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DescTools (version 0.99.37)

Assocs: Association Measures

Description

Collects a number of association measures for nominal and ordinal data.

Usage

Assocs(x, conf.level = 0.95, verbose = NULL)

# S3 method for Assocs print(x, digits = 4, ...)

Arguments

x

a 2 dimensional contingency table or a matrix.

conf.level

confidence level of the interval. If set to NA no confidence interval will be calculated. Default is 0.95.

verbose

integer out of c(2, 1, 3) defining the verbosity of the reported results. 2 (default) means medium, 1 less and 3 extensive results. Applies only to tables and is ignored else.

digits

integer which determines the number of digits used in formatting the measures of association.

further arguments to be passed to or from methods.

Value

numeric matrix, dimension [1:17, 1:3] the first column contains the estimate, the second the lower confidence interval, the third the upper one.

Details

This function wraps the association measures phi, contingency coefficient, Cramer's V, Goodman Kruskal's Gamma, Kendall's Tau-b, Stuart's Tau-c, Somers' Delta, Pearson and Spearman correlation, Guttman's Lambda, Theil's Uncertainty Coefficient and the mutual information.

See Also

Phi, ContCoef, CramerV, GoodmanKruskalGamma, KendallTauB, StuartTauC, SomersDelta, SpearmanRho, Lambda, UncertCoef, MutInf

Examples

Run this code
# NOT RUN {
options(scipen=8)

# Example taken from: SAS/STAT(R) 9.2 User's Guide, Second Edition, The FREQ Procedure
# http://support.sas.com/documentation/cdl/en/statugfreq/63124/PDF/default/statugfreq.pdf
# Hair-Eye-Color pp. 1816

tob <- as.table(matrix(c(
  69, 28, 68, 51,  6,
  69, 38, 55, 37,  0,
  90, 47, 94, 94, 16
), nrow=3, byrow=TRUE,
   dimnames=list(eye=c("blue","green","brown"),
                 hair=c("fair","red","medium","dark","black")) ))
Desc(tob)
Assocs(tob)

# Example taken from: http://www.math.wpi.edu/saspdf/stat/chap28.pdf
# pp. 1349

pain <- as.table(matrix(c(
   26,  6,
   26,  7,
   23,  9,
   18, 14,
    9, 23
   ), ncol=2, byrow=TRUE))

Desc(pain)
Assocs(pain)
# }

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