Description Usage Arguments Details Value Author(s) References See Also Examples
Calculates partial eta-squared for linear models or multivariate analogs of eta-squared (or R^2), indicating the partial association for each term in a multivariate linear model. There is a different analog for each of the four standard multivariate test statistics: Pillai's trace, Hotelling-Lawley trace, Wilks' Lambda and Roy's maximum root test.
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x |
A |
anova |
A logical, indicating whether the result should also contain the
test statistics produced by |
partial |
A logical, indicating whether to calculate partial or classical eta^2. |
... |
Other arguments passed down to |
For univariate linear models, classical η^2 = SSH / SST and partial η^2 = SSH / (SSH + SSE). These are identical in one-way designs.
Partial eta-squared describes the proportion of total variation attributable to a given factor, partialling out (excluding) other factors from the total nonerror variation. These are commonly used as measures of effect size or measures of (non-linear) strength of association in ANOVA models.
All multivariate tests are based on the s=min(p, df_h) latent roots of H E^{-1}. The analogous multivariate partial η^2 measures are calculated as:
η^2 = V/s
η^2 = T/(T+s)
η^2 = L^{1/s}
η^2 = R/(R+1)
When anova=FALSE
, a one-column data frame containing the
eta-squared values for each term in the model.
When anova=TRUE
, a 5-column (lm) or 7-column (mlm) data frame containing the
eta-squared values and the test statistics produced by print.Anova()
for each term in the model.
Michael Friendly
Muller, K. E. and Peterson, B. L. (1984). Practical methods for computing power in testing the Multivariate General Linear Hypothesis Computational Statistics and Data Analysis, 2, 143-158.
Muller, K. E. and LaVange, L. M. and Ramey, S. L. and Ramey, C. T. (1992). Power Claculations for General Linear Multivariate Models Including Repeated Measures Applications. Journal of the American Statistical Association, 87, 1209-1226.
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Loading required package: car
eta^2
Block 0.5585973
Contour 0.6692989
Depth 0.5983772
Contour:Depth 0.2058495
eta^2
Block 0.5585973
Contour 0.6692989
Depth 0.5983772
Contour:Depth 0.2058495
Type II MANOVA Tests: Pillai test statistic
eta^2 Df test stat approx F num Df den Df Pr(>F)
Block 0.55860 3 1.6758 3.7965 27 81 1.777e-06 ***
Contour 0.66930 2 1.3386 5.8468 18 52 2.730e-07 ***
Depth 0.59838 3 1.7951 4.4697 27 81 8.777e-08 ***
Contour:Depth 0.20585 6 1.2351 0.8640 54 180 0.7311
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
eta^2
Block 0.5701385
Contour 0.7434504
Depth 0.8294239
Contour:Depth 0.2250388
eta^2
Block 0.5823516
Contour 0.8009753
Depth 0.9421533
Contour:Depth 0.2456774
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