nice {afex} | R Documentation

## Make nice ANOVA table for printing.

### Description

This generic function produces a nice ANOVA table for printin for objects of class. `nice_anova` takes an object from `Anova` possible created by the convenience functions `aov_ez` or `aov_car`. When within-subject factors are present, either sphericity corrected or uncorrected degrees of freedom can be reported.

### Usage

    nice(object, ...)

    ## S3 method for class 'afex_aov'
    nice(object, es = attr(object$anova_table, "es"),
      observed = attr(object$anova_table, "observed"),
      correction = attr(object$anova_table, "correction"), MSE = NULL,
      intercept = NULL, p_adjust_method = attr(object$anova_table,
      "p_adjust_method"), sig_symbols = attr(object$anova_table, "sig_symbols"),
      ...)

    ## S3 method for class 'anova'
    nice(object, MSE = NULL, intercept = NULL,
      sig_symbols = attr(object, "sig_symbols"), sig.symbols, ...)

    ## S3 method for class 'mixed'
    nice(object, sig_symbols = attr(object$anova_table,
      "sig_symbols"), ...)

    ## S3 method for class 'nice_table'
    print(x, ...)

### Arguments

`object, x` | An object of class `"afex_aov"` (see `aov_car`) or of class `"mixed"` (see `mixed`) as returned from the afex functions. Alternatively, an object of class `"Anova.mlm"` or `"anova"` as returned from `Anova`.
`...` | currently ignored.
`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). The default `NULL` corresponds to no adjustment.
`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")`.
`sig.symbols` | deprecated argument, only for backwards compatibility, use `"sig_symbols"` instead.

### Details

The returned `data.frame` is print-ready when adding to a document with proper methods. Either directly via knitr or similar approaches such as via packages ascii or xtable (nowadays knitr is probably the best approach, see here). ascii provides conversion to AsciiDoc and org-mode (see `ascii` and `print-ascii`). xtable converts a `data.frame` into LaTeX code with many possible options (e.g., allowing for `"longtable"` or `"sidewaystable"`), see `xtable` and `print.xtable`. See Examples.

Conversion functions to other formats (such as HTML, ODF, or Word) can be found at the Reproducible Research Task View.

The default reports generalized eta squared (Olejnik & Algina, 2003), the "recommended effect size for repeated measured designs" (Bakeman, 2005). Note that it is important that all measured variables (as opposed to experimentally manipulated variables), such as e.g., age, gender, weight, ..., must be declared via `observed` to obtain the correct effect size estimate. Partial eta squared (`"pes"`) does not require this.

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

A `data.frame` of class `nice_table` with the ANOVA table consisting of characters. The columns that are always present are: `Effect`, `df` (degrees of freedom), `F`, and `p`.

`ges` contains the generalized eta-squared effect size measure (Bakeman, 2005), `pes` contains partial eta-squared (if requested).

### Author(s)

The code for calculating generalized eta-squared was written by Mike Lawrence.
Everything else was written by Henrik Singmann.

### References

Bakeman, R. (2005). Recommended effect size statistics for repeated measures designs. Behavior Research Methods, 37(3), 379-384. doi:10.3758/BF03192707

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

Olejnik, S., & Algina, J. (2003). Generalized Eta and Omega Squared Statistics: Measures of Effect Size for Some Common Research Designs. Psychological Methods, 8(4), 434-447. doi:10.1037/1082-989X.8.4.434

### See Also

`aov_ez` and `aov_car` are the convenience functions to create the object appropriate for `nice_anova`.

### Examples

    ## example from Olejnik & Algina (2003)
    # "Repeated Measures Design" (pp. 439):
    data(md_12.1)
    # create object of class afex_aov:
    rmd <- aov_ez("id", "rt", md_12.1, within = c("angle", "noise"))
    rmd
    nice(rmd)
    str(nice(rmd))
    # use different es:
    nice(rmd, es = "pes") # noise: .82
    nice(rmd, es = "ges") # noise: .39

    # same data other approach:
    rmd2 <- aov_ez("id", "rt", md_12.1, within = c("angle", "noise"),
                   anova_table=list(correction = "none", es = "none"))
    nice(rmd2)
    nice(rmd2, correction = "GG")
    nice(rmd2, correction = "GG", es = "ges")

    # exampel using obk.long (see ?obk.long), a long version of the OBrienKaiser dataset from car.
    data(obk.long)
    # create object of class afex_aov:
    tmp.aov <- aov_car(value ~ treatment * gender + Error(id/phase*hour), data = obk.long)

    nice(tmp.aov, observed = "gender")

    nice(tmp.aov, observed = "gender", sig_symbols = rep("", 4))

    ## Not run:
    # use package ascii or xtable for formatting of tables ready for printing.

    full <- nice(tmp.aov, observed = "gender")

    require(ascii)
    print(ascii(full, include.rownames = FALSE, caption = "ANOVA 1"), type = "org")

    require(xtable)
    print.xtable(xtable(full, caption = "ANOVA 2"), include.rownames = FALSE)

    ## End(Not run)

[Package afex version 0.18-0 Index]