single vectors. NA values present, function error, either remove return NA, depending option. \"na..complete\" valid option use. named vector named list matrices returned, tau pvalue values.","code":""},{"path":"/reference/pairwise_completeness.html","id":null,"dir":"Reference","previous_headings":"","what":"pairwise completeness — pairwise_completeness","title":"pairwise completeness — pairwise_completeness","text":"Calculates completeness two samples using \"\", entry missing either X \"\" Y.","code":""},{"path":"/reference/pairwise_completeness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"pairwise completeness — pairwise_completeness","text":"","code":"pairwise_completeness(data_matrix, global_na = c(NA, Inf, 0), zero_value = 0)"},{"path":"/reference/pairwise_completeness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"pairwise completeness — pairwise_completeness","text":"data_matrix samples rows, features columns global_na globally, treated NA? zero_value actual zero value?","code":""},{"path":"/reference/pairwise_completeness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"pairwise completeness — pairwise_completeness","text":"matrix degree completeness","code":""},{"path":"/news/index.html","id":"icikendalltau-0210","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.2.10","title":"ICIKendallTau 0.2.10","text":"Provide kt_fast function handles missing NA values similarly stats::cor function, uses ici_kt fast function underneath.","code":""},{"path":"/news/index.html","id":"icikendalltau-028","dir":"Changelog","previous_headings":"","what":"ICIKendalltau 0.2.8","title":"ICIKendalltau 0.2.8","text":"Made error outputs length default output containing NA. Check case one variables ici_kt identical arguments, warns user returns NA.","code":""},{"path":"/news/index.html","id":"icikendalltau-021","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.2.1","title":"ICIKendallTau 0.2.1","text":"Added new argument include_only ici_kendalltau allows user define pairwise correlations actually .","code":""},{"path":"/news/index.html","id":"icikendalltau-0117","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.1.17","title":"ICIKendallTau 0.1.17","text":"switched theme. updated installation instructions use r-universe.","code":""},{"path":"/news/index.html","id":"icikendalltau-0116","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.1.16","title":"ICIKendallTau 0.1.16","text":"updating documentation examples","code":""},{"path":"/news/index.html","id":"icikendalltau-014","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.1.4","title":"ICIKendallTau 0.1.4","text":"Switched pairwise_completeness also use global_na parameter. Oh yes, function pairwise_completeness enable scaling completeness two samples.","code":""},{"path":"/news/index.html","id":"icikendalltau-012","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.1.2","title":"ICIKendallTau 0.1.2","text":"Updated API use single variable, global_na defines values set NA correlation calculation. big API change, bumped version 0.1.","code":""},{"path":"/news/index.html","id":"icikendalltau-006","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.0.6","title":"ICIKendallTau 0.0.6","text":"Fixing bug 55,000 elements vector, match overflows 32 bit 64 bit, results make sense.","code":""},{"path":"/news/index.html","id":"icikendalltau-005","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.0.5","title":"ICIKendallTau 0.0.5","text":"Fixed bug instead returning two element vector, returned zero length value. probably happened one entries contained NA values, tried pass “vectors” less two entries.","code":""},{"path":"/news/index.html","id":"icikendalltau-004","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.0.4","title":"ICIKendallTau 0.0.4","text":"Fixing things came R CMD check documentation. good now (hope).","code":""},{"path":"/news/index.html","id":"icikendalltau-003","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.0.3","title":"ICIKendallTau 0.0.3","text":"Added tests! Removed bunch code wasn’t necessary using incorrect formula’s, correct version ici_kt_pairs function reference purposes.","code":""},{"path":"/news/index.html","id":"icikendalltau-002","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.0.2","title":"ICIKendallTau 0.0.2","text":"Fixed issue warning issued {furrr} installed.","code":""},{"path":"/news/index.html","id":"icikendalltau-001","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.0.1","title":"ICIKendallTau 0.0.1","text":"First release. ’s tested used rather thoroughly , ’d say ’s still rather alpha. Even though use time . Added NEWS.md file track changes package.","code":""}] +[{"path":[]},{"path":"/CODE_OF_CONDUCT.html","id":"our-pledge","dir":"","previous_headings":"","what":"Our Pledge","title":"Contributor Covenant Code of Conduct","text":"members, contributors, leaders pledge make participation community harassment-free experience everyone, regardless age, body size, visible invisible disability, ethnicity, sex characteristics, gender identity expression, level experience, education, socio-economic status, nationality, personal appearance, race, religion, sexual identity orientation. pledge act interact ways contribute open, welcoming, diverse, inclusive, healthy community.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"our-standards","dir":"","previous_headings":"","what":"Our Standards","title":"Contributor Covenant Code of Conduct","text":"Examples behavior contributes positive environment community include: Demonstrating empathy kindness toward people respectful differing opinions, viewpoints, experiences Giving gracefully accepting constructive feedback Accepting responsibility apologizing affected mistakes, learning experience Focusing best just us individuals, overall community Examples unacceptable behavior include: use sexualized language imagery, sexual attention advances kind Trolling, insulting derogatory comments, personal political attacks Public private harassment Publishing others’ private information, physical email address, without explicit permission conduct reasonably considered inappropriate professional setting","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"enforcement-responsibilities","dir":"","previous_headings":"","what":"Enforcement Responsibilities","title":"Contributor Covenant Code of Conduct","text":"Community leaders responsible clarifying enforcing standards acceptable behavior take appropriate fair corrective action response behavior deem inappropriate, threatening, offensive, harmful. Community leaders right responsibility remove, edit, reject comments, commits, code, wiki edits, issues, contributions aligned Code Conduct, communicate reasons moderation decisions appropriate.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"scope","dir":"","previous_headings":"","what":"Scope","title":"Contributor Covenant Code of Conduct","text":"Code Conduct applies within community spaces, also applies individual officially representing community public spaces. Examples representing community include using official e-mail address, posting via official social media account, acting appointed representative online offline event.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"enforcement","dir":"","previous_headings":"","what":"Enforcement","title":"Contributor Covenant Code of Conduct","text":"Instances abusive, harassing, otherwise unacceptable behavior may reported community leaders responsible enforcement rflight79@gmail.com. complaints reviewed investigated promptly fairly. community leaders obligated respect privacy security reporter incident.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"enforcement-guidelines","dir":"","previous_headings":"","what":"Enforcement Guidelines","title":"Contributor Covenant Code of Conduct","text":"Community leaders follow Community Impact Guidelines determining consequences action deem violation Code Conduct:","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"id_1-correction","dir":"","previous_headings":"Enforcement Guidelines","what":"1. 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Permanent Ban","title":"Contributor Covenant Code of Conduct","text":"Community Impact: Demonstrating pattern violation community standards, including sustained inappropriate behavior, harassment individual, aggression toward disparagement classes individuals. Consequence: permanent ban sort public interaction within community.","code":""},{"path":"/CODE_OF_CONDUCT.html","id":"attribution","dir":"","previous_headings":"","what":"Attribution","title":"Contributor Covenant Code of Conduct","text":"Code Conduct adapted Contributor Covenant, version 2.0, available https://www.contributor-covenant.org/version/2/0/ code_of_conduct.html. Community Impact Guidelines inspired Mozilla’s code conduct enforcement ladder. answers common questions code conduct, see FAQ https://www.contributor-covenant.org/faq. 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EVENT SHALL AUTHORS COPYRIGHT HOLDERS LIABLE CLAIM, DAMAGES LIABILITY, WHETHER ACTION CONTRACT, TORT OTHERWISE, ARISING , CONNECTION SOFTWARE USE DEALINGS SOFTWARE.","code":""},{"path":"/articles/ici-kendalltau.html","id":"problem","dir":"Articles","previous_headings":"","what":"Problem","title":"Information-Content-Informed Kendall Tau Correlation","text":"handle missing data (.e. NA’s) calculating correlation two variables. However, whether observation made semi-quantitative information many analytical measurements sensitivity limits. .e. many cases, missing observations “missing--random”, “missing---random” due falling detection limit. cases, NA informative.","code":""},{"path":"/articles/ici-kendalltau.html","id":"approach","dir":"Articles","previous_headings":"","what":"Approach","title":"Information-Content-Informed Kendall Tau Correlation","text":"Kendall Tau Correlation coefficient calculates correlation based number concordant discordant pairs: \\(\\tau = \\frac{ | pairs_{concordant} | - | pairs_{discordant} |}{\\binom{n}{2}}\\) pair two x-y data points. \\(x_i > x_j\\) \\(y_i > y_j\\) \\(x_i < x_j\\) \\(y_i < y_j\\) \\(x_i > x_j\\) \\(y_i < y_j\\) \\(x_i < x_j\\) \\(y_i > y_j\\) definitions can expanded handle missing observations: \\(x_i > x_j\\) \\(y_i > y_j\\) \\(x_i < x_j\\) \\(y_i < y_j\\) \\(x_i > x_j\\) \\(y_i \\& !y_j\\) \\(x_i < x_j\\) \\(!y_i \\& y_j\\) \\(x_i \\& !x_j\\) \\(y_i > y_j\\) \\(!x_i \\& x_j\\) \\(y_i < y_j\\) \\(x_i \\& !x_j\\) \\(y_i \\& !y_j\\) (used local perspective version) \\(x_i \\& x_j\\) \\(!y_i \\& y_j\\) (used local perspective version) \\(x_i > x_j\\) \\(y_i < y_j\\) \\(x_i < x_j\\) \\(y_i > y_j\\) \\(x_i > x_j\\) \\(!y_i \\& y_j\\) \\(x_i < x_j\\) \\(y_i \\& !y_j\\) \\(x_i \\& !x_j\\) \\(y_i < y_j\\) \\(!x_i \\& x_j\\) \\(y_i > y_j\\) \\(x_i \\& !x_j\\) \\(!y_i \\& y_j\\) (used local perspective version) \\(!x_i \\& x_j\\) \\(y_i \\& !y_j\\) (used local perspective version) Also data points missing x y values naturally reduce strength correlation value, since can neither concordant discordant another (NA, NA) pair, impact denominator. equivalent removing last two concordant discordant pair tests.","code":""},{"path":"/articles/ici-kendalltau.html","id":"handling-tied-values","dir":"Articles","previous_headings":"","what":"Handling Tied Values","title":"Information-Content-Informed Kendall Tau Correlation","text":"base Kendall tau correlation must adjusted handle tied values, ie. tau-b version equation. \\[\\tau = \\frac{ | pairs_{concordant} | - | pairs_{discordant} |}{\\sqrt{ ( n_0 - n_{xtie} ) ( n_0 - n_{ytie} )}} \\] : \\(n_0 = \\frac{n \\left ( n - 1 \\right )}{2}\\) \\(n_{xtie} = \\sum_{}^{} \\frac{t_{xi} \\left ( t_{xi} - 1 \\right )}{2}\\) \\(n_{ytie} = \\sum_{}^{} \\frac{t_{yi} \\left ( t_{yi} - 1 \\right )}{2}\\) \\(t_{xi}\\) - size ith group tied x values. \\(t_{yi}\\) - size ith group tied y values. local perspective, number NAs x y can treated group tied values calculation \\(n_{xtie}\\) \\(n_{ytie}\\), respectively.","code":""},{"path":"/articles/ici-kendalltau.html","id":"scaling-by-the-correlation-with-the-highest-information-content","dir":"Articles","previous_headings":"","what":"Scaling by the correlation with the highest information content","title":"Information-Content-Informed Kendall Tau Correlation","text":"generating correlation matrix (heatmap) large analytical datasets, number observations common can become quite low two variables. becomes advantageous scale pair variables highest information content. One objective scaling factor highest possible absolute correlation maximum information content observed across dataset, dividing maximum possible absolute correlation scale whole dataset appropriately. \\[maxcorr = \\frac{\\binom{n-m}{2} + n * m}{\\binom{n-m}{2} + n * m + \\binom{m}{2}}\\] : Choose two variables least number missing values across dataset. n length variables. formula based perfect correlation given number (NA,NA) pairs added.","code":""},{"path":"/articles/ici-kendalltau.html","id":"functions","dir":"Articles","previous_headings":"","what":"Functions","title":"Information-Content-Informed Kendall Tau Correlation","text":"functions implement include: option perspective control NA values influence ties. comparing multiple samples, likely want use perspective = \"global\". Rows samples, columns features. library(furrr) plan(multiprocess) Otherwise use single core.","code":""},{"path":"/articles/ici-kendalltau.html","id":"implementation","dir":"Articles","previous_headings":"","what":"Implementation","title":"Information-Content-Informed Kendall Tau Correlation","text":"turns , think really hard, truly necessary replace missing values vector value smaller minimum value one. local version, first remove common missing values vector. C++ implementation explicitly can speed, instead wrapping {stats::cor} function. also use double merge-sort algorithm, translating {scipy:: ::stats::kendalltau} function C++ using {Rcpp}.","code":""},{"path":"/articles/ici-kendalltau.html","id":"speed","dir":"Articles","previous_headings":"","what":"Speed","title":"Information-Content-Informed Kendall Tau Correlation","text":"","code":"x = rnorm(1000) y = rnorm(1000) library(microbenchmark) microbenchmark( cor(x, y, method = \"kendall\"), ici_kt(x, y, \"global\"), times = 5 ) #> Unit: microseconds #> expr min lq mean median #> cor(x, y, method = \"kendall\") 12095.660 12738.316 13295.7824 13155.880 #> ici_kt(x, y, \"global\") 244.055 246.677 302.6702 275.807 #> uq max neval #> 13565.978 14923.078 5 #> 303.536 443.276 5 all.equal(ici_kt(x, y, \"global\")[[1]], cor(x, y, method = \"kendall\")) #> [1] TRUE"},{"path":"/articles/ici-kendalltau.html","id":"running-many","dir":"Articles","previous_headings":"","what":"Running Many","title":"Information-Content-Informed Kendall Tau Correlation","text":"Just like R’s cor function, can also calculate correlations many variables. contrast R’s function, ici_kendalltau calculates rows matrix. Let’s make fake data try .","code":"set.seed(1234) s1 = sort(rnorm(1000, mean = 100, sd = 10)) s2 = s1 + 10 s2[sample(length(s1), 100)] = s1[1:100] s3 = s1 s3[c(1:10, sample(length(s1), 5))] = NA matrix_1 = cbind(s1, s2, s3) r_1 = ici_kendalltau(t(matrix_1)) r_1$cor #> s1 s2 s3 #> s1 1.0000000 0.8049209 0.9907488 #> s2 0.8049209 1.0000000 0.7956652 #> s3 0.9907488 0.7956652 0.9850000"},{"path":"/articles/ici-kendalltau.html","id":"parallelism","dir":"Articles","previous_headings":"","what":"Parallelism","title":"Information-Content-Informed Kendall Tau Correlation","text":"{future} {furrr} packages installed, also possible split set matrix comparisons across compute resources multiprocessing engine registered {future}.","code":"library(furrr) future::plan(multicore, workers = 4) r_2 = ici_kendalltau(t(matrix_1))"},{"path":"/articles/ici-kendalltau.html","id":"many-many-comparisons","dir":"Articles","previous_headings":"","what":"Many Many Comparisons","title":"Information-Content-Informed Kendall Tau Correlation","text":"case hundreds thousands comparisons done, result matrices can become , large, require lots memory storage. also inefficient, lower upper triangular components stored. alternative storage format data.frame, single row comparison performed. actually results stored internally, converted matrix form requested (default).s keep data.frame output, add argument return_matrix=FALSE call ici_kendalltau.","code":"r_3 = ici_kendalltau(t(matrix_1), return_matrix = FALSE) r_3$cor #> s1 s2 core raw pvalue taumax cor #> 1 s1 s2 1 0.8049209 0 1.0000000 0.8049209 #> 2 s1 s3 1 0.9907488 0 0.9998949 0.9907488 #> 3 s2 s3 1 0.7956652 0 0.9998949 0.7956652 #> 4 s1 s1 0 1.0000000 0 1.0000000 1.0000000 #> 5 s2 s2 0 1.0000000 0 1.0000000 1.0000000 #> 6 s3 s3 0 0.9850000 0 1.0000000 0.9850000"},{"path":"/authors.html","id":null,"dir":"","previous_headings":"","what":"Authors","title":"Authors and Citation","text":"Robert M Flight. Author, maintainer. Hunter NB Moseley. Author.","code":""},{"path":"/authors.html","id":"citation","dir":"","previous_headings":"","what":"Citation","title":"Authors and Citation","text":"Flight RM, Moseley HN (2024). ICIKendallTau: Calculates information-content-informed Kendall-tau. R package version 0.3.21, https://moseleybioinformaticslab.github.io/ICIKendallTauhttps://github.com/moseleybioinformaticslab/ICIKendallTau.","code":"@Manual{, title = {ICIKendallTau: Calculates information-content-informed Kendall-tau}, author = {Robert M Flight and Hunter NB Moseley}, year = {2024}, note = {R package version 0.3.21}, url = {https://moseleybioinformaticslab.github.io/ICIKendallTau https://github.com/moseleybioinformaticslab/ICIKendallTau}, }"},{"path":"/index.html","id":"icikendalltau","dir":"","previous_headings":"","what":"Calculates information-content-informed Kendall-tau","title":"Calculates information-content-informed Kendall-tau","text":"can see pkgdown site .","code":""},{"path":"/index.html","id":"installation","dir":"","previous_headings":"","what":"Installation","title":"Calculates information-content-informed Kendall-tau","text":"can install current version ICIKendallTau via GitHub: can also install Windows Mac binaries using r-universe:","code":"remotes::install_github(\"MoseleyBioinformaticsLab/ICIKendallTau\") options(repos = c( moseleybioinformaticslab = 'https://moseleybioinformaticslab.r-universe.dev', CRAN = \"https://cloud.r-project.org\")) install.packages(\"ICIKendallTau\")"},{"path":"/index.html","id":"problem","dir":"","previous_headings":"","what":"Problem","title":"Calculates information-content-informed Kendall-tau","text":"handle missing data (.e. NA’s) calculating correlation two variables. However, whether observation present missing semi-quantitative information many analytical measurements sensitivity limits. .e. many cases, missing observations “missing--random”, “missing---random” due falling detection limit. cases, NA informative. Therefore, analytical measurements (gene expression, proteomics, metabolomics), missing measurements included, contribute correlation. want read solve problem, see package vignette.","code":""},{"path":"/index.html","id":"package-functions","dir":"","previous_headings":"","what":"Package Functions","title":"Calculates information-content-informed Kendall-tau","text":"functions implement include: option perspective control NA values influence ties. comparing samples, likely want use perspective = \"global\". Rows samples, columns features. library(furrr) plan(multiprocess) Otherwise use single core.","code":""},{"path":"/index.html","id":"examples","dir":"","previous_headings":"","what":"Examples","title":"Calculates information-content-informed Kendall-tau","text":"common case large matrix independent samples (columns) measured features samples (.e. gene expression). make artificial data show correlation changes introduce missingness. Now introduce missing values low end one. just simplest thing introduce NA values bottom set.","code":"set.seed(1234) library(ICIKendallTau) s1 = sort(rnorm(1000, mean = 100, sd = 10)) s2 = s1 + 10 matrix_1 = cbind(s1, s2) r_1 = ici_kendalltau(t(matrix_1)) r_1$cor #> s1 s2 #> s1 1 1 #> s2 1 1 s3 = s1 s3[sample(100, 50)] = NA s4 = s2 s4[sample(100, 50)] = NA matrix_2 = cbind(s3, s4) r_2 = ici_kendalltau(t(matrix_2)) r_2$cor #> s3 s4 #> s3 1.0000000 0.9944616 #> s4 0.9944616 1.0000000"},{"path":"/index.html","id":"is-it-fast","dir":"","previous_headings":"","what":"Is It Fast?","title":"Calculates information-content-informed Kendall-tau","text":"C++ code implementation (thanks {Rcpp}!) based SciPy implementation, uses two merge sorts ranks vector, looks differences . fastest method know , complexity O(nlogn). naive way computing , explicitly examines pairs, complexity n^2. implementation compared {pcaPP::cov.fk} function, use {Rcpp} inefficient copying vectors makes 3X slower one. honestly isn’t bad. case 40,000 features, average time modern CPU 14 milliseconds. course, want use calculate Kendall-tau-b without incorporating missingness, can just fine well. also provide kt_fast function, want something treats NA values similarly stats::cor.","code":"library(microbenchmark) x = rnorm(1000) y = rnorm(1000) x2 = rnorm(40000) y2 = rnorm(40000) library(microbenchmark) microbenchmark( cor(x, y, method = \"kendall\"), ici_kt(x, y, \"global\"), ici_kt(x2, y2, \"global\"), times = 5 ) #> Unit: microseconds #> expr min lq mean median uq max #> cor(x, y, method = \"kendall\") 13379.183 13401.532 13493.0316 13401.829 13595.070 13687.544 #> ici_kt(x, y, \"global\") 271.586 279.007 369.9088 314.779 357.086 627.086 #> ici_kt(x2, y2, \"global\") 15983.902 16484.666 16952.9788 16667.885 17124.932 18503.509 #> neval #> 5 #> 5 #> 5 k_tau = ici_kt(x, y, \"global\") all.equal(k_tau[[1]] ,cor(x, y, method = \"kendall\")) #> [1] TRUE k_tau_fast = kt_fast(x, y) k_tau_fast #> tau pvalue #> -0.003411411 0.871672260"},{"path":"/index.html","id":"parallelism","dir":"","previous_headings":"","what":"Parallelism","title":"Calculates information-content-informed Kendall-tau","text":"{future} {furrr} packages installed, also possible split set matrix comparisons across compute resources multiprocessing engine registered {future}.","code":"library(furrr) future::plan(multicore, workers = 4) r_3 = ici_kendalltau(t(matrix_2))"},{"path":"/index.html","id":"many-many-comparisons","dir":"","previous_headings":"","what":"Many Many Comparisons","title":"Calculates information-content-informed Kendall-tau","text":"case hundreds thousands comparisons done, result matrices can become , large, require lots memory storage. also inefficient, lower upper triangular components stored. alternative storage format data.frame, single row comparison performed. actually results stored internally, converted matrix form requested (default).s keep data.frame output, add argument return_matrix=FALSE call ici_kendalltau.","code":"r_4 = ici_kendalltau(t(matrix_2), return_matrix = FALSE) r_4 #> $cor #> s1 s2 core raw pvalue taumax cor #> 1 s3 s4 1 0.9924359 0 0.997963 0.9944616 #> 2 s3 s3 0 1.0000000 0 1.000000 1.0000000 #> 3 s4 s4 0 1.0000000 0 1.000000 1.0000000 #> #> $run_time #> [1] 0.02034521"},{"path":"/index.html","id":"code-of-conduct","dir":"","previous_headings":"","what":"Code of Conduct","title":"Calculates information-content-informed Kendall-tau","text":"Please note ICIKendallTau project released Contributor Code Conduct. contributing project, agree abide terms.","code":""},{"path":"/reference/cor_matrix_2_long_df.html","id":null,"dir":"Reference","previous_headings":"","what":"convert matrix to data.frame — cor_matrix_2_long_df","title":"convert matrix to data.frame — cor_matrix_2_long_df","text":"Given square correlation matrix, converts long data.frame, three columns.","code":""},{"path":"/reference/cor_matrix_2_long_df.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"convert matrix to data.frame — cor_matrix_2_long_df","text":"","code":"cor_matrix_2_long_df(in_matrix)"},{"path":"/reference/cor_matrix_2_long_df.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"convert matrix to data.frame — cor_matrix_2_long_df","text":"cor_matrix correlation matrix","code":""},{"path":"/reference/cor_matrix_2_long_df.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"convert matrix to data.frame — cor_matrix_2_long_df","text":"data.frame","code":""},{"path":"/reference/cor_matrix_2_long_df.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"convert matrix to data.frame — cor_matrix_2_long_df","text":"data.frame contains three columns: s1: first entry comparison s2: second entry comparison cor: correlation value","code":""},{"path":"/reference/disable_logging.html","id":null,"dir":"Reference","previous_headings":"","what":"turn logging off — disable_logging","title":"turn logging off — disable_logging","text":"may good reasons turn logging turned . basically tells package logger available.","code":""},{"path":"/reference/disable_logging.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"turn logging off — disable_logging","text":"","code":"disable_logging()"},{"path":"/reference/enable_logging.html","id":null,"dir":"Reference","previous_headings":"","what":"turn logging on — enable_logging","title":"turn logging on — enable_logging","text":"Choose enable logging, specific file desired.","code":""},{"path":"/reference/enable_logging.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"turn logging on — enable_logging","text":"","code":"enable_logging(log_file = NULL, memory = FALSE)"},{"path":"/reference/enable_logging.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"turn logging on — enable_logging","text":"log_file file log memory provide memory logging ? available Linux MacOS","code":""},{"path":"/reference/enable_logging.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"turn logging on — enable_logging","text":"Uses logger package hood, suggested dependencies. logging enabled nice see things starting stopping, exactly done, without needing write messages console. especially useful getting errors, really see , can add \"memory\" logging see running memory. Default log file pattern: YYYY.MM.DD.HH.MM.SS_ICIKendallTau_run.log","code":""},{"path":"/reference/ici_kendalltau.html","id":null,"dir":"Reference","previous_headings":"","what":"information-content-informed kendall tau — ici_kendalltau","title":"information-content-informed kendall tau — ici_kendalltau","text":"Given data-matrix, computes information-theoretic Kendall-tau-b samples.","code":""},{"path":"/reference/ici_kendalltau.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"information-content-informed kendall tau — ici_kendalltau","text":"","code":"ici_kendalltau( data_matrix, global_na = c(NA, Inf, 0), perspective = \"global\", scale_max = TRUE, diag_good = TRUE, include_only = NULL, check_timing = FALSE, return_matrix = TRUE )"},{"path":"/reference/ici_kendalltau.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"information-content-informed kendall tau — ici_kendalltau","text":"data_matrix samples rows, features columns global_na globally, treated NA? perspective treat missing data denominator ties, see details scale_max everything scaled compared maximum correlation? diag_good diagonal entries reflect many entries sample \"good\"? include_only run correlations include members (vector) combinations (list data.frame) check_timing try estimate run time full dataset? (default FALSE) return_matrix data.frame matrix result returned?","code":""},{"path":"/reference/ici_kendalltau.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"information-content-informed kendall tau — ici_kendalltau","text":"list cor, raw, pval, taumax","code":""},{"path":"/reference/ici_kendalltau.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"information-content-informed kendall tau — ici_kendalltau","text":"details, see ICI-Kendall-tau vignette browseVignettes(\"ICIKendallTau\") default global_na includes values data replace NA Kendall-tau calculation. default global_na = c(NA, Inf, 0). want replace something 0, example, might use global_na = c(NA, Inf, -2), values -2 replaced instead 0. check_timing = TRUE, 5 random pairwise comparisons run generate timings single core, estimates long full set take calculated. data returned data.frame, low side, provide good idea long data take. Returned list containing matrices : cor: scaled correlations raw: raw kendall-tau correlations pval: p-values taumax: theoretical maximum kendall-tau value possible Eventually, plan provide two parameters replacing values, feature_na feature specific NA values sample_na sample specific NA values.","code":""},{"path":"/reference/ici_kendalltau.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"information-content-informed kendall tau — ici_kendalltau","text":"","code":"if (FALSE) { # not run set.seed(1234) s1 = sort(rnorm(1000, mean = 100, sd = 10)) s2 = s1 + 10 matrix_1 = cbind(s1, s2) r_1 = ici_kendalltau(t(matrix_1)) r_1$cor # s1 s2 # s1 1 1 # s2 1 1 names(r_1) # \"cor\", \"raw\", \"pval\", \"taumax\", \"keep\", \"run_time\" s3 = s1 s3[sample(100, 50)] = NA s4 = s2 s4[sample(100, 50)] = NA matrix_2 = cbind(s3, s4) r_2 = ici_kendalltau(t(matrix_2)) r_2$cor # s3 s4 # s3 1.0000000 0.9944616 # s4 0.9944616 1.0000000 # using include_only set.seed(1234) x = matrix(rnorm(5000), nrow = 100, ncol = 50) rownames(x) = paste0(\"s\", seq(1, nrow(x))) # only calculate correlations of other columns with \"s1\" include_s1 = \"s1\" s1_only = ici_kendalltau(x, include_only = include_s1) # include s1 and s3 things both include_s1s3 = c(\"s1\", \"s3\") s1s3_only = ici_kendalltau(x, include_only = include_s1s3) # only specify certain pairs either as a list include_pairs = list(g1 = \"s1\", g2 = c(\"s2\", \"s3\")) s1_other = ici_kendalltau(x, include_only = include_pairs) # or a data.frame include_df = as.data.frame(list(g1 = \"s1\", g2 = c(\"s2\", \"s3\"))) s1_df = ici_kendalltau(x, include_only = include_df) }"},{"path":"/reference/ici_kendalltau_ref.html","id":null,"dir":"Reference","previous_headings":"","what":"information-content-informed kendall tau — ici_kendalltau_ref","title":"information-content-informed kendall tau — ici_kendalltau_ref","text":"Given data-matrix, computes information-content-informed (ICI) Kendall-tau-b samples.","code":""},{"path":"/reference/ici_kendalltau_ref.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"information-content-informed kendall tau — ici_kendalltau_ref","text":"","code":"ici_kendalltau_ref( data_matrix, global_na = c(NA, Inf, 0), zero_value = 0, perspective = \"global\", scale_max = TRUE, diag_good = TRUE, progress = FALSE )"},{"path":"/reference/ici_kendalltau_ref.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"information-content-informed kendall tau — ici_kendalltau_ref","text":"data_matrix samples rows, features columns global_na values treated missing (NA)? zero_value actual zero value? perspective treat missing data denominator ties, see details scale_max everything scaled compared maximum correlation? diag_good diagonal entries reflect many entries sample \"good\"? progress progress displayed.","code":""},{"path":"/reference/ici_kendalltau_ref.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"information-content-informed kendall tau — ici_kendalltau_ref","text":"numeric","code":""},{"path":"/reference/ici_kendalltau_ref.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"information-content-informed kendall tau — ici_kendalltau_ref","text":"details, see ICI-Kendall-tau vignette: vignette(\"ici-kendalltau\", package = \"ICIKendallTau\")","code":""},{"path":"/reference/ici_kt.html","id":null,"dir":"Reference","previous_headings":"","what":"Calculates ici-kendall-tau — ici_kt","title":"Calculates ici-kendall-tau — ici_kt","text":"Calculates ici-kendall-tau","code":""},{"path":"/reference/ici_kt.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"Calculates ici-kendall-tau — ici_kt","text":"","code":"ici_kt( x, y, perspective = \"local\", alternative = \"two.sided\", continuity = FALSE, output = \"simple\" )"},{"path":"/reference/ici_kt.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"Calculates ici-kendall-tau — ici_kt","text":"x numeric vector y numeric vector perspective consider \"local\" \"global\" perspective? alternative alternative p-value test? continuity logical: true, continuity correction used output used control reporting values debugging","code":""},{"path":"/reference/ici_kt.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"Calculates ici-kendall-tau — ici_kt","text":"kendall tau correlation, p-value, max-correlation","code":""},{"path":"/reference/ici_kt.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"Calculates ici-kendall-tau — ici_kt","text":"Calculates information-content-informed Kendall-tau correlation measure. correlation based concordant discordant ranked pairs, like Kendall-tau, also includes missing values (NA). Missing values assumed primarily due lack detection due instrumental sensitivity, therefore encode information. details see ICI-Kendall-tau vignette: browseVignettes(\"ICIKendallTau\")","code":""},{"path":"/reference/ici_kt.html","id":"ref-examples","dir":"Reference","previous_headings":"","what":"Examples","title":"Calculates ici-kendall-tau — ici_kt","text":"","code":"x = sort(rnorm(100)) y = x + 1 y2 = y y2[1:10] = NA ici_kt(x, y) #> tau pvalue tau_max #> 1.000000e+00 3.480281e-49 1.000000e+00 ici_kt(x, y2, \"global\") #> tau pvalue tau_max #> 9.954442e-01 2.228308e-48 9.954442e-01 ici_kt(x, y2) #> tau pvalue tau_max #> 9.954442e-01 2.228308e-48 9.954442e-01"},{"path":"/reference/kt_fast.html","id":null,"dir":"Reference","previous_headings":"","what":"fast kendall tau — kt_fast","title":"fast kendall tau — kt_fast","text":"Uses underlying c++ implementation ici_kt provide fast version Kendall-tau correlation.","code":""},{"path":"/reference/kt_fast.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"fast kendall tau — kt_fast","text":"","code":"kt_fast(x, y = NULL, use = \"everything\", return_matrix = TRUE)"},{"path":"/reference/kt_fast.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"fast kendall tau — kt_fast","text":"x numeric vector, matrix, data frame. y NULL (default) vector. use optional character string giving method computing correlations presence missing values. must (abbreviation ) one strings \"everything\", \".obs\", \"complete.obs\", \"pairwise.complete.obs\". return_matrix matrices values returned, long data.frame","code":""},{"path":"/reference/kt_fast.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"fast kendall tau — kt_fast","text":"named vector list matrices.","code":""},{"path":"/reference/kt_fast.html","id":"details","dir":"Reference","previous_headings":"","what":"Details","title":"fast kendall tau — kt_fast","text":"Although interface mostly identical built-stats::cor method, differences. providing x y, assumed single vectors. NA values present, function error, either remove return NA, depending option. \"na..complete\" valid option use. named vector named list matrices returned, tau pvalue values.","code":""},{"path":"/reference/log_memory.html","id":null,"dir":"Reference","previous_headings":"","what":"log memory usage — log_memory","title":"log memory usage — log_memory","text":"Logs amount memory used log file available, generating warnings amount RAM hits zero.","code":""},{"path":"/reference/log_memory.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"log memory usage — log_memory","text":"","code":"log_memory()"},{"path":"/reference/log_message.html","id":null,"dir":"Reference","previous_headings":"","what":"log messages — log_message","title":"log messages — log_message","text":"log_appender available, logs given message info level.","code":""},{"path":"/reference/log_message.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"log messages — log_message","text":"","code":"log_message(message_string)"},{"path":"/reference/log_message.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"log messages — log_message","text":"message_string string put message","code":""},{"path":"/reference/long_df_2_cor_matrix.html","id":null,"dir":"Reference","previous_headings":"","what":"convert data.frame to matrix — long_df_2_cor_matrix","title":"convert data.frame to matrix — long_df_2_cor_matrix","text":"Given long data.frame, converts possibly square correlation matrix","code":""},{"path":"/reference/long_df_2_cor_matrix.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"convert data.frame to matrix — long_df_2_cor_matrix","text":"","code":"long_df_2_cor_matrix(long_df, is_square = TRUE)"},{"path":"/reference/long_df_2_cor_matrix.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"convert data.frame to matrix — long_df_2_cor_matrix","text":"long_df long data.frame","code":""},{"path":"/reference/long_df_2_cor_matrix.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"convert data.frame to matrix — long_df_2_cor_matrix","text":"matrix","code":""},{"path":"/reference/pairwise_completeness.html","id":null,"dir":"Reference","previous_headings":"","what":"pairwise completeness — pairwise_completeness","title":"pairwise completeness — pairwise_completeness","text":"Calculates completeness two samples using \"\", entry missing either X \"\" Y.","code":""},{"path":"/reference/pairwise_completeness.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"pairwise completeness — pairwise_completeness","text":"","code":"pairwise_completeness( data_matrix, global_na = c(NA, Inf, 0), include_only = NULL, return_matrix = TRUE )"},{"path":"/reference/pairwise_completeness.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"pairwise completeness — pairwise_completeness","text":"data_matrix samples rows, features columns global_na globally, treated NA? include_only certain comparisons ? return_matrix matrix data.frame returned?","code":""},{"path":"/reference/pairwise_completeness.html","id":"value","dir":"Reference","previous_headings":"","what":"Value","title":"pairwise completeness — pairwise_completeness","text":"matrix degree completeness","code":""},{"path":"/reference/show_progress.html","id":null,"dir":"Reference","previous_headings":"","what":"turn progress on off — show_progress","title":"turn progress on off — show_progress","text":"Allow user turn progress messages console . Default provide messages console.","code":""},{"path":"/reference/show_progress.html","id":"ref-usage","dir":"Reference","previous_headings":"","what":"Usage","title":"turn progress on off — show_progress","text":"","code":"show_progress(progress = TRUE)"},{"path":"/reference/show_progress.html","id":"arguments","dir":"Reference","previous_headings":"","what":"Arguments","title":"turn progress on off — show_progress","text":"progress logical ","code":""},{"path":"/news/index.html","id":"icikendalltau-0320","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.3.20","title":"ICIKendallTau 0.3.20","text":"kt_fast now uses data.frame format output ici_kendalltau, returns matrix default. data.frame useful large amounts comparisons run.","code":""},{"path":"/news/index.html","id":"icikendalltau-030","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.3.0","title":"ICIKendallTau 0.3.0","text":"Handling parallel execution differently avoid really large matrix issues core. 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Removed bunch code wasn’t necessary using incorrect formula’s, correct version ici_kt_pairs function reference purposes.","code":""},{"path":"/news/index.html","id":"icikendalltau-002","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.0.2","title":"ICIKendallTau 0.0.2","text":"Fixed issue warning issued {furrr} installed.","code":""},{"path":"/news/index.html","id":"icikendalltau-001","dir":"Changelog","previous_headings":"","what":"ICIKendallTau 0.0.1","title":"ICIKendallTau 0.0.1","text":"First release. ’s tested used rather thoroughly , ’d say ’s still rather alpha. Even though use time . Added NEWS.md file track changes package.","code":""}] diff --git a/docs/sitemap.xml b/docs/sitemap.xml index 7ef494a..03df9a5 100644 --- a/docs/sitemap.xml +++ b/docs/sitemap.xml @@ -27,6 +27,15 @@ /news/index.html + + /reference/cor_matrix_2_long_df.html + + + /reference/disable_logging.html + + + /reference/enable_logging.html + /reference/ici_kendalltau.html @@ -42,6 +51,15 @@ /reference/kt_fast.html + + /reference/log_memory.html + + + /reference/log_message.html + + + /reference/long_df_2_cor_matrix.html + /reference/pairwise_completeness.html @@ -51,4 +69,7 @@ /reference/ref_kendallt.html + + /reference/show_progress.html + diff --git a/vignettes/ici-kendalltau.Rmd b/vignettes/ici-kendalltau.Rmd index 1efd05e..e76d499 100644 --- a/vignettes/ici-kendalltau.Rmd +++ b/vignettes/ici-kendalltau.Rmd @@ -134,6 +134,29 @@ microbenchmark( all.equal(ici_kt(x, y, "global")[[1]], cor(x, y, method = "kendall")) ``` +## Running Many + +Just like R's `cor` function, we can also calculate correlations between many variables. +In contrast to R's function, `ici_kendalltau` calculates between rows of a matrix. + +Let's make some fake data and try it out. + +```{r} +#| label: matrix-example +set.seed(1234) + +s1 = sort(rnorm(1000, mean = 100, sd = 10)) +s2 = s1 + 10 +s2[sample(length(s1), 100)] = s1[1:100] +s3 = s1 +s3[c(1:10, sample(length(s1), 5))] = NA + +matrix_1 = cbind(s1, s2, s3) + +r_1 = ici_kendalltau(t(matrix_1)) +r_1$cor +``` + ## Parallelism If you have {future} and the {furrr} packages installed, then it is also possible to split up the a set of matrix comparisons across compute resources for any multiprocessing engine registered with {future}. @@ -143,7 +166,7 @@ If you have {future} and the {furrr} packages installed, then it is also possibl #| eval: false library(furrr) future::plan(multicore, workers = 4) -r_3 = ici_kendalltau(t(matrix_2)) +r_2 = ici_kendalltau(t(matrix_1)) ``` ## Many Many Comparisons @@ -156,6 +179,6 @@ To keep the `data.frame` output, add the argument `return_matrix=FALSE` to the c ```{r} #| label: matrix -r_4 = ici_kendalltau(t(matrix_2), return_matrix = FALSE) -r_4 +r_3 = ici_kendalltau(t(matrix_1), return_matrix = FALSE) +r_3$cor ```