Description Usage Arguments Details Value Examples

This generic function calculates Eta coefficients which are also known as “Correlation ratios” or (the squared value) as the “Proportion of explained variance”.

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`object` |
the R object |

`...` |
arguments passed to other methods |

`fac` |
vector for conditioning variable |

The Eta coefficient (more specifically its squared value) has a interpretation in terms of the proportion of explained variance.

In the decision theory the interpretation is related to the identification
problem that involves two variables: *y* and *x*. The task is two
identify the values of `y`

.

The value of the *Eta^2* is the proportion by which the error of
predicting values of *y* is reduced by using the information contained
in *x*.

For numeric vectors the function requires additional argument: a
vector of the same length as the first. The result is a value of the
*Eta^2* assuming that we want to predict the values of
`object`

with the values of `fac`

using the so called “Type I
regression of means”.

For two variables *y* and *x* the *Eta* is given by the
formula:

*Eta^2 = ( D^2(y) - E[D^2(y|x)] ) / D^2(y)*

For objects of class `anova`

the function calculates the Eta's
and Partial Eta Squares for all effects in the given model. In this setting
the eta squares for the given effect are equal to:

*SSeffect / SStotal*

where *SS* are apropriate Sums of Squares. The “Partial Eta Squares”
for the given effect are equal to:

*SSeffect / (SSeffect+SSresid)*

For objects of class `lm`

the function is applied on the
result of calling `anova`

.

Values of eta and partial eta coefficients.

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mbojan/mbtools documentation built on Nov. 9, 2017, 3:21 p.m.

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