afex_aov-methods {afex} | R Documentation

## 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,
      sig_symbols = attr(object$anova_table, "sig_symbols"), ...)

    ## 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.
`es` | 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 `"none"`.
`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 Greenhouse-Geisser correction. Possible values are `"GG"`, `"HF"` (i.e., Hyunh-Feldt correction), and `"none"` (i.e., no correction).
`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).
`sig_symbols` | Character. What should be the symbols designating significance? When entering an vector with `length(sig.symbol) < 4` only those elements of the default (`c(" +", " *", " **", " ***")`) will be replaced. `sig_symbols = ""` will display the stars but not the `+`, `sig_symbols = rep("", 4)` will display no symbols. The default is given by `afex_options("sig_symbols")`.
`...` | further arguments passed through, see description of return value for details.
`trms, xlev, grid` | same as for `lsm.basis`.

### 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.

### Value

`anova`

  Returns an ANOVA table of class `c("anova", "data.frame")`. Information such as effect size (`es`) or df-correction are calculated each time this method is called.

`summary`

  For ANOVAs containing within-subject factors it returns the full output of the within-subject tests: the uncorrected results, results containing Greenhousse-Geisser and Hyunh-Feldt correction, and the results of the Mauchly test of sphericity (all achieved via `summary.Anova.mlm`). For other ANOVAs, the `anova` table is simply returned.

`print`

  Prints (and invisibly returns) the ANOVA table as constructed from `nice` (i.e., as strings rounded nicely). Arguments in `...` are passed to `nice` allowing to pass arguments such as `es` and `correction`.

`recover.data` and `lsm.basis`

  Provide the backbone for using `lsmeans` and related functions from lsmeans directly on `afex_aov` objects by returning a `ref.grid` object. Should not be called directly but through the functionality provided by lsmeans.

### 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:10.3758/s13423-015-0913-5

[Package afex version 0.18-0 Index]