userfriendlyscience (version 0.5-2)

associationMatrix Helper Functions: associationMatrix Helper Functions

Description

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

Usage

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, ...)

Arguments

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.

Value

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:And in addition, it often contains (among other things, sometimes):For computeEffectSize, this object always contains:And in addition, it often contains (among other things, sometimes):

See Also

meanDiff, associationMatrix

Examples

Run this code

computeStatistic_f(Orange$Tree, Orange$circumference)
computeEffectSize_etasq(Orange$Tree, Orange$circumference)

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