# confIntV

##### crossTab, confIntV and cramersV

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

- Keywords
- bivar

##### 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));
# }
```

*Documentation reproduced from package userfriendlyscience, version 0.7.2, License: GPL (>= 3)*