# confIntV: crossTab, confIntV and cramersV

## Description

These functions compute the point estimate and confidence interval for
Cramer's V. The crossTab function also shows a crosstable.

## Usage

crossTab(x, y=NULL, conf.level=.95,
digits=2, pValueDigits=3, ...)
cramersV(x, y = NULL, digits=2)
confIntV(x, y = NULL, conf.level=.95,
samples = 500, digits=2,
method=c('bootstrap', 'fisher'),
storeBootstrappingData = FALSE)

## Arguments

x

Either a crosstable to analyse, or one of two vectors to use to generate
that crosstable. The vector should be a factor, i.e. a categorical
variable identified as such by the 'factor' class).

y

If x is a crosstable, y can (and should) be empty. If x is a vector, y
must also be a vector.

digits

Minimum number of digits after the decimal point to show in the result.

pValueDigits

Minimum number of digits after the decimal point to show in the Chi
Square p value in the result.

conf.level

Level of confidence for the confidence interval.

samples

Number of samples to generate when bootstrapping.

method

Whether to use Fisher's Z or bootstrapping to compute the confidence
interval.

storeBootstrappingData

Whether to store (or discard) the data generating during the bootstrapping
procedure.

...

Extra arguments to `crossTab`

are passed on to `confIntV`

.

## Value

The cramersV and confIntV functions return either a point estimate or
a confidence interval for Cramer's V, an effect size to describe the
association between two categorical variables. The crossTab function is
just a wrapper around confIntV.

## Examples

# NOT RUN {
crossTab(infert$education, infert$induced, samples=50);
### Get confidence interval for Cramer's V
### Note that by using 'table', and so removing the raw data, inhibits
### bootstrapping, which could otherwise take a while.
confIntV(table(infert$education, infert$induced));
# }