cohenW | R Documentation |
Calculates Cohen's w for a table of nominal variables.
cohenW(
x,
y = NULL,
p = NULL,
ci = FALSE,
conf = 0.95,
type = "perc",
R = 1000,
histogram = FALSE,
digits = 4,
reportIncomplete = FALSE,
...
)
x |
Either a two-way table or a two-way matrix. Can also be a vector of observations for one dimension of a two-way table. |
y |
If |
p |
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 |
digits |
The number of significant digits in the output. |
reportIncomplete |
If |
... |
Additional arguments passed to |
Cohen's w is used as a measure of association between two nominal 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 Cohen's w. The value of Cohen's w is not bound by 1 on the upper end.
Cohen's w is "naturally nondirectional". That is,
the value will always be zero or positive.
Because of this, if type="perc"
,
the confidence interval will
never cross zero.
The confidence interval range should not
be used for statistical inference.
However, if type="norm"
, the confidence interval
may cross zero.
When w is close to 0 or very large, or with small counts, the confidence intervals determined by this method may not be reliable, or the procedure may fail.
A single statistic, Cohen's w. Or a small data frame consisting of Cohen's w, and the lower and upper confidence limits.
Salvatore Mangiafico, mangiafico@njaes.rutgers.edu
https://rcompanion.org/handbook/H_10.html
Cohen J. 1992. "A Power Primer". Psychological Bulletin 12(1): 155-159.
Cohen, J. 1988. Statistical Power Analysis for the Behavioral Sciences, 2nd Ed. Routledge.
cramerV
### Example with table
data(Anderson)
fisher.test(Anderson)
cohenW(Anderson)
### Example for goodness-of-fit
### Bird foraging example, Handbook of Biological Statistics
observed = c(70, 79, 3, 4)
expected = c(0.54, 0.40, 0.05, 0.01)
chisq.test(observed, p = expected)
cohenW(observed, p = expected)
### 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))
fisher.test(Species, Color)
cohenW(Species, Color)
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