These objects contain a number of settings and functions for associationMatrix.

```
computeStatistic_t(var1, var2, conf.level=.95, var.equal='test', ...)
computeStatistic_r(var1, var2, conf.level=.95, ...)
computeStatistic_f(var1, var2, conf.level=.95, ...)
computeStatistic_chisq(var1, var2, conf.level=.95, ...)
```computeEffectSize_d(var1, var2, conf.level=.95, var.equal='test', ...)
computeEffectSize_r(var1, var2, conf.level=.95, ...)
computeEffectSize_etasq(var1, var2, conf.level=.95, ...)
computeEffectSize_omegasq(var1, var2, conf.level=.95, ...)
computeEffectSize_v(var1, var2, conf.level=.95,
bootstrap=FALSE, samples=5000, ...)

var1

One of the two variables for which to compute a statistic or effect size

var2

The other variable for which to compute the statistic or effect size

conf.level

The confidence for the confidence interval for the effect size

bootstrap

Whether to bootstrap to estimate the confidence interval for Cramer's V. If FALSE, the Fisher's Z conversion is used.

samples

If bootstrapping, the number of samples to generate (of course, more samples means more accuracy and longer processing time).

var.equal

Whether to test for equal variances ('test'), assume equality ('yes'), or assume unequality ('no'). See `meanDiff`

for more information.

…

Any additonal arguments are sometimes used to specify exactly how statistics and effect sizes should be computed.

associationMatrixStatDefaults and associationMatrixESDefaults contain the default functions from computeStatistic and computeEffectSize that are called (see the help file for associationMatrix for more details).

The other functions return an object with the relevant statistic or effect size, with a confidence interval for the effect size.

For computeStatistic, this object always contains:

The relevant statistic

The type of statistic

The degrees of freedom for this statistic

The p-value of this statistic for NHST

The object from which the statistics are extracted

The point estimate for the effect size

The type of effect size

The confidence interval for the effect size

The object from which the effect size is extracted

# NOT RUN { computeStatistic_f(Orange$Tree, Orange$circumference) computeEffectSize_etasq(Orange$Tree, Orange$circumference) # }