Calculates phi for a 2 x 2 table of nominal variables; confidence intervals by bootstrap.
phi( x, y =NULL, ci =FALSE, conf =0.95, type ="perc", R =1000, histogram =FALSE, verbose =FALSE, digits =3, reportIncomplete =FALSE,...)
Arguments
x: Either a 2 x 2 table or a 2 x 2 matrix. Can also be a vector of observations for one dimension of a 2 x 2 table.
y: If x is a vector, y is the vector of observations for the second dimension of a 2 x2 table.
ci: If TRUE, returns confidence intervals by bootstrap. May be slow.
conf: The level for the confidence interval.
type: The type of confidence interval to use. Can be any of "norm", "basic", "perc", or "bca". Passed to boot.ci.
R: The number of replications to use for bootstrap.
histogram: If TRUE, produces a histogram of bootstrapped values.
verbose: If TRUE, prints the table of counts.
digits: The number of significant digits in the output.
reportIncomplete: If FALSE (the default), NA will be reported in cases where there are instances of the calculation of the statistic failing during the bootstrap procedure.
...: Additional arguments. (Ignored.)
Returns
A single statistic, phi. Or a small data frame consisting of phi, and the lower and upper confidence limits.
Details
phi is used as a measure of association between two binomial variables, or as an effect size for a chi-square test of association for a 2 x 2 table. The absolute value of the phi statistic is the same as Cramer's V for a 2 x 2 table.
Unlike Cramer's V, phi can be positive or negative (or zero), and ranges from -1 to 1.
When phi is close to its extremes, or with small counts, the confidence intervals determined by this method may not be reliable, or the procedure may fail.
Examples
### Example with tableMatrix = matrix(c(13,26,26,13), ncol=2)phi(Matrix)### Example with two vectorsSpecies = c(rep("Species1",16), rep("Species2",16))Color = c(rep(c("blue","blue","blue","green"),4), rep(c("green","green","green","blue"),4))phi(Species, Color)