correlation: Correlation and measures of association

In rcompanion: Functions to Support Extension Education Program Evaluation

Correlation and measures of association

Description

Produces measures of association for all variables in a data frame with confidence intervals when available.

Usage

correlation(
  data = NULL,
  printClasses = FALSE,
  progress = TRUE,
  methodNum = "pearson",
  methodOrd = "kendall",
  methodNumOrd = "spearman",
  methodNumNom = "eta",
  methodNumBin = "pearson",
  testChisq = "chisq",
  ci = FALSE,
  conf = 0.95,
  R = 1000,
  correct = FALSE,
  reportIncomplete = TRUE,
  na.action = "na.omit",
  digits = 3,
  pDigits = 4,
  ...
)

Arguments

data	A data frame.
printClasses	If TRUE, prints a table of classes for all variables.
progress	If TRUE, prints progress bar when bootstrap methods are called.
methodNum	The method for the correlation for two numeric variables. The default is "pearson". Other options are "spearman" and "kendall".
methodOrd	The method for the correlation for two ordinal variables. The default is "kendall", with Kendall's tau-c used. Other option is "spearman".
methodNumOrd	The method for the correlation of a numeric and an ordinal variable. The default is "pearson". Other options are "spearman" and "kendall".
methodNumNom	The method for the correlation of a numeric and a nominal variable. The default is "eta", which is the square root of the r-squared value from anova. The other option is "epsilon", which is the same, except with the numeric value rank-transformed.
methodNumBin	The method for the correlation of a numeric and a binary variable. The default is "pearson". The other option is "glass", which uses the Glass rank biserial correlation.
testChisq	The method for the test of two nominal variables. The default is "chisq". The other option is "fisher".
ci	If TRUE, calculates confidence intervals for methods requiring bootstrap. If FALSE, will return only those confidence intervals from methods not requiring bootstrap.
conf	The confidence level for confidence intervals.
R	The number of replications to use for bootstrap confidence intervals for applicable methods.
correct	Passed to chisq.test.
reportIncomplete	If FALSE, NA will be reported in cases where there are instances of the calculation of the statistic failing during the bootstrap procedure.
na.action	If "na.omit", the function will use only complete cases, assessed on a bivariate basis. The other option is "na.pass".
digits	The number of decimal places in the output of most statistics.
pDigits	The number of decimal places in the output for p-values.
...	Other arguments.

Details

It’s important that variables are assigned the correct class to get an appropriate measure of association. That is, factor variables should be of class "factor", not "character". Ordered factors should be ordered factors (and have their levels in the correct order!).

Date variables are treated as numeric.

The default for measures of association tend to be "parametric" type. That is, e.g. Pearson correlation where appropriate.

Nonparametric measures of association will be reported with the options methodNum = "spearman", methodNumNom = "epsilon", methodNumBin = "glass".

Value

A data frame of variables, association statistics, p-values, and confidence intervals.

Author(s)

Salvatore Mangiafico, [e-mail]

References

https://rcompanion.org/handbook/I_14.html

See Also

phi, spearmanRho, cramerV, freemanTheta, wilcoxonRG

Examples

Length   = c(0.29, 0.25, NA, 0.40, 0.50, 0.57, 0.62, 0.88, 0.99, 0.90)
Rating   = factor(ordered=TRUE, levels=c("Low", "Medium", "High"),
                  x = rep(c("Low", "Medium", "High"), c(3,3,4)))
Color    = factor(rep(c("Red", "Green", "Blue"), c(4,4,2)))
Flag     = factor(rep(c(TRUE, FALSE, TRUE), c(5,4,1)))
Answer   = factor(rep(c("Yes", "No", "Yes"), c(4,3,3)), levels=c("Yes", "No"))
Location = factor(rep(c("Home", "Away", "Other"), c(2,4,4)))
Distance = factor(ordered=TRUE, levels=c("Low", "Medium", "High"),
                  x = rep(c("Low", "Medium", "High"), c(5,2,3))) 
Start    = seq(as.Date("2024-01-01"), by = "month", length.out = 10)
Data = data.frame(Length, Rating, Color, Flag, Answer, Location, Distance, Start)  
correlation(Data)

rcompanion documentation built on April 3, 2025, 11:55 p.m.