confIntV: crossTab, confIntV and cramersV
In userfriendlyscience: Quantitative Analysis Made Accessible

Description Usage Arguments Value Examples

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

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)

`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`.

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.

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));