afex_aov-methods: Methods for afex_aov objects

Description

Methods defined for objects returned from the ANOVA functions aov_car et al. of class afex_aov containing both the ANOVA fitted via car::Anova and base R's aov.

Usage

## S3 method for class 'afex_aov':
anova(object, es = afex_options("es_aov"),
  observed = NULL, correction = afex_options("correction_aov"),
  MSE = TRUE, intercept = FALSE, p.adjust.method = NULL, ...)
## S3 method for class 'afex_aov':
print(x, ...)
## S3 method for class 'afex_aov':
summary(object, ...)
## S3 method for class 'afex_aov':
recover.data(object, ...)
## S3 method for class 'afex_aov':
lsm.basis(object, trms, xlev, grid, ...)

Arguments

object,x

object of class afex_aov as returned from aov_car and related functions.

Effect Size to be reported. The default is given by afex_options("es_aov"), which is initially set to "ges" (i.e., reporting generalized eta-squared, see details). Also supported is partial eta-squared ("pes") or

observed

character vector referring to the observed (i.e., non manipulated) variables/effects in the design. Important for calculation of generalized eta-squared (ignored if es is not "ges"), see details.

correction

Character. Which sphericity correction of the degrees of freedom should be reported for the within-subject factors. The default is given by afex_options("correction_aov"), which is initially set to "GG" corresponding to the Gree

MSE

logical. Should the column containing the Mean Sqaured Error (MSE) be displayed? Default is TRUE.

intercept

logical. Should intercept (if present) be included in the ANOVA table? Default is FALSE which hides the intercept.

p.adjust.method

character indicating if p-values for individual effects should be adjusted for multiple comparisons (see p.adjust and details).

...

further arguments passed through, see description of return value for details.

trms,xlev,grid

same as for lsm.basis.

Value

[object Object],[object Object],[object Object],[object Object]

Details

Exploratory ANOVA, for which no detailed hypotheses have been specified a priori, harbor a multiple comparison problem (Cramer et al., 2015). To avoid an inflation of familywise Type I error rate, results need to be corrected for multiple comparisons using p.adjust.method. p.adjust.method defaults to the method specified in the call to aov_car in anova_table. If no method was specified and p.adjust.method = NULL p-values are not adjusted.

References

Cramer, A. O. J., van Ravenzwaaij, D., Matzke, D., Steingroever, H., Wetzels, R., Grasman, R. P. P. P., ... Wagenmakers, E.-J. (2015). Hidden multiplicity in exploratory multiway ANOVA: Prevalence and remedies. Psychonomic Bulletin & Review, 1–8. doi:http://doi.org/10.3758/s13423-015-0913-5{10.3758/s13423-015-0913-5}