phi | R Documentation |
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,
...
)
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 |
ci |
If |
conf |
The level for the confidence interval. |
type |
The type of confidence interval to use.
Can be any of " |
R |
The number of replications to use for bootstrap. |
histogram |
If |
verbose |
If |
digits |
The number of significant digits in the output. |
reportIncomplete |
If |
... |
Additional arguments. (Ignored.) |
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.
A single statistic, phi. Or a small data frame consisting of phi, and the lower and upper confidence limits.
Salvatore Mangiafico, mangiafico@njaes.rutgers.edu
https://rcompanion.org/handbook/H_10.html
cramerV
### Example with table
Matrix = matrix(c(13, 26, 26, 13), ncol=2)
phi(Matrix)
### Example with two vectors
Species = 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)
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