aov_car: Convenient ANOVA estimation for factorial designs

Description

These functions allow convenient specification of any type of ANOVAs (i.e., purely within-subjects ANOVAs, purely between-subjects ANOVAs, and mixed between-within or split-plot ANOVAs) for data in the long format (i.e., one observation per row). If the data has more than one observation per individual and cell of the design (e.g., multiple responses per condition), the data will by automatically aggregated. The default settings reproduce results from commercial statistical packages such as SPSS or SAS. aov_ez is called specifying the factors as character vectors, aov_car is called using a formula similar to aov specifying an error strata for the within-subject factor(s), and aov_4 is called with a lme4-like formula (all ANOVA functions return identical results). The returned object contains the ANOVA also fitted via base R's aov which can be passed to e.g., lsmeans for further analysis (e.g., follow-up tests, contrasts, plotting, etc.). These functions employ Anova (from the car package) to provide test of effects avoiding the somewhat unhandy format of car::Anova.

Usage

aov_ez(id, dv, data, between = NULL, within = NULL, covariate = NULL,
     observed = NULL, fun.aggregate = NULL, type = afex_options("type"),
     factorize = afex_options("factorize"),
     check.contrasts = afex_options("check.contrasts"),
     return = afex_options("return_aov"),
     anova_table = list(), ..., print.formula = FALSE)
aov_car(formula, data, fun.aggregate = NULL, type = afex_options("type"),
     factorize = afex_options("factorize"),
     check.contrasts = afex_options("check.contrasts"),
     return = afex_options("return_aov"), observed = NULL,
     anova_table = list(), ...)
aov_4(formula, data, observed = NULL, fun.aggregate = NULL, type = afex_options("type"),
     factorize = afex_options("factorize"),
     check.contrasts = afex_options("check.contrasts"),
     return = afex_options("return_aov"),
     anova_table = list(), ..., print.formula = FALSE)

Arguments

formula

A formula specifying the ANOVA model similar to aov (for aov_car or similar to lme4:lmer for aov_4). Should include an error term (i.e., Error(id/...) for

data

A data.frame containing the data. Mandatory.

fun.aggregate

The function for aggregating the data before running the ANOVA if there is more than one observation per individual and cell of the design. The default NULL issues a warning if aggregation is necessary and uses

type

The type of sums of squares for the ANOVA. The default is given by afex_options("type"), which is initially set to 3. Passed to Anova. Possible values are "II", <

factorize

logical. Should between subject factors be factorized (with note) before running the analysis. he default is given by afex_options("factorize"), which is initially TRUE. If one wants to run an ANCOVA, needs to be set to FAL

check.contrasts

logical. Should contrasts for between-subject factors be checked and (if necessary) changed to be "contr.sum". See details. The default is given by afex_options("check.contrasts"), which is initially TRUE

return

What should be returned? The default is given by afex_options("return_aov"), which is initially "afex_aov", returning an S3 object of class afex_aov for which various

observed

character vector indicating which of the variables are observed (i.e, measured) as compared to experimentally manipulated. The default effect size reported (generalized eta-squared) requires correct specification of the obsered (in contrast t

anova_table

list of further arguments passed to function producing the ANOVA table. Arguments such as es (effect size) or correction are passed to either anova.afex_aov or nice. Note that those settin

...

Further arguments passed to fun.aggregate.

character 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.

between

character vector indicating the between-subject(s) factor(s)/column(s) in data. Default is NULL indicating no between-subjects factors.

within

character vector indicating the within-subject(s)(or repeated-measures) factor(s)/column(s) in data. Default is NULL indicating no within-subjects factors.

covariate

character vector indicating the between-subject(s) covariate(s) (i.e., column(s)) in data. Default is NULL indicating no covariates.

print.formula

aov_ez and aov_4 are wrapper for aov_car. This boolean argument indicates whether the formula in the call to car.aov should be printed.

Value

aov_car, aov_4, and aov_ez are wrappers for Anova and aov, the return value is dependent on the return argument. Per default, an S3 object of class "afex_aov" is returned containing the following slots:
[object Object],[object Object],[object Object],[object Object],[object Object] In addition, the object has the following attributes: "dv", "id", "within", "between", and "type".
The print method for afex_aov objects (invisibly) returns (and prints) the same as if return is "nice": a nice ANOVA table (produced by nice) with the following columns: Effect, df, MSE (mean-squared errors), F (potentially with significant symbols), ges (generalized eta-squared), p.

encoding

UTF-8

Details

Details of ANOVA Specification{ aov_ez will concatenate 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.

The formulas for aov_car or aov_4 must contain a single Error term specifying the ID column and potential within-subject factors (you can use mixed for running mixed-effects models 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; in other words, 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!

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 include calculation of the effect size generalized eta-squared for which all non-manipluated (i.e., observed) variables need to be specified via the observed argument to obtain correct results. When changing the effect size to "pes" (partial eta-squared) or "none" via anova_table this becomes unnecessary.

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".) }

Statistical Issues{ Type 3 sums of squares are default in afex. While some authors argue that so-called type 3 sums of squares are dangerous and/or problematic (most notably Venables, 2000), they are the default in many commercial statistical application such as SPSS or SAS. Furthermore, statisticians with an applied perspective recommend type 3 tests (e.g., Maxwell and Delaney, 2004). Consequently, they are the default for the ANOVA functions described here. For some more discussion on this issue see http://stats.stackexchange.com/q/6208/442{here}.

Note that lower order effects (e.g., main effects) in type 3 ANOVAs are only meaningful with http://www.ats.ucla.edu/stat/mult_pkg/faq/general/effect.htm{effects coding}. That is, contrasts should be set to contr.sum to obtain meaningful results. This is imposed automatically for the functions discussed here as long as check.contrasts is TRUE (the default). I nevertheless recommend to set the contrasts globally to contr.sum via running set_sum_contrasts. For a discussion of the other (non-recommended) coding schemes see http://www.ats.ucla.edu/stat/r/library/contrast_coding.htm{here}. }

Follow-Up Contrasts and Post-Hoc Tests{ The S3 object returned per default can be directly passed to lsmeans::lsmeans for further analysis. This allows to test any type of contrasts that might be of interest independent of whether or not this contrast involves between-subject variables, within-subject variables, or a combination thereof. The general procedure to run those contrasts is the following (see Examples for a full example):

Estimate anafex_aovobject with the function returned here. For example:x <- aov_car(dv ~ a*b + (id/c), d)
Obtain aref.gridobject by runninglsmeanson theafex_aovobject from step 1 using the factors involved in the contrast. For example:r <- lsmeans(x, ~a:c)
Create a list containing the desired contrasts on the reference grid object from step 2. For example:con1 <- list(a_x = c(-1, 1, 0, 0, 0, 0), b_x = c(0, 0, -0.5, -0.5, 0, 1))
Test the contrast on the reference grid usingcontrast. For example:contrast(r, con1)
To control for multiple testing p-value adjustments can be specified. For example the Bonferroni-Holm correction:contrast(r, con1, adjust = "holm")

Note that lsmeans allows for a variety of advanced settings and simplifiations, for example: all pairwise comparison of a single factor using one command (e.g., lsmeans(x, "a", contr = "pairwise")) or advanced control for multiple testing by passing objects to multcomp. A comprehensive overview of the functionality is provided in the accompanying vignettes (see http://cran.r-project.org/package=lsmeans{here}).

A caveat regarding the use of lsmeans concerns the assumption of sphericity for ANOVAs including within-subjects/repeated-measures factors (with more than two levels). While the ANOVA tables per default report results using the Greenhousse-Geisser correction, no such correction is available when using lsmeans. This may result in anti-conservative tests.

lsmeans is loaded/attached automatically when loading afex via library or require. }

Methods for afex_aov Objects{ A full overview over the methods provided for afex_aov objects is provided in the corresponding help page: afex_aov-methods. The probably most important ones for end-users are summary and anova.

The summary method returns, for ANOVAs containing within-subject (repeated-measures) factors with more than two levels, the complete univariate analysis: Results without df-correction, the Greenhouse-Geisser corrected results, the Hyunh-Feldt corrected results, and the results of the Mauchly test for sphericity.

The anova method returns a data.frame of class "anova" containing the ANOVA table in numeric form (i.e., the one in slot anova_table of a afex_aov). This method has arguments such as correction and es and can be used to obtain an ANOVA table with different correction than the one initially specified. }

References

Maxwell, S. E., & Delaney, H. D. (2004). Designing Experiments and Analyzing Data: A Model-Comparisons Perspective. Mahwah, N.J.: Lawrence Erlbaum Associates.

Venables, W.N. (2000). Exegeses on linear models. Paper presented to the S-Plus User's Conference, Washington DC, 8-9 October 1998, Washington, DC. Available from: http://www.stats.ox.ac.uk/pub/MASS3/Exegeses.pdf

Examples

Run this code

##########################
## 1: Specifying ANOVAs ##
##########################

# Example using a purely within-subjects design 
# (Maxwell & Delaney, 2004, Chapter 12, Table 12.5, p. 578):
data(md_12.1)
aov_ez("id", "rt", md_12.1, within = c("angle", "noise"), 
       anova_table=list(correction = "none", es = "none"))

# Default output
aov_ez("id", "rt", md_12.1, within = c("angle", "noise"))       


# examples using obk.long (see ?obk.long), a long version of the OBrienKaiser dataset (car package).
# Data is a split-plot or mixed design: contains both within- and between-subjects factors.
data(obk.long, package = "afex")

# estimate mixed ANOVA on the full design:
aov_car(value ~ treatment * gender + Error(id/(phase*hour)), 
        data = obk.long, observed = "gender")

aov_4(value ~ treatment * gender + (phase*hour|id), 
        data = obk.long, observed = "gender")

aov_ez("id", "value", obk.long, between = c("treatment", "gender"), 
        within = c("phase", "hour"), observed = "gender")

# the three calls return the same ANOVA table:
##                         Effect          df   MSE         F  ges p.value
## 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   .0003
## 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  <.0001
## 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


# "numeric" variables are per default converted to factors (as long as factorize = TRUE):
obk.long$hour2 <- as.numeric(as.character(obk.long$hour))

# gives same results as calls before
aov_car(value ~ treatment * gender + Error(id/hour2*phase), 
        data = obk.long, observed = c("gender"))


# 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)

aov_4(value ~ treatment * gender + age + (phase*hour|id), 
        data = obk.long, observed = c("gender", "age"), factorize = FALSE)

aov_ez("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")

aov_ez("id", "value", obk.long, c("treatment", "gender"), "hour", observed = "gender")

# aggregating over both within-subjects factors (again with warning),
# only between-subjects factors:
aov_car(value ~ treatment * gender + Error(id), data = obk.long, observed = c("gender"))
aov_4(value ~ treatment * gender + (1|id), data = obk.long, observed = c("gender"))
aov_ez("id", "value", obk.long, between = c("treatment", "gender"), observed = "gender")

# only within-subject factors (ignoring between-subjects factors)
aov_car(value ~ Error(id/(phase*hour)), data = obk.long)
aov_4(value ~ (phase*hour|id), data = obk.long)
aov_ez("id", "value", obk.long, within = c("phase", "hour"))

### changing defaults of ANOVA table:

# no df-correction & partial eta-squared:
aov_car(value ~ treatment * gender + Error(id/(phase*hour)), 
        data = obk.long, anova_table = list(correction = "none", es = "pes"))

# no df-correction and no MSE
aov_car(value ~ treatment * gender + Error(id/(phase*hour)), 
        data = obk.long,observed = "gender", 
        anova_table = list(correction = "none", MSE = FALSE))


###########################
## 2: Follow-up Analysis ##
###########################

# use data as above
data(obk.long, package = "afex")

# 1. obtain afex_aov object:
a1 <- aov_ez("id", "value", obk.long, between = c("treatment", "gender"), 
        within = c("phase", "hour"), observed = "gender")

# 1b. plot data:
lsmip(a1, gender ~ hour | treatment+phase)

# 2. obtain reference grid object:
r1 <- lsmeans(a1, ~treatment +phase)
r1

# 3. create list of contrasts on the reference grid:
c1 <- list(
  A_B_pre = c(0, -1, 1, rep(0, 6)),  # A versus B for pretest
  A_B_comb = c(0, 0, 0, 0, -0.5, 0.5, 0, -0.5, 0.5), # A vs. B for post and follow-up combined
  effect_post = c(0, 0, 0, -1, 0.5, 0.5, 0, 0, 0), # control versus A&B post
  effect_fup = c(0, 0, 0, 0, 0, 0, -1, 0.5, 0.5), # control versus A&B follow-up
  effect_comb = c(0, 0, 0, -0.5, 0.25, 0.25, -0.5, 0.25, 0.25) # control versus A&B combined
)

# 4. test contrasts on reference grid:
contrast(r1, c1)

# same as before, but using Bonferroni-Holm correction for multiple testing:
contrast(r1, c1, adjust = "holm")

# 2. (alternative): all pairwise comparisons of treatment:
lsmeans(a1, "treatment", contr = "pairwise")

#######################
## 3: Other examples ##
#######################
data(obk.long, package = "afex")

# replicating ?Anova using aov_car:
obk_anova <- aov_car(value ~ treatment * gender + Error(id/(phase*hour)), 
        data = obk.long, type = 2)
# in contrast to aov you do not need the within-subject factors outside Error()

str(obk_anova, 1, give.attr = FALSE)
## List of 6
##  $ anova_table:Classes 'anova' and 'data.frame':  15 obs. of  6 variables:
##  $ aov        :List of 5
##  $ Anova      :List of 14
##  $ lm         :List of 13
##  $ data       :List of 3
##  $ information:List of 5

obk_anova$Anova
## 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

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