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Anova (from the car::Anova. aov.car can be called using a
formula similar to aov specifying an error
strata for the within-subject factor(s). ez.glm is
called specifying the factors as character vectors.aov.car(formula, data, fun.aggregate = NULL, type = 3,
factorize = TRUE, check.contrasts = TRUE, return =
"nice", observed = NULL, args.return = list(), ...)
ez.glm(id, dv, data, between = NULL, within = NULL,
covariate = NULL, observed = NULL, fun.aggregate =
NULL, type = 3, factorize = TRUE, check.contrasts =
TRUE, return = "nice", args.return = list(), ...,
print.formula = FALSE)
univ(object)aov. Should include an error
term (i.e., Error( / )). Note that the
within-subject factors do not need to be outside the
Error term (thischaracter vector (of length 1)
indicating the subject identifier column in data.character vector (of length 1)
indicating the column containing the dependent
variable in data.character vector indicating the
between-subject(s) factor(s)/column(s) in
data. Default is NULL indicating no
between-subjects factors.character vector indicating the
within-subject(s) factor(s)/column(s) in
data. Default is NULL indicating no
within-subjects factors.character vector indicating the
between-subject(s) covariate(s) (i.e., column(s)) in
data. Default is NULL indicating no
covariates.character vector indicating which
of the variables are observed (i.e, measured) as compared
to experimentally manipulated. The default behavior is to
return a ANOVA table with generalized eta squared effect
size for which this infdata.frame containing the data.
Mandatory.NULL issues a warning if aggregation is
necessary and uses Anova. Possible values are
"II", "III", 2, or 3.TRUE. If one wants to run an ANCOVA,
needs to be set to FALSE (in which case centering
on 0 is checked on numerlogical. Should contrasts
for between-subject factors be checked and (if necessary)
changed to be "contr.sum". See details.ez.glm is a wrapper for
aov.car. This boolean argument indicates whether
the formula in the call to car.aov should be
printed."nice"
(the default) will return a nice ANOVA table (produced by
nice.anova. Possible values are
c("Anova", "lm", "data", "nice", "full", "all",
list of further arguments
passed to the function which produces the return value.
Currently only supports return = "nice" (the
default) which then passes arguments to
nice.anovfun.aggregate.Anova.mlm as
returned by aov.car, ez.glm, or
Anova.aov.car and ez.glm are wrappers to
Anova, the return value is dependent
on the return argument. When argument
return is "nice" (the default) a nice ANOVA
table is returnd (nice.anova) with the
following columns: Effect, df, MSE
(mean-squared errors), F (potentially with
significant symbols), ges (generalized
eta-squared), p.
If return = "full" or return = "all" a list
list with the following elements:
[object Object],[object Object],[object Object],[object Object]
If return = "univariate" the object returned from
univ.
univ returns a list of data.frames
containing the univariate results (i.e., the classical
ANOVA results) from an object of class
"Anova.mlm". This is essentially the output from
summary.Anova.mlm with multivariate =
FALSE, e.g. summary(aov.car(...), multivriate =
FALSE), as a list instead of printed to the console.
For objects of class "anova" (i.e., the object
returned by car::Anova for a purely
between-subjects ANOVA) the object is returned unaltered.
The elements of the list returned by univ are:
anova, mauchly, and
spehricity.correction (containing both,
Greenhouse-Geisser and Hyundt-Feldt correction).
If return = "lme4" The data (possibly aggregated
to have one observation per cell) is fitted with
lme4::lmer using all within-subjects factors as
fully crossed random slopes and the obtained mer
object returned for further analysis. (This behavhior is
rather experimental!)contr.sum via
options(contrasts=c('contr.sum','contr.poly')).
This should be done automatically when loading options("contrasts").
The formula for aov.car must contain a
single Error term specyfying the ID column
and potential within-subject factors (you may use
mixed with multiple error terms). Factors
outside the Error term are treated as
between-subject factors (the within-subject factors
specified in the Error term are ignored outside
the Error term, i.e., it is not necessary to
specify them outside the Error term, see
Examples).
Suppressing the intercept (i.e, via 0 + or -
1) is ignored. Specific specifications of effects (e.g.,
excluding terms with - or using ^) could be
okay but is not tested. Using the I or
poly function within the formula is not
tested and not supported!
For ez.glm either between or within
must not be NULL.
ez.glm will concatante all between-subject factors
using * (i.e., producing all main effects and
interactions) and all covariates by + (i.e.,
adding only the main effects to the existing
between-subject factors). The within-subject factors do
fully interact with all between-subject factors and
covariates. This is essentially identical to the behavior
of SPSS's glm function.
To run an ANCOVA you need to set factorize = FALSE
and make sure that all variables have the correct type
(i.e., factors are factors and numeric variables are
numeric and centered).
Note that the default behavior is to return a
nice.anova data.frame. This includes
calculation of generalized eta squared for which
all non manipluated (i.e., observed) variables
need to be specified via the observed argument.
Changing the effect size to "pes" (partial
eta-squared) via args.return or the return value
via return removes this necessity.
If check.contrasts = TRUE, contrasts will be set
to "contr.sum" for all between-subject factors if
default contrasts are not equal to "contr.sum" or
attrib(factor, "contrasts") != "contr.sum".
(within-subject factors are hard-coded
"contr.sum".)nice.anova creats the nice ANOVA tables
which are by default returned. See also there for a
slightly longer discussion of effect sizes.
mixed provides a (formula) interface for
obtaining p-values for mixed-models via obk.long describes the long version of the
OBrienKaiser dataset used in the examples.# exampel using obk.long (see ?obk.long), a long version of the OBrienKaiser dataset from car.
data(obk.long, package = "afex")
# run univariate mixed ANOVA for the full design:
aov.car(value ~ treatment * gender + Error(id/phase*hour),
data = obk.long, observed = "gender")
ez.glm("id", "value", obk.long, between = c("treatment", "gender"),
within = c("phase", "hour"), observed = "gender")
# both calls return the same:
## Effect df MSE F ges p
## 1 treatment 2, 10 22.81 3.94 + .20 .05
## 2 gender 1, 10 22.81 3.66 + .11 .08
## 3 treatment:gender 2, 10 22.81 2.86 .18 .10
## 4 phase 1.60, 15.99 5.02 16.13 *** .15 <.001
## 5 treatment:phase 3.20, 15.99 5.02 4.85 * .10 .01
## 6 gender:phase 1.60, 15.99 5.02 0.28 .003 .71
## 7 treatment:gender:phase 3.20, 15.99 5.02 0.64 .01 .61
## 8 hour 1.84, 18.41 3.39 16.69 *** .13 <.001
## 9 treatment:hour 3.68, 18.41 3.39 0.09 .002 .98
## 10 gender:hour 1.84, 18.41 3.39 0.45 .004 .63
## 11 treatment:gender:hour 3.68, 18.41 3.39 0.62 .01 .64
## 12 phase:hour 3.60, 35.96 2.67 1.18 .02 .33
## 13 treatment:phase:hour 7.19, 35.96 2.67 0.35 .009 .93
## 14 gender:phase:hour 3.60, 35.96 2.67 0.93 .01 .45
## 15 treatment:gender:phase:hour 7.19, 35.96 2.67 0.74 .02 .65
# replicating ?Anova using aov.car:
aov.car(value ~ treatment * gender + Error(id/phase*hour),
data = obk.long, type = 2, return = "Anova")
# in contrast to aov you do not need the within-subject factors outside Error()
# replicating ?Anova using ez.glm:
ez.glm("id", "value", obk.long, c("treatment", "gender"),
c("phase", "hour"), type = 2, return = "Anova")
#both return:
## Type II Repeated Measures MANOVA Tests: Pillai test statistic
## Df test stat approx F num Df den Df Pr(>F)
## (Intercept) 1 0.970 318 1 10 0.0000000065 ***
## treatment 2 0.481 5 2 10 0.03769 *
## gender 1 0.204 3 1 10 0.14097
## treatment:gender 2 0.364 3 2 10 0.10447
## phase 1 0.851 26 2 9 0.00019 ***
## treatment:phase 2 0.685 3 4 20 0.06674 .
## gender:phase 1 0.043 0 2 9 0.82000
## treatment:gender:phase 2 0.311 1 4 20 0.47215
## hour 1 0.935 25 4 7 0.00030 ***
## treatment:hour 2 0.301 0 8 16 0.92952
## gender:hour 1 0.293 1 4 7 0.60237
## treatment:gender:hour 2 0.570 1 8 16 0.61319
## phase:hour 1 0.550 0 8 3 0.83245
## treatment:phase:hour 2 0.664 0 16 8 0.99144
## gender:phase:hour 1 0.695 1 8 3 0.62021
## treatment:gender:phase:hour 2 0.793 0 16 8 0.97237
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# ANCOVA: adding a covariate (necessary to set factorize = FALSE)
aov.car(value ~ treatment * gender + age + Error(id/phase*hour),
data = obk.long, observed = c("gender", "age"), factorize = FALSE)
ez.glm("id", "value", obk.long, between = c("treatment", "gender"),
within = c("phase", "hour"), covariate = "age",
observed = c("gender", "age"), factorize = FALSE)
# aggregating over one within-subjects factor (phase) with warning:
aov.car(value ~ treatment * gender + Error(id/hour), data = obk.long, observed = "gender")
ez.glm("id", "value", obk.long, c("treatment", "gender"), "hour", observed = "gender")
# runs with "numeric" factors
obk.long$hour2 <- as.numeric(as.character(obk.long$hour))
aov.car(value ~ treatment * gender + Error(id/hour2),
data = obk.long, type = 2,observed = c("gender"))
# only between
aov.car(value ~ treatment * gender + Error(id),
data = obk.long, type = 2,observed = c("gender"))
aov.car(value ~ treatment * gender + Error(id),
data = obk.long, type = 2, observed = c("gender"))
ez.glm("id", "value", obk.long, c("treatment", "gender"),
within = NULL, type = 2, print.formula = TRUE, observed = "gender")
# only within
aov.car(value ~ Error(id/phase*hour), data = obk.long, type = 2)
ez.glm("id", "value", obk.long, NULL, c("phase", "hour"),
type = 2, print.formula = TRUE)
# using return = "full":
str(aov.car(value ~ Error(id/phase*hour), data = obk.long, return = "full"), 1)
## List of 4
## $ Anova:List of 14
## ..- attr(*, "class")= chr "Anova.mlm"
## $ lm :List of 11
## ..- attr(*, "class")= chr [1:2] "mlm" "lm"
## $ data :'data.frame': 16 obs. of 16 variables:
## $ idata:'data.frame': 15 obs. of 2 variables:
# use args.return arguments:
aov.car(value ~ treatment * gender + Error(id/phase*hour),
data = obk.long, args.return = list(correction = "none", es = "pes"))
aov.car(value ~ treatment * gender + Error(id/phase*hour),
data = obk.long,observed = "gender",
args.return = list(correction = "none", MSE = FALSE))Run the code above in your browser using DataLab