This generic function produces a nice ANOVA table for printing 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.
nice(object, ...)# S3 method for 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$anova_table, "sig_symbols"),
...
)
# S3 method for anova
nice(
object,
MSE = NULL,
intercept = NULL,
sig_symbols = attr(object, "sig_symbols"),
sig.symbols,
...
)
# S3 method for mixed
nice(object, sig_symbols = attr(object$anova_table, "sig_symbols"), ...)
# S3 method for nice_table
print(x, ...)
currently ignored.
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".
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.
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).
logical. Should the column containing the Mean Sqaured Error (MSE)
be displayed? Default is TRUE.
logical. Should intercept (if present) be included in the
ANOVA table? Default is FALSE which hides the intercept.
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.
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").
deprecated argument, only for backwards compatibility, use
"sig_symbols" instead.
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).
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 package xtable (nowadays knitr is
probably the best approach, see here).
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.
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
aov_ez and aov_car are the convenience
functions to create the object appropriate for nice_anova.
# NOT RUN {
## 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)
# }
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