cramerVFit {rcompanion} | R Documentation |
Cramer's V for chi-square goodness-of-fit tests
Description
Calculates Cramer's V for a vector of counts and expected counts; confidence intervals by bootstrap.
Usage
cramerVFit(
x,
p = rep(1/length(x), length(x)),
ci = FALSE,
conf = 0.95,
type = "perc",
R = 1000,
histogram = FALSE,
digits = 4,
reportIncomplete = FALSE,
verbose = FALSE,
...
)
Arguments
x |
A vector of observed counts. |
p |
A vector of expected or default probabilities. |
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 |
verbose |
If |
... |
Additional arguments passed to |
Details
This modification of Cramer's V could be used to indicate an effect size in cases where a chi-square goodness-of-fit test might be used. It indicates the degree of deviation of observed counts from the expected probabilities.
In the case of equally-distributed expected frequencies, Cramer's V will be equal to 1 when all counts are in one category, and it will be equal to 0 when the counts are equally distributed across categories. This does not hold if the expected frequencies are not equally-distributed.
Because V is always positive,
if type="perc"
,
the confidence interval will
never cross zero, and should not
be used for statistical inference.
However, if type="norm"
, the confidence interval
may cross zero.
When V is close to 0 or 1, or with small counts, the confidence intervals determined by this method may not be reliable, or the procedure may fail.
In addition, the function will not return a confidence interval if there are zeros in any cell.
Value
A single statistic, Cramer's V. Or a small data frame consisting of Cramer's V, and the lower and upper confidence limits.
Author(s)
Salvatore Mangiafico, mangiafico@njaes.rutgers.edu
References
https://rcompanion.org/handbook/H_03.html
See Also
Examples
### Equal probabilities example
### From https://rcompanion.org/handbook/H_03.html
nail.color = c("Red", "None", "White", "Green", "Purple", "Blue")
observed = c( 19, 3, 1, 1, 2, 2 )
expected = c( 1/6, 1/6, 1/6, 1/6, 1/6, 1/6 )
chisq.test(x = observed, p = expected)
cramerVFit(x = observed, p = expected)
### Unequal probabilities example
### From https://rcompanion.org/handbook/H_03.html
race = c("White", "Black", "American Indian", "Asian", "Pacific Islander",
"Two or more races")
observed = c(20, 9, 9, 1, 1, 1)
expected = c(0.775, 0.132, 0.012, 0.054, 0.002, 0.025)
chisq.test(x = observed, p = expected)
cramerVFit(x = observed, p = expected)
### Examples of perfect and zero fits
cramerVFit(c(100, 0, 0, 0, 0))
cramerVFit(c(10, 10, 10, 10, 10))