nice {afex}  R Documentation 
Make nice ANOVA table for printing.
Description
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 withinsubject 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$anova_table, "sig_symbols"),
round_ps = attr(object$anova_table, "round_ps"),
...
)
## S3 method for class 'anova'
nice(
object,
MSE = NULL,
intercept = NULL,
sig_symbols = attr(object, "sig_symbols"),
round_ps = attr(object, "round_ps"),
sig.symbols,
...
)
## S3 method for class 'mixed'
nice(
object,
sig_symbols = attr(object$anova_table, "sig_symbols"),
round_ps = attr(object$anova_table, "round_ps"),
...
)
## S3 method for class 'nice_table'
print(x, ...)
Arguments
object , x 
An object of class 
... 
currently ignored. 
es 
Effect Size to be reported. The default is given by

observed 
character vector referring to the observed (i.e., non
manipulated) variables/effects in the design. Important for calculation of
generalized etasquared (ignored if 
correction 
Character. Which sphericity correction of the degrees of
freedom should be reported for the withinsubject factors. The default is
given by 
MSE 
logical. Should the column containing the Mean Sqaured Error (MSE)
be displayed? Default is 
intercept 
logical. Should intercept (if present) be included in the
ANOVA table? Default is 
p_adjust_method 

sig_symbols 
Character. What should be the symbols designating
significance? When entering an vector with 
round_ps 
Function that should be used for rounding pvalues. The
default is given by 
sig.symbols 
deprecated argument, only for backwards compatibility, use

Details
The returned data.frame
is printready 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
pvalues 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 etasquared effect size measure
(Bakeman, 2005), pes
contains partial etasquared (if requested).
Author(s)
The code for calculating generalized etasquared 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), 379384. 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, 18. doi:10.3758/s1342301509135
Olejnik, S., & Algina, J. (2003). Generalized Eta and Omega Squared Statistics: Measures of Effect Size for Some Common Research Designs. Psychological Methods, 8(4), 434447. doi:10.1037/1082989X.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)