This document is designed for students in the APSY510/511 introductory statistics sequence at the University at Albany. However, it should have much more general utility for other students and researchers learning to use R. A major strength of the document is presentation of extensive methods/logic of employing analytical contrasts both as an approach to obtaining the omnibus analysis and for direct evaluation.
Designs that are called “Completely Randomized Factorial Designs”, presume assignment of cases to groups independently. They are sometimes called “between subjects” designs in the psychological sciences. Two independent variables are factorially arranged so that each level of each factor is found in combination with each level of the other factor.
The goal of this document is to provide a fairly comprehensive overview of techniques to obtain all of the core assessments and tests that a researcher would typically need to employ. This includes assessment of the”omnibus” main effects and interactions. It also includes coverage of simple main effects. In addition, the ability to employ analytical/orthogonal contrasts is a central part of the analysis. Contrasts on all main effects, interactions, and simple main effects are all possible in R. Several post hoc, pairwise comparison, approaches are also demonstrated.
Additional sections cover effect size computations, graphical exploration and presentation of the data, assessment of assumptions, and some robust methods.
One issue with programming software to handle these designs is the non-uniformity of the language used to describe effects. In particular the phrases “simple main effect”, “simple main effect contrasts”, “partial interactions”, “interaction contrasts” and “interaction comparisons” do not gain usage much beyond the experimental design textbooks - particularly those from the behavioral sciences such as Winer, Kirk, Keppel, etc. In R, these effects are, with some labor, possible to define and test, but the labels are not those we are comfortable with. Newer add-on packages are being refined to make evaluation of these followups to the omnibus tests more approachable. The user should not expect the approach to be as direct/simple as it was with our consideration of the SPSS MANOVA procedure.
The document is constantly under revision as additional methods become available in new R packages or R packages that I learn about.
Resampling methods such as bootstrapping are not included at this time and will be covered in a separate document.
Quite a few packages are used in this document. In many code chunks, where a the package origin of a function is not described or obvious, I use the pkgname::functionname format when a function is called. For example psych::describeBy calls the describeBy function from the **psych** package. This format is not used when functions come from base system packages. One can always ask for help on a function (?functionname`) to establish the package of its origin if I’ve been inconsistent in this formatting.
library(psych)
library(car)
library(foreign)
library(ggplot2)
library(ggthemes)
library(ggdist)
library(ggridges)
library(phia)
library(plyr)
library(ez)
library(sciplot)
library(granova)
library(afex)
library(emmeans)
library(sjstats)
library(effectsize)
library(knitr)
#library(kableExtra)
library(gt)
library(nortest)
library(BayesFactor)
library(Rmisc)
library(gt)The data set used in this document is the 3x2 factorial employed for several prior illustrations in the 510/511 sequence. It was first introduced as the “example1” data set in the fall. The DV was the number of words recalled in a memorized list. IV’s were a treatment variable where participants received either positive or negative feedback during acquisition or were in a control condition. The second IV was an age categorization of young vs older participants. I believe that this data set originally came from an early edition of the Keppel experimental design textbook (perhaps Keppel and Zedeck).
Note that if we imported via the .csv file, R would have changed the order of the Praise/Reproof/None categories to alphabetical. The read.SPSS function we used (from the foreign package) permits us to keep the order as we used them in SPSS. This is very helpful for the sake of comparison to our earlier SPSS work. Of course, we could do a rework of the order in R, but this way is faster.
The example is an “equal N” design, so we will not address issues of Type I/II/III SS in this document.
Note that the ‘attach’ function is used so that variables may be simply named later in the document without the ‘$’ convention. This is most helpful with the graphics sections and the data frame is detached after those sections.
#prn.data <- foreign::read.spss(file.choose(), use.value.labels=TRUE,
# max.value.labels=Inf, to.data.frame=TRUE)
prn.data <- foreign::read.spss("2way_base.sav", use.value.labels=TRUE,
max.value.labels=Inf, to.data.frame=TRUE)re-encoding from CP1252#prn.data <- read.csv("2way_base.csv", stringsAsFactors=TRUE)
attach(prn.data)
# gt is used to provide nicer formatting of this table; the 'headTail' function is useful
gt::gt(psych::headTail(prn.data))| group | age | errors |
|---|---|---|
| PRAISE | CHILDREN | 4 |
| PRAISE | CHILDREN | 7 |
| PRAISE | CHILDREN | 5 |
| PRAISE | CHILDREN | 3 |
| NA | NA | ... |
| NONE | ADULTS | 6 |
| NONE | ADULTS | 4 |
| NONE | ADULTS | 3 |
| NONE | ADULTS | 7 |
Exploratory data analysis is always important. The approach taken here is more or less the standard sequence we have used for other illustrations.
Using the psych package, look at some descriptive statistics. Note that type=2 sets the type of skewness and kurtosis coefficients used in the describeBy function
library(psych)
db1 <- describeBy(errors,list(group,age),mat=TRUE,type=2, digits=3)
# I subset the db1 data frame to leave out some of the descriptive stats
db1[,c(1:3, 5:8,11:16)]Or use gt' or knitr::kable with kableExtra::kable_styling to produce a more nicely formatted table:
# kable(db1[,c(2:3, 5:8,11:16)]) %>%
# kable_styling(full_width = F, position = "left")
gt(db1[,c(2:3, 5:8,11:16)])| group1 | group2 | n | mean | sd | median | min | max | range | skew | kurtosis | se |
|---|---|---|---|---|---|---|---|---|---|---|---|
| PRAISE | CHILDREN | 7 | 5.000 | 1.414 | 5 | 3 | 7 | 4 | 0.000 | -1.200 | 0.535 |
| REPROOF | CHILDREN | 7 | 8.714 | 1.113 | 9 | 7 | 10 | 3 | -0.249 | -0.944 | 0.421 |
| NONE | CHILDREN | 7 | 6.429 | 1.512 | 7 | 4 | 8 | 4 | -0.620 | -0.809 | 0.571 |
| PRAISE | ADULTS | 7 | 5.429 | 1.718 | 6 | 2 | 7 | 5 | -1.487 | 2.666 | 0.649 |
| REPROOF | ADULTS | 7 | 2.857 | 1.345 | 3 | 1 | 5 | 4 | 0.352 | -0.302 | 0.508 |
| NONE | ADULTS | 7 | 5.000 | 2.000 | 4 | 3 | 8 | 5 | 0.525 | -1.550 | 0.756 |
Several types of graphical displays are illustrated here to provide a rapid visual impression of the data set in the EDA process. A few more advanced types of plots are found toward the end of the section.
It is possible to use the stripchart function the way we used it for a 1-way design, by passing the factorial nature of this 2way in the model formula. The product may be a useful EDA method, but it doesn’t label the Y axis completely with my bare-bones example. Using this may require exploring control of graph attributes a bit more, especially if there are more than 3 and 2 levels of the factors. But here we can discern that the three children groups are at the lowest Y axis positions and adults the highest. And the treatment group order follows the factor ordering, praise, reproof, none. This is a good example of a quick eda plot, but one that is nowhere near a publication quality graph.
stripchart(errors~group*age, pch=1,method="stack", cex.axis=.5)
The plot.design function from the base R installation provides a quick way to “see” main effect variation.
plot.design(errors~age*group, main="Examine Main Effects")
We may want to examine the distribution of the DV within each of the six groups. I’ve done this here by producing a ggplot2 graph that creates “facets” for the groups. For our data set, with such a low N, the frequency histograms may not be very helpful above and beyond what simple dot plots show us.
# first without kernel densities
p1a <- ggplot(prn.data, aes(errors))
p1b <- p1a + geom_histogram(
binwidth=1,
col="black",
fill="grey80") +
facet_wrap(age~group)
p1c <- p1b +theme_gray()
#p1c
#now with kernel densities
p2a <- ggplot(prn.data, aes(errors))
p2b <- p2a + geom_histogram(aes(y =..density..),
binwidth=1,
col="black",
fill="grey80") +
geom_density(col="black") +
facet_wrap(age~group)
p2c <- p2b +theme_bw()
p2cWarning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
ℹ Please use `after_stat(density)` instead.
The base system ’boxplot function permits specification of a factorial model with the familiar “formula” specification. This results in a fairly nice clustered boxplot. The axis labels are overwritten and confusing - we can discern the three children groups to the left and the three adult groups to the right - in the praise, reproof, none order. It is not the best plot for disseminating information, but for EDA purposes it is an efficient method with this simple code.
boxplot(errors~group*age, data=prn.data,col=c("gray75","gray95"),
main="2-way Factorial Design Example",
xlab="Treatment Group",
ylab="Mean Errors")
The so-called “dynamite plot” is the standard graph for factorial design preseentation of means with standard errors. Initially, I used a function from the sciplot package called bargraph.CI. Its default implementation produces standard error bars (+/- 1 std error).
#require(sciplot)
bargraph.CI(group,errors,group=age,lc=TRUE, uc=TRUE,legend=T,
cex.leg=1,bty="n",col=c("gray75","gray95"),ylim=c(0.01,9.9),
ylab="MEAN ERRORS",main="Base 2-Way Design Illustration")
box()
axis(4,labels=F)
First, we need to summarize the data set to produce a new data frame that ggplot can used with ggplot2. This uses a function from the Rmisc package that extracts the means/SDs from the original data frame and produces a new data frame that has just means and std errors for the groups. The function returns a data frame that contains the means, standard deviations, std errors and CI’s for each of the groups. We might use any of the three indices of dispersion on our bar chart.
myData <- Rmisc::summarySE(data=prn.data, groupvars=c("group","age"), measurevar="errors" )
myData group age N errors sd se ci
1 PRAISE CHILDREN 7 5.000000 1.414214 0.5345225 1.307929
2 PRAISE ADULTS 7 5.428571 1.718249 0.6494372 1.589116
3 REPROOF CHILDREN 7 8.714286 1.112697 0.4205600 1.029073
4 REPROOF ADULTS 7 2.857143 1.345185 0.5084323 1.244089
5 NONE CHILDREN 7 6.428571 1.511858 0.5714286 1.398235
6 NONE ADULTS 7 5.000000 2.000000 0.7559289 1.849691Next I produce a relatively finished graph. Note that it would be easy to switch colors from my favored honeydew shades to a set of grays by changing the color names in the ‘scale_fill_manual’ ggplot argument.
#library(ggplot2)
#library(ggthemes)
# Default bar plot
p<- ggplot(myData, aes(x=group, y=errors, fill=age)) +
geom_bar(stat="identity", color="black",
position=position_dodge()) +
geom_errorbar(aes(ymin=errors-se, ymax=errors+se), width=.2,
position=position_dodge(.9))
#print(p)
# a bit more finished bar plot
p2 <- p+labs(title="Errors by Treatment and Age +/- SEM", x="Treatment Group", y = "Mean Errors") +
theme_classic() +
theme(panel.grid.major.x = element_blank(),
panel.grid.major.y = element_blank(),
panel.grid.minor.x = element_blank(),
panel.grid.minor.y = element_blank(),
panel.background = element_blank(),
axis.line.y = element_line(colour="black", size=.7),
axis.line.x = element_line(colour="black", size=.7),
plot.title = element_text(hjust=.5)
) +
coord_cartesian(ylim=c(0, 9.25)) + scale_y_continuous(expand = c(0,0)) +
theme(legend.justification=c(0,0), legend.position=c(0,.8)) +
scale_fill_manual(values=c('honeydew3','honeydew2'))Warning: The `size` argument of `element_line()` is deprecated as of ggplot2 3.4.0.
ℹ Please use the `linewidth` argument instead.Warning: A numeric `legend.position` argument in `theme()` was deprecated in ggplot2
3.5.0.
ℹ Please use the `legend.position.inside` argument of `theme()` instead.print(p2)
ggplot2A variety of newer types of plots have become somewhat in vogue in ggplot2 circles. The first is a simply density display and then the next two are variations on what have come to be called raincloud plots.
The ggridges package provides a geom_density_ridges function that can provide information in a different way than typically done for these types of ANOVA designs. Most of the code here is style configuration. Note how one IV is specified in the initial “aes” and the other is specified to be displayed in facets.
ggplot(prn.data,
aes(x = errors, y = group, fill = group)) +
geom_density_ridges() +
theme_bw() +
labs("Errors in recall by Treatment") +
xlab("Errors")+ ylab("Treatment Group")+
scale_fill_manual(values=c('honeydew3','honeydew2','lemonchiffon2')) +
facet_grid(. ~age)+
theme(legend.position = "none") +
theme(
axis.title.x = element_text(hjust=.5,size=11),
axis.title.y = element_text(hjust=.5,size=11)
)
A more informative plot shows boxplots, original data points, and density plots for each group, using the stat_halfeye function from the ggdist package.
ggplot(prn.data,aes(x = factor(group), y = errors, fill = factor(group))) +
xlab("Treatment Group") + ylab("Errors") +
# add half-violin from {ggdist} package
stat_halfeye(
# adjust bandwidth
adjust = 0.5,
# move to the right
justification = -0.2,
# remove the slub interval
.width = 0,
point_colour = NA
) +
geom_boxplot(
width = 0.12,
# removing outliers
outlier.color = NA,
alpha = 0.5
) +
stat_dots(
# ploting on left side
side = "left",
# adjusting position
justification = 1.1,
# adjust grouping (binning) of observations
binwidth = 0.25
) +
scale_fill_manual(values=c('honeydew3','honeydew2','lemonchiffon2')) +
theme_gray() +
facet_grid(. ~age) +
theme(axis.text.x = element_text(angle = -45, vjust = 0.5, hjust=0))
The final plot shown here is a more classic “raincloud” plot where the DV axis is the Y axis and the data points (rain) look to be falling from the density “cloud”. This plot uses the geom_rain function from the ggrain package.
ggplot2::ggplot(prn.data, aes(group, errors, fill = group)) +
ggrain::geom_rain(alpha = .5,
point.args.pos = rlang::list2(position =
position_jitter(width = 0.07, height = 0)),
boxplot.args.pos = list(
width = 0.05, position = position_nudge(x = 0.13)),
violin.args.pos = list(
side = "r",
width = 0.7, position = position_nudge(x = 0.2))) +
theme_classic() +
scale_fill_brewer(palette = 'Dark2') +
guides(fill = 'none', color = 'none') +
coord_flip()+
facet_grid(. ~age) +
theme_gray() +
xlab("Treatment Group") + ylab("Errors")Registered S3 methods overwritten by 'ggpp':
method from
heightDetails.titleGrob ggplot2
widthDetails.titleGrob ggplot2
As we saw with the 1way design, both aov and lm can be employed to assess the 2way design. Both approaches are executed here.
In this and following sections where we can name the data frame in an argment in the aov or lm function, we don’t need the data frame to be attached. It is detached here:
detach(prn.data)aov and lm for the omnibus 2-way factorial modelFor factorial designs, the model specification can proceed one of two ways. A full model that contains both interactions and main effects could be specified as:
errors ~ group + age + group:age, where the colon notation specifies an interaction term.
But a more efficient way of specifying the model is to use
errors ~ group*age, where the asterisk term results in production of both the higher order interaction term and the lower order main effects.
Here, using aov, we see that the summary and anova functions give similar ANOVA summary tables. Notice that the interaction term are always written using the colon notation.
# We can do the 2 way ANOVA with AOV
fit.1aov <- aov(errors~ group*age, data=prn.data)
summary(fit.1aov) Df Sum Sq Mean Sq F value Pr(>F)
group 2 2.71 1.36 0.57 0.571
age 1 54.86 54.86 23.04 2.76e-05 ***
group:age 2 73.00 36.50 15.33 1.53e-05 ***
Residuals 36 85.71 2.38
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit.1aov)Analysis of Variance Table
Response: errors
Df Sum Sq Mean Sq F value Pr(>F)
group 2 2.714 1.357 0.57 0.5705
age 1 54.857 54.857 23.04 2.764e-05 ***
group:age 2 73.000 36.500 15.33 1.527e-05 ***
Residuals 36 85.714 2.381
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1#Anova(fit.1aov, type=3)When we use lm, the summary and anova functions give different tables. Summary produces a listing of all individual regression coefficients (“estimates”) and their std errors and t-tests. This prompts a question of what the coding scheme is that R has used by default and we consider that in the next section. Use of anova produces the expected ANOVA summary table of SS/MS/DF/F’s/p’s with multiple df sources listed rather than all single df terms.
# Or, we can do the 2 way anova with the lm function (same outcome)
fit.1lm <- lm(errors~group*age, data=prn.data)
summary(fit.1lm)
Call:
lm(formula = errors ~ group * age, data = prn.data)
Residuals:
Min 1Q Median 3Q Max
-3.4286 -1.0000 0.1429 1.1071 3.0000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.0000 0.5832 8.573 3.21e-10 ***
groupREPROOF 3.7143 0.8248 4.503 6.78e-05 ***
groupNONE 1.4286 0.8248 1.732 0.0918 .
ageADULTS 0.4286 0.8248 0.520 0.6065
groupREPROOF:ageADULTS -6.2857 1.1664 -5.389 4.56e-06 ***
groupNONE:ageADULTS -1.8571 1.1664 -1.592 0.1201
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.543 on 36 degrees of freedom
Multiple R-squared: 0.6037, Adjusted R-squared: 0.5487
F-statistic: 10.97 on 5 and 36 DF, p-value: 1.823e-06anova(fit.1lm)Analysis of Variance Table
Response: errors
Df Sum Sq Mean Sq F value Pr(>F)
group 2 2.714 1.357 0.57 0.5705
age 1 54.857 54.857 23.04 2.764e-05 ***
group:age 2 73.000 36.500 15.33 1.527e-05 ***
Residuals 36 85.714 2.381
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The lm summary object above included regression coefficient for five coding vectors. What are the “contrasts” employed as the default?
contrasts(prn.data$group) REPROOF NONE
PRAISE 0 0
REPROOF 1 0
NONE 0 1contrasts(prn.data$age) ADULTS
CHILDREN 0
ADULTS 1This reveals that R creates default coding vectors for each IV. But the age IV only requires one since it is just a two level variable. The pattern of coefficients for the treatment group IV clearly reveal that R is using dummy coding by default (it may be called indicator coding in some help docs and could have been specified with contr.treatment as we saw with the 1-way design tutorial). The first group (praise) is the reference category, by default (and could be changed if desired). The regression coefficients in the output of the summary(fit.1lm) code above should now be interpretable since we know the coding scheme. It is worthwhile to contemplate what the product vectors were that produced the interaction vectors.
With our equal N design, these three types of SS are equivalent. If we had unequal N, we might want to employ Type 2 or 3 SS. We can use the Anova function from car to obtain these. However, Anova will not provide proper computations if we continue to use dummy coding for the model. So, we can change this to “zero sum” contrasts by choosing effect coding. Later in this document we will use orthogonal contrast coding which will also suffice, and provides the analyst with complete control over contrast specification.
#options(conrasts=c("contr.sum","contr.poly"))
contrasts(prn.data$group)=contr.sum
contrasts(prn.data$age)=contr.sum
contrasts(prn.data$group) [,1] [,2]
PRAISE 1 0
REPROOF 0 1
NONE -1 -1contrasts(prn.data$age) [,1]
CHILDREN 1
ADULTS -1Now, refitting the base aov model uses these newly specified contrasts. Note that coding vector choice does not affect the omnibus F test outcomes. The same SS/MS/F/p values emerge
fit.1aovb <- aov(errors~group*age, data=prn.data)
#anova(fit.1aovb)
Anova(fit.1aovb, type=3)Anova Table (Type III tests)
Response: errors
Sum Sq Df F value Pr(>F)
(Intercept) 1303.71 1 547.56 < 2.2e-16 ***
group 2.71 2 0.57 0.5705
age 54.86 1 23.04 2.764e-05 ***
group:age 73.00 2 15.33 1.527e-05 ***
Residuals 85.71 36
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1However, if we examine the output with summary.lm to see the individual regression coefficients and tests, these will be different than those seen above. With “effect” coding the intercept has a value equal to the grand mean of the DV, rather than the mean of a reference category as seen above with indicator coding. And the regression coefficients have values equivalent to the deviation of individual groups from the grand mean. The hypotheses about these individuals group deviations from the grand mean my not be of interest to the researcher, so the illustration of “effect” coding here is provided simply as an alternative to dummy coding for production of the omnibus F tests of main effects and interactions. Work found in a later section of this document, with orthogonal/analytical contrasts is a preferred strategy.
summary.lm(fit.1aovb)
Call:
aov(formula = errors ~ group * age, data = prn.data)
Residuals:
Min 1Q Median 3Q Max
-3.4286 -1.0000 0.1429 1.1071 3.0000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.5714 0.2381 23.400 < 2e-16 ***
group1 -0.3571 0.3367 -1.061 0.295909
group2 0.2143 0.3367 0.636 0.528544
age1 1.1429 0.2381 4.800 2.76e-05 ***
group1:age1 -1.3571 0.3367 -4.031 0.000276 ***
group2:age1 1.7857 0.3367 5.303 5.93e-06 ***
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.543 on 36 degrees of freedom
Multiple R-squared: 0.6037, Adjusted R-squared: 0.5487
F-statistic: 10.97 on 5 and 36 DF, p-value: 1.823e-06I recognize three main ways of obtaining effect sizes:
Use the sjstats package
Use the afex package (see section below)
Use the effectsize package
The sjstats package has a function that computes several useful effect size statistics and it works well with two-factor designs. The anova_stats function does the analysis and also provides effect sizes. Note that this default approach will produce effect sizes based on Type I SS. If Type III are desired, see the section two code chunks below.
# the gt function permits nicer formatting of the table
#anova_stats(fit.1aov)
gt::gt(anova_stats(fit.1aov))| etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | term | sumsq | df | meansq | statistic | p.value | power |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.013 | 0.031 | -0.009 | -0.021 | -0.009 | 0.178 | group | 2.714 | 2 | 1.357 | 0.57 | 0.571 | 0.145 |
| 0.254 | 0.390 | 0.240 | 0.344 | 0.243 | 0.800 | age | 54.857 | 1 | 54.857 | 23.04 | 0.000 | 0.998 |
| 0.338 | 0.460 | 0.312 | 0.406 | 0.315 | 0.923 | group:age | 73.000 | 2 | 36.500 | 15.33 | 0.000 | 0.999 |
| NA | NA | NA | NA | NA | NA | Residuals | 85.714 | 36 | 2.381 | NA | NA | NA |
Or, we could specify an indidual set of effect sizes which would then be accompanied by confidence intervals. Note that since effect sizes cannot be negative, the smallest lower boundary for a CI would be zero. This happens whenever a term is NS at an alpha level equivalent to the CI level (e.g., .05 alpha and .95 CI level). This outcome occurred for the main effect of group in this data set.
effectsize::eta_squared(fit.1aov, partial=TRUE, ci=.95, alternative="two")# Effect Size for ANOVA (Type I)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
group | 0.03 | [0.00, 0.17]
age | 0.39 | [0.15, 0.58]
group:age | 0.46 | [0.20, 0.63]For future reference, when designs have unequal N, the following approach can compute these effect sizes using Type III SS, if that is desirable. anova_stats can work with a model object produced by the Anova function (upper case first letter A) from car, which permits Type II or III specification. Notice that we had to use the model object where the default coding was changed from dummy to zero-sum or effect coding.
#options(conrasts=c("contr.sum","contr.poly"))
gt::gt(anova_stats(Anova(fit.1aovb, type = "III")))Type 3 ANOVAs only give sensible and informative results when covariates
are mean-centered and factors are coded with orthogonal contrasts (such
as those produced by `contr.sum`, `contr.poly`, or `contr.helmert`, but
*not* by the default `contr.treatment`).| etasq | partial.etasq | omegasq | partial.omegasq | epsilonsq | cohens.f | term | sumsq | df | meansq | statistic | p.value | power |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0.013 | 0.031 | -0.009 | -0.021 | -0.009 | 0.178 | group | 2.714 | 2 | 1.357 | 0.57 | 0.571 | 0.145 |
| 0.254 | 0.390 | 0.240 | 0.344 | 0.243 | 0.800 | age | 54.857 | 1 | 54.857 | 23.04 | 0.000 | 0.998 |
| 0.338 | 0.460 | 0.312 | 0.406 | 0.315 | 0.923 | group:age | 73.000 | 2 | 36.500 | 15.33 | 0.000 | 0.999 |
| NA | NA | NA | NA | NA | NA | Residuals | 85.714 | 36 | 2.381 | NA | NA | NA |
To illustrate a way of using information in the fit object we can “manually” extract the relevant SS from the AOV model fit object. This permits computation of several effect size statistics “by hand”. Here I compute the partial eta squareds. Notice that the results match the values produced by the anova_stats function.
fit.1SS <- anova(fit.1aov)
SStreat <- fit.1SS["group", "Sum Sq"]
SSage <- fit.1SS["age", "Sum Sq"]
SSInt <- fit.1SS["group:age", "Sum Sq"]
SSErr <- fit.1SS["Residuals", "Sum Sq"]
pEtaSq.treat <- SStreat / (SStreat + SSErr)
pEtaSq.age <- SSage / (SSage + SSErr)
pEtaSq.int <- SSInt / (SSInt + SSErr)
pEtaSq.treat[1] 0.03069467pEtaSq.age[1] 0.3902439pEtaSq.int[1] 0.459946This simple approach with the effectsize package also permits quick evaluation of several effect size statistics, with confidence intervals.
eta_squared(fit.1aov, partial=TRUE)# Effect Size for ANOVA (Type I)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
group | 0.03 | [0.00, 1.00]
age | 0.39 | [0.19, 1.00]
group:age | 0.46 | [0.25, 1.00]
- One-sided CIs: upper bound fixed at [1.00].omega_squared(fit.1aov, partial=TRUE)# Effect Size for ANOVA (Type I)
Parameter | Omega2 (partial) | 95% CI
-------------------------------------------
group | 0.00 | [0.00, 1.00]
age | 0.34 | [0.15, 1.00]
group:age | 0.41 | [0.19, 1.00]
- One-sided CIs: upper bound fixed at [1.00].cohens_f(fit.1aov, partial=TRUE)# Effect Size for ANOVA (Type I)
Parameter | Cohen's f (partial) | 95% CI
---------------------------------------------
group | 0.18 | [0.00, Inf]
age | 0.80 | [0.48, Inf]
group:age | 0.92 | [0.57, Inf]
- One-sided CIs: upper bound fixed at [Inf].Next we produce the diagnostic plots for linear models and test normality. The plot function works on an aov object in the same way it does for lm objects. I have produced the two most important plots by extraction with the which argument. Even with the small sample sizes per group, these plots give some reassurance that any violation of homoscedasticity (homogeneity of variance) and normality assumptions is small for this data set.
plot(fit.1aov,which=1)
plot(fit.1aov,which=2)
#plot(fit.1aov,which=3)
#plot(fit.1aov,which=5)Several tests of the assumption that residuals are normal are also available (review the larger set of possibilities in the 1-way tutorial document). Here, I used the Anderson-Darling test and provide commented code for the Shapiro-Wilk test.
# Perform the Shapiro-Wilk test for normality on the residuals
#shapiro.test(residuals(fit.1aov))
# perform the Anderson-Darling normality test on the residuals
nortest::ad.test(residuals(fit.1aov))
Anderson-Darling normality test
data: residuals(fit.1aov)
A = 0.31887, p-value = 0.5231For illustration, I’ve done both the mean-centered and median=centered Levene Tests for Homogeneity of Variance (using the function from the car package). The median-centered version is preferred.
# first use the mean-centering approach and recall that this
# is what SPSS uses in one-way and GLM
car::leveneTest(errors~group*age,center=mean,data=prn.data)Levene's Test for Homogeneity of Variance (center = mean)
Df F value Pr(>F)
group 5 0.8957 0.4944
36 # then with median centering
# to be more robust against non-normality
car::leveneTest(errors~group*age,center=median,data=prn.data)Levene's Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 5 0.35 0.8789
36 We will want to implement orthogonal contrast coding for the treatment group factor. Age, with only two levels is already a 1 df contrast. Nonetheless, I specified the contrast for age directly as a placeholder to remind us that we need to specify contrasts for each factor separately (for larger designs when both factors have more than two levels)
# Let's create contrasts of interest to us.
# The same orthogonal set we used in our SPSS work with this data set.
contrasts.group <- matrix(c(-1,2,-1,1,0,-1),ncol=2)
contrasts(prn.data$group) <- contrasts.group
contrasts(prn.data$group) [,1] [,2]
PRAISE -1 1
REPROOF 2 0
NONE -1 -1For the sake of completeness, we will specify that age also uses a sum to zero analytical contrast. But since it is only a two-level factor, this contrast is equivalent to effect coding for which the variable is already set by the code in a previous section.
contrasts.age <- matrix(c(1,-1),ncol=1)
contrasts(prn.data$age) <- contrasts.age
contrasts(prn.data$age) [,1]
CHILDREN 1
ADULTS -1aov and lm modelingWe can apply these contrasts to the AOV and/or lm models from above, simply by rerunning them. The ‘split’ argument is used in a manner similar to what we did in 1-way designs. When ‘split’ sees the factor name “group” it knows to look for two contrasts (and we have named them ac1 and ac2). This results in a decomposition of both the main effect of group, and the interaction. Once again, ‘summary’ does different things on the ‘aov’ and ‘lm’ objects.
fit.2aov <- aov(errors~group*age, data=prn.data)
fit.2lm <- lm(errors~group*age, data=prn.data)
summary(fit.2aov, split=list(group=list(group.ac1=1, group.ac2=2))) Df Sum Sq Mean Sq F value Pr(>F)
group 2 2.71 1.36 0.570 0.571
group: group.ac1 1 0.96 0.96 0.405 0.529
group: group.ac2 1 1.75 1.75 0.735 0.397
age 1 54.86 54.86 23.040 2.76e-05 ***
group:age 2 73.00 36.50 15.330 1.53e-05 ***
group:age: group.ac1 1 66.96 66.96 28.125 5.93e-06 ***
group:age: group.ac2 1 6.04 6.04 2.535 0.120
Residuals 36 85.71 2.38
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary(fit.2lm, split=list(group=list(group.ac1=1, group.ac2=2)))
Call:
lm(formula = errors ~ group * age, data = prn.data)
Residuals:
Min 1Q Median 3Q Max
-3.4286 -1.0000 0.1429 1.1071 3.0000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.5714 0.2381 23.400 < 2e-16 ***
group1 0.1071 0.1684 0.636 0.529
group2 -0.2500 0.2916 -0.857 0.397
age1 1.1429 0.2381 4.800 2.76e-05 ***
group1:age1 0.8929 0.1684 5.303 5.93e-06 ***
group2:age1 -0.4643 0.2916 -1.592 0.120
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.543 on 36 degrees of freedom
Multiple R-squared: 0.6037, Adjusted R-squared: 0.5487
F-statistic: 10.97 on 5 and 36 DF, p-value: 1.823e-06#summary.lm(fit.2aov) #see belowThe ANOVA summary Tables from anova and Anova do not partition the 2-df sources into single df effects. Instead, then return only the Type I or Type II SS omnibus ANOVA summary tables. This gives four different tables containing the identical result. And, since ‘summary’ on the aov object above gave the omnibus sources plus contrasts, it would be the preferred approach (unless Type III SS were needed). For tests of Type III SS of contrasts and their effect sizes, we use the emmeans and phia approaches found in later sections in this document, or the immediately succeeding code chunk here that uses summary.lm.
anova(fit.2aov)Analysis of Variance Table
Response: errors
Df Sum Sq Mean Sq F value Pr(>F)
group 2 2.714 1.357 0.57 0.5705
age 1 54.857 54.857 23.04 2.764e-05 ***
group:age 2 73.000 36.500 15.33 1.527e-05 ***
Residuals 36 85.714 2.381
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit.2lm)Analysis of Variance Table
Response: errors
Df Sum Sq Mean Sq F value Pr(>F)
group 2 2.714 1.357 0.57 0.5705
age 1 54.857 54.857 23.04 2.764e-05 ***
group:age 2 73.000 36.500 15.33 1.527e-05 ***
Residuals 36 85.714 2.381
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# now produce Type III SS with the Anova function
# note that the Anova function in car will not produce the expected results
# on AOV or lm model objects when the "contrasts" employed are dummy or effect coding.
# since the default coding is dummy coding, as seen above, Anova should only be used
# for type 3 SS after contrasts are changed to zero-summing contrasts, as
# we have done here, implementing the orthogonal contrasts for each variable.
#require(car)
car::Anova(fit.2aov,type=3)Anova Table (Type III tests)
Response: errors
Sum Sq Df F value Pr(>F)
(Intercept) 1303.71 1 547.56 < 2.2e-16 ***
group 2.71 2 0.57 0.5705
age 54.86 1 23.04 2.764e-05 ***
group:age 73.00 2 15.33 1.527e-05 ***
Residuals 85.71 36
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(fit.2lm, type=3)Anova Table (Type III tests)
Response: errors
Sum Sq Df F value Pr(>F)
(Intercept) 1303.71 1 547.56 < 2.2e-16 ***
group 2.71 2 0.57 0.5705
age 54.86 1 23.04 2.764e-05 ***
group:age 73.00 2 15.33 1.527e-05 ***
Residuals 85.71 36
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary.lm to obtain tests of single df contrastsWe have seen that using the “split” argument in summary on an aov object is an efficient way of obtaining SS decompositions and F tests of contrasts. However, these will be Type I SS in unbalanced designs. We had seen in the 1-way tutorial that efficient way of obtaining inferences on single df contrasts in an aov object is to use the summary.lm function. This approach produces t-tests rather than F tests, but they are equivalent since the squares of the t’s are the F values. But in cases of unequal N, this equivalence is for Type III tests - an outcome that is desirable in most experimental circumstances with factorial designs. The only downside to using this approach is that it does not produce the individual SS for the contrasts, so are not available for things like manual computation of effect sizes.
summary.lm(fit.2aov)
Call:
aov(formula = errors ~ group * age, data = prn.data)
Residuals:
Min 1Q Median 3Q Max
-3.4286 -1.0000 0.1429 1.1071 3.0000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.5714 0.2381 23.400 < 2e-16 ***
group1 0.1071 0.1684 0.636 0.529
group2 -0.2500 0.2916 -0.857 0.397
age1 1.1429 0.2381 4.800 2.76e-05 ***
group1:age1 0.8929 0.1684 5.303 5.93e-06 ***
group2:age1 -0.4643 0.2916 -1.592 0.120
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.543 on 36 degrees of freedom
Multiple R-squared: 0.6037, Adjusted R-squared: 0.5487
F-statistic: 10.97 on 5 and 36 DF, p-value: 1.823e-06Manual computation of partial eta squareds is done here. First on the omnibus effects:
# compute partial eta squareds on Type III SS (same as Type I here since
# we have equal N)
fit.2SS <- Anova(fit.2aov)
SStreat2 <- fit.2SS["group", "Sum Sq"]
SSage2 <- fit.2SS["age", "Sum Sq"]
SSInt2 <- fit.2SS["group:age", "Sum Sq"]
SSErr2 <- fit.2SS["Residuals", "Sum Sq"]
pEtaSq2.treat <- SStreat2 / (SStreat2 + SSErr2)
pEtaSq2.age <- SSage2 / (SSage2 + SSErr2)
pEtaSq2.int <- SSInt2 / (SSInt2 + SSErr2)
pEtaSq2.treat[1] 0.03069467pEtaSq2.age[1] 0.3902439pEtaSq2.int[1] 0.459946Next, SS for the analytical contrasts are extracted from the model and then manually combined to calculate partial eta squareds.
# Note: To obtain partial eta squareds on the single df contrasts,
# save the SS partition table from the summary function and manually extract
# following ths strategy (the summary function is the same one from above)
SS.contrastmodel2 <- summary(fit.2aov,
split=list(group=list(group.ac1=1,
group.ac2=2)))
str(SS.contrastmodel2) # look at the table object to identify where the relevant SS are with strList of 1
$ :Classes 'anova' and 'data.frame': 8 obs. of 5 variables:
..$ Df : Named num [1:8] 2 1 1 1 2 1 1 36
.. ..- attr(*, "names")= chr [1:8] "" "group.ac1" "group.ac2" "" ...
..$ Sum Sq : Named num [1:8] 2.714 0.964 1.75 54.857 73 ...
.. ..- attr(*, "names")= chr [1:8] "" "group.ac1" "group.ac2" "" ...
..$ Mean Sq: Named num [1:8] 1.357 0.964 1.75 54.857 36.5 ...
.. ..- attr(*, "names")= chr [1:8] "" "group.ac1" "group.ac2" "" ...
..$ F value: Named num [1:8] 0.57 0.405 0.735 23.04 15.33 ...
.. ..- attr(*, "names")= chr [1:8] "" "group.ac1" "group.ac2" "" ...
..$ Pr(>F) : Named num [1:8] 5.71e-01 5.29e-01 3.97e-01 2.76e-05 1.53e-05 ...
.. ..- attr(*, "names")= chr [1:8] "" "group.ac1" "group.ac2" "" ...
- attr(*, "class")= chr [1:2] "summary.aov" "listof"SS.contrastmodel2 # look at the SS table to identify where the relevant SS Df Sum Sq Mean Sq F value Pr(>F)
group 2 2.71 1.36 0.570 0.571
group: group.ac1 1 0.96 0.96 0.405 0.529
group: group.ac2 1 1.75 1.75 0.735 0.397
age 1 54.86 54.86 23.040 2.76e-05 ***
group:age 2 73.00 36.50 15.330 1.53e-05 ***
group:age: group.ac1 1 66.96 66.96 28.125 5.93e-06 ***
group:age: group.ac2 1 6.04 6.04 2.535 0.120
Residuals 36 85.71 2.38
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# for example, the SS from the first interaction contrast.
SS.intcontr1 <- SS.contrastmodel2[[1]]["Sum Sq"][6,] # relevant SS from sixth row of object
SS.intcontr1group.ac1
66.96429 # now SS resid
SS.error <- SS.contrastmodel2[[1]]["Residuals", "Sum Sq"]
SS.error
85.71429 # now partial eta squared for this first interaction contrast:
pEtaSq.intcontr1 <- SS.intcontr1/(SS.intcontr1 + SS.error)
pEtaSq.intcontr1group.ac1
0.4385965 I am not aware of a built-in way of obtaining SS for individual Type III SS so that eta and partial eta squareds can be computed manually. This is revisited in a later section.
NOTE that ‘granova.2w’ produces an interactive 3D plot that will not render with R markdown, but I’ve included a screen capture below this code chunk’s output. Running this code independently from the markdown document will produce the plot in a separate graphics window.
# require(granova)
# granova requires the first variable in the data frame to be the dv, so.....
prn.data2 <- as.data.frame(cbind(prn.data$errors,prn.data$group,prn.data$age))
colnames(prn.data2) <- c("errors","group","age")
#prn.data2
granova.2w(data=prn.data2,formula=errors~group*age)$group.effects
1 3 2
-0.357 0.143 0.214
$age.effects
2 1
-1.14 1.14
$CellCounts.Reordered
age
group 2 1
1 7 7
3 7 7
2 7 7
$CellMeans.Reordered
age
group 2 1
1 5.43 5.00
3 5.00 6.43
2 2.86 8.71
$aov.summary
Df Sum Sq Mean Sq F value Pr(>F)
group 2 2.71 1.36 0.57 0.571
age 1 54.86 54.86 23.04 2.76e-05 ***
group:age 2 73.00 36.50 15.33 1.53e-05 ***
Residuals 36 85.71 2.38
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The plot fits an additive model plane for a two-IV model so that the deviation of group means from the expected position based on an additive model can help visualize any interaction.
The reader is encouraged to use the Moderator models shiny app that is provided for class purposes in the bcdstats package to see more detailed implementations of surface fitting in additive and interactive models. This app is also found on bcdudek.net
The phia package provides strong tools for exploring interactions. It will produce tests of contrasts and simple main effects, including main effect contrasts, interaction contrasts, simple main effects, and simple main effect contrasts. It appears to work well here with this balanced design (equal N), but extension to other kinds of simple effects, and to repeated measures deisgns may be challenging.
It appears to produce tests of Type III SS which usually is recommended for experimental work, but we cover that topic later in the course as we consider “unbalanced” designs” with unequal sample sizes.
Initially, we can repeat the omnibus anova, making sure that the proper contrasts are in place for the group factor. Even though age is only a 2-level factor, the contrast is switched to what would either be effect coding or contrast coding since initially R would have it as dummy coding.
# Let's rework the whole analysis from the beginning.
# Note that I changed the first contrast to use fractions rather than the original
# -1,2,-1 from above. This will help match up SME psi values with SME contrast psi values
contrasts.group <- matrix(c(-.5,1,-.5,1,0,-1),ncol=2)
contrasts(prn.data$group) <- contrasts.group
contrasts(prn.data$group) [,1] [,2]
PRAISE -0.5 1
REPROOF 1.0 0
NONE -0.5 -1contrasts.age <- matrix(c(1,-1),ncol=1)
contrasts(prn.data$age) <- contrasts.age
contrasts(prn.data$age) [,1]
CHILDREN 1
ADULTS -1# repeat the AOV with our othorgonal contrasts to make sure the correct fit model is
# employed. Same as analyses above, to make sure fit objects match.
fit.2aov <- aov(errors~group*age, data=prn.data)
fit.2lm <- lm(errors~group*age, data=prn.data)With the model objects in place we can view them. Both the aov and lm results are requested using summary and the split argument. This duplicates what was done earlier in this document for comparison to the results with the phia package to follow.
# this first summary function yields Type I SS if data set is unequal N
summary(fit.2aov, split=list(group=list(group.ac1=1, group.ac2=2))) Df Sum Sq Mean Sq F value Pr(>F)
group 2 2.71 1.36 0.570 0.571
group: group.ac1 1 0.96 0.96 0.405 0.529
group: group.ac2 1 1.75 1.75 0.735 0.397
age 1 54.86 54.86 23.040 2.76e-05 ***
group:age 2 73.00 36.50 15.330 1.53e-05 ***
group:age: group.ac1 1 66.96 66.96 28.125 5.93e-06 ***
group:age: group.ac2 1 6.04 6.04 2.535 0.120
Residuals 36 85.71 2.38
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# this second summary function yields Type III tests if data set is unequal N
summary(fit.2lm)
Call:
lm(formula = errors ~ group * age, data = prn.data)
Residuals:
Min 1Q Median 3Q Max
-3.4286 -1.0000 0.1429 1.1071 3.0000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.5714 0.2381 23.400 < 2e-16 ***
group1 0.2143 0.3367 0.636 0.529
group2 -0.2500 0.2916 -0.857 0.397
age1 1.1429 0.2381 4.800 2.76e-05 ***
group1:age1 1.7857 0.3367 5.303 5.93e-06 ***
group2:age1 -0.4643 0.2916 -1.592 0.120
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.543 on 36 degrees of freedom
Multiple R-squared: 0.6037, Adjusted R-squared: 0.5487
F-statistic: 10.97 on 5 and 36 DF, p-value: 1.823e-06Now the anova function is used, reminding us of commonalities and differences in what summary and anova provide for omnibus models.
anova(fit.2aov)Analysis of Variance Table
Response: errors
Df Sum Sq Mean Sq F value Pr(>F)
group 2 2.714 1.357 0.57 0.5705
age 1 54.857 54.857 23.04 2.764e-05 ***
group:age 2 73.000 36.500 15.33 1.527e-05 ***
Residuals 36 85.714 2.381
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1anova(fit.2lm)Analysis of Variance Table
Response: errors
Df Sum Sq Mean Sq F value Pr(>F)
group 2 2.714 1.357 0.57 0.5705
age 1 54.857 54.857 23.04 2.764e-05 ***
group:age 2 73.000 36.500 15.33 1.527e-05 ***
Residuals 36 85.714 2.381
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Next, repeat the Anova function to obtain type III SS, even though type I and type III are the same here with our equal N data set.
# obtain Type III SS on the model (not relevant for our equal-N data set)
Anova(fit.2aov, type=3)Anova Table (Type III tests)
Response: errors
Sum Sq Df F value Pr(>F)
(Intercept) 1303.71 1 547.56 < 2.2e-16 ***
group 2.71 2 0.57 0.5705
age 54.86 1 23.04 2.764e-05 ***
group:age 73.00 2 15.33 1.527e-05 ***
Residuals 85.71 36
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Anova(fit.2lm, type=3)Anova Table (Type III tests)
Response: errors
Sum Sq Df F value Pr(>F)
(Intercept) 1303.71 1 547.56 < 2.2e-16 ***
group 2.71 2 0.57 0.5705
age 54.86 1 23.04 2.764e-05 ***
group:age 73.00 2 15.33 1.527e-05 ***
Residuals 85.71 36
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The 1interactionMeans1 function from phia is introduced here as a way of obtaining the cell means and std errors. Note that the std errors are identical for all cells. The function is creating the “generalized” std error of the mean from the MSresidual (MSwg) term.
# obtain the model means. THese are the "cell means"
modmeans <- phia::interactionMeans(fit.2aov)
modmeans group age adjusted mean std. error
1 PRAISE CHILDREN 5.000000 0.5832118
2 REPROOF CHILDREN 8.714286 0.5832118
3 NONE CHILDREN 6.428571 0.5832118
4 PRAISE ADULTS 5.428571 0.5832118
5 REPROOF ADULTS 2.857143 0.5832118
6 NONE ADULTS 5.000000 0.5832118The base system plot function is aware of the interactionMeans structure and uses it to plot a graph - perhaps useful for exploratory purposes.
# plot the means
# I find this very useful for exploratory data analysis, but not publication
#plot(modmeans)
plot(modmeans, multiple=TRUE, abbrev.levels=TRUE)
We can also use the interactionMeans function to obtain marginal means.
# obtain the marginal means for the two IVs (The main effect means)
# NOTE: the marginal means produced by `interactionMeans` appear to be
# UNWEIGHTED marginal means, when the design is unbalanced (unequal N)
interactionMeans(fit.2aov, factors="group") group adjusted mean std. error
1 PRAISE 5.214286 0.412393
2 REPROOF 5.785714 0.412393
3 NONE 5.714286 0.412393interactionMeans(fit.2aov, factors="age") age adjusted mean std. error
1 CHILDREN 6.714286 0.3367175
2 ADULTS 4.428571 0.3367175testInteractions function in phiaThe testInteractions function is very powerful, but not intuitive in its argument specifications.
It yields a table of output that does have easily recognized df, SS, and F values with p values. The column in the table that is labeled “Value” is useful. If we use orthogonal contrasts (as we have done here), then the “Value” is our linear combination quantity that we have called Psi. If the default coding is used, then the “Values” are cell mean differences as would follow from dummy coding on that variable. In our particular example the rows are labeled clearly except that when using contrasts, an effect labeled “group1”, for example, is the first contrast on the “group” factor.
Now we are ready to obtain all of the effects of interest.
We want to obtain tests of main effect contrasts, interaction contrasts, simple main effects and their contrasts.
Note that the testInteractions function has an argument titled “adjustment” that can apply p-value adjustments (e.g., Sidak, etc) if needed. Those are not illustrated here.
NOTE: phia requires the contrasts in a different format than we used above
ALSO NOTE: this produces the same SS and F’s as the summary/split approach above
# create the contrasts on the treatment group factor and make them usable objects
# I used "ac" to denote an analytica contrast.
ac1 <- list(group=c(-.5, 1, -.5)) # define first contrast on factor A which is group
ac2 <- list(group=c(1,0,-1)) # define second contrast on factor A which is groupThis code chunk begins the analyses available from phia by evaluating the two main effect contrasts for the grouping factor, using the contrasts defined as “ac1” and “ac2”. These would not be of interest in the “real” analysis of this data set because there is an interaction present and because the main effect was not significant (mostly because of the former). It is included here so that the template is in place if it is needed for other data sets that use this same code sequence.
The adjustment argument permits specification of a member of the bonferroni family of error rate corrections if desired.
First, main effect contrasts on group are requested. These will be will be type III SS if unequal n is present. Recall that in this study the main effect SS for the treatment group was very small since the three means were close together.
The labeling of the tabled output is confusing. It never uses the “ac1 and”ac2” labeling. Instead each contrast on group is labeled as “group1” which just means the first contrast from that individual testInteractions statement.
testInteractions(fit.2aov, custom=ac1, adjustment="none")F Test:
P-value adjustment method: none
Value SE Df Sum of Sq F Pr(>F)
group1 0.32143 0.505 1.000 0.96429 0.405 0.5285
Residuals 36.000 85.714 testInteractions(fit.2aov, custom=ac2, adjustment="none")F Test:
P-value adjustment method: none
Value SE Df Sum of Sq F Pr(>F)
group1 -0.5 0.583 1.000 1.75 0.735 0.3969
Residuals 36.000 85.714 Note that adding the SS for the two contrasts yields the value of the SS for the main effect of group obtained above with the omnibus analyses.
.964+1.750[1] 2.714Next, we can obtain the interaction contrasts. Since “group” had two contrasts, we request a test with each of them separately. Notice that the interaction is produced by specifying the “across” argument with the other factor.
Once again the labeling of the two contrasts is the same since they are the first contrasts from the individually created testInteractions functions.
# interaction contrasts. will be type III SS if unequal N
# do their SS sum to what they should?
testInteractions(fit.2aov, custom=ac1, adjustment="none", across="age")F Test:
P-value adjustment method: none
Value SE Df Sum of Sq F Pr(>F)
group1 5.3571 1.01 1.000 66.964 28.125 5.93e-06 ***
Residuals 36.00 85.714
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1testInteractions(fit.2aov, custom=ac2, adjustment="none", across="age")F Test:
P-value adjustment method: none
Value SE Df Sum of Sq F Pr(>F)
group1 -1.8571 1.166 1.000 6.0357 2.535 0.1201
Residuals 36.000 85.714 The sum of the SS for these two interaction contrasts produces the same value for SS of the 2 df interaction term seen above.
66.964+6.036[1] 73Simple main effects are obtained with the same phia function, while passing different arguments to set the level of the factor held constant. In this case I have first asked for the simple effects of group at levels of age by using the “fixed” argument for age. The second requests the other set by fixing group.
Since “group” has three levels, each SME has two df. The error df reflect the fact that these tests each use the overall MSwg error term. Each SME of age at levels of group has 1 df since there are only two levels of age.
Group at levels of age:
testInteractions(fit.2aov, fixed="age",across="group",adjust="none")F Test:
P-value adjustment method: none
group1 group2 SE1 SE2 Df Sum of Sq F Pr(>F)
CHILDREN 3.0000 -1.42857 0.714 0.825 2 49.143 10.32 0.0002866 ***
ADULTS -2.3571 0.42857 0.714 0.825 2 26.571 5.58 0.0077467 **
Residuals 36.000 85.714
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Age at levels of group:
testInteractions(fit.2aov,fixed="group",across="age",adjust="none")F Test:
P-value adjustment method: none
Value SE Df Sum of Sq F Pr(>F)
PRAISE -0.4286 0.825 1.000 0.643 0.27 0.60651
REPROOF 5.8571 0.825 1.000 120.071 50.43 2.418e-08 ***
NONE 1.4286 0.825 1.000 7.143 3.00 0.09183 .
Residuals 36.000 85.714
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1We can double check to see that the sum of the SS for the first set of SME adds up to what it should. It should equal the pooling of the SS for the main effect of group and the interaction (just as the 2+2 df pooling equals the pooling of the df for the main effect of group and the interaction).
49.143+26.571[1] 75.714Finally, we can partition the SME of group at levels of age into their single df contrasts using the same contrasts already specified. Again, the table doesn’t use the ac1 and ac2 labels. We just recognize that the first is ac2 and the second ac2 even though they are both labeled “group1”
These would be ac1 @ children and ac1 @ adults, in that order in the first table. And then ac2@ children and adults in that order in the second table.
# now obtain sme contrasts for the group factor
testInteractions(fit.2aov, custom=ac1, adjustment="none", fixed="age")F Test:
P-value adjustment method: none
Value SE Df Sum of Sq F Pr(>F)
CHILDREN : group1 3.0000 0.714 1.000 42.000 17.64 0.0001675 ***
ADULTS : group1 -2.3571 0.714 1.000 25.929 10.89 0.0021865 **
Residuals 36.000 85.714
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1testInteractions(fit.2aov, custom=ac2, adjustment="none", fixed="age")F Test:
P-value adjustment method: none
Value SE Df Sum of Sq F Pr(>F)
CHILDREN : group1 -1.42857 0.825 1.000 7.1429 3.00 0.09183 .
ADULTS : group1 0.42857 0.825 1.000 0.6429 0.27 0.60651
Residuals 36.000 85.714
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1The conclusion here is that the testInteractions function is very flexible. All F tests match what we obtained in manual illustrations and with SPSS MANOVA analyses.
The emmeans package provides an alternate facility for performing follow up analyses to the omnibus effects in factorial designs. It’s general logic is to set a grid of descriptive stats for the groups in the design and then perform analyses using that grid. P value adjustments can also be applied to effects evaluated with emmeans. Some of those are illustrated in this section and others in the PostHoc/MultipleComparisions section that follows.
We can use the original aov model object used above to produce the omnibus analyses. Note that emmeans from the emmeans package also produces a test of the global omnibus effect of groups - treating the factorial as a 1-way design with six groups…….probably not an interesting inference.
A caveat: This section used the “fit.2aov” object where the orthogonal contrasts were employed. But it doesn’t matter which object is employed since emmeans extracts only the means and the error term MS to use in it’s inferences. We could have used the dummy-coded or effect-coded objects as well. But when it comes to unbalanced designs and marginal effects such as main effects, it is best to begin with the object that used the orthogonal contrasts. In fact, follow up analyses with dummy-coded object is not recommended for factorials.
#https://cran.r-project.org/web/packages/emmeans/vignettes/interactions.html
fit1.emm <- emmeans::emmeans(fit.2aov, c("group","age"))
fit1.emm group age emmean SE df lower.CL upper.CL
PRAISE CHILDREN 5.00 0.583 36 3.82 6.18
REPROOF CHILDREN 8.71 0.583 36 7.53 9.90
NONE CHILDREN 6.43 0.583 36 5.25 7.61
PRAISE ADULTS 5.43 0.583 36 4.25 6.61
REPROOF ADULTS 2.86 0.583 36 1.67 4.04
NONE ADULTS 5.00 0.583 36 3.82 6.18
Confidence level used: 0.95 This is the test of the whole model - all six groups as a 1way:
emmeans::test(contrast(fit1.emm, "eff"), joint=TRUE) df1 df2 F.ratio p.value note
5 36 10.968 <.0001 d
d: df1 reduced due to linear dependence emmeansSimple Main Effects of group at levels of age are found by establishing a matrix of means involving the group factor at levels of age. emmeans permits this specification with the “by” argument and the inferential tests for each of the two tables are, in fact, the 2 df simple main effects, and the results match what we saw above with phia and with our SPSS work.
fit1.emm.g <- emmeans::emmeans(fit.2aov, "group", by="age")
fit1.emm.gage = CHILDREN:
group emmean SE df lower.CL upper.CL
PRAISE 5.00 0.583 36 3.82 6.18
REPROOF 8.71 0.583 36 7.53 9.90
NONE 6.43 0.583 36 5.25 7.61
age = ADULTS:
group emmean SE df lower.CL upper.CL
PRAISE 5.43 0.583 36 4.25 6.61
REPROOF 2.86 0.583 36 1.67 4.04
NONE 5.00 0.583 36 3.82 6.18
Confidence level used: 0.95 emmeans::test(contrast(fit1.emm.g, "eff"), joint=TRUE) age df1 df2 F.ratio p.value note
CHILDREN 2 36 10.320 0.0003 d
ADULTS 2 36 5.580 0.0077 d
d: df1 reduced due to linear dependence Simple Main Effect contrasts require passing a list of those explicit contrasts to the contrast function. It works here because these contrasts are applied in the context of the “fit1.emm.g” object which already structured the effects as the effects of group at levels of age. Note that the tests employ t statistics. Squaring them gives the F’s we have found elsewhere.
lincombs_gsme <- contrast(fit1.emm.g,
list(ac1=c(-.5,1, -.5), ac2=c(1,0,-1))) # second one not changed
test(lincombs_gsme, adjust="none")age = CHILDREN:
contrast estimate SE df t.ratio p.value
ac1 3.000 0.714 36 4.200 0.0002
ac2 -1.429 0.825 36 -1.732 0.0918
age = ADULTS:
contrast estimate SE df t.ratio p.value
ac1 -2.357 0.714 36 -3.300 0.0022
ac2 0.429 0.825 36 0.520 0.6065emmeansWe can also create an emm object for the main effect means; in this case the main effect of group marginal means are requested. The pairs function permits pairwise comparison among the set of requested means. This pairwise comparison might be done using Tukey tests, as was done here, or other adjustments (“none” would give no correction for alpha inflation). One could use this pairs approach on any matrix of means derived by the emmeans function. Note that we would probably not be interested in these main effect comparisons here because of the presence of an interaction.
fit1.emm.a <- emmeans(fit.1aov, "group", data=prn.data)NOTE: Results may be misleading due to involvement in interactionspairs(fit1.emm.a, adjust="tukey") contrast estimate SE df t.ratio p.value
PRAISE - REPROOF -0.5714 0.583 36 -0.980 0.5942
PRAISE - NONE -0.5000 0.583 36 -0.857 0.6703
REPROOF - NONE 0.0714 0.583 36 0.122 0.9918
Results are averaged over the levels of: age
P value adjustment: tukey method for comparing a family of 3 estimates We can also obtain main effect contrasts for the group factor in an analogous manner.
lincombs_g <- contrast(fit1.emm.a,
list(ac1=c(-.5,1, -.5), ac2=c(1,0,-1))) # second one not changed
test(lincombs_g, adjust="none") contrast estimate SE df t.ratio p.value
ac1 0.321 0.505 36 0.636 0.5285
ac2 -0.500 0.583 36 -0.857 0.3969
Results are averaged over the levels of: age emmeansExploring interaction contrasts with emmeans involves a bit more effort. Since all of the means in the factorial are involved in interactions, we work with the grid of six means initially established above and called “fit1.emm”. In order to find the two interaction contrasts (the interaction has 2 df to be decomposed into single df terms) we need to construct contrast vectors that have six coefficients, one for each group. Please recall that we have done this elsewhere and this table should remind you of that:
Now that we have the two coefficient vectors for the interaction contrasts, we can apply them to the six-group grid of means. Notice that the order of the coefficients is important relative to the ordering of the groups in the “fit1.emm” object. This is why comparison of those means in the fit1.emm grid and the coefficients to the ordering seen in the table just above is important. The analysis reproduces estimates and t values first seen in use of the summary.lm function on the fit.2aov object and their squares match the F’s seen with the comparable phia tests (within rounding error taking these t’s to three decimal places).
lincombs_int <- contrast(fit1.emm,
list(intc1=c(-.5,1, -.5, .5, -1, .5),
intc2=c(1,0,-1, -1,0,1)))
test(lincombs_int, adjust="none") contrast estimate SE df t.ratio p.value
intc1 5.36 1.01 36 5.303 <.0001
intc2 -1.86 1.17 36 -1.592 0.1201Note that we had, perhaps more efficiently, obtained these interaction contrasts in an earlier section where we used the summary.lm function on the aov fit object. The testInteractions function in phia also provided a straight forward way to obtain them. I prefer a mix of using these three approaches in advanced designs.
Some capability for adjustment of p values for the multiple testing situation was demonstrated in the phia and emmeans sections above. The p value adjustments are especially valuable if the user needs their application in the use of contrasts. The pairwise comparison methods are typically called Multiple Comparisons. The family of these is large. This section focuses on use of the Tukey test in part because that is the most visibly apparent in the R ecosystem and in part because application of these methods is a logical challenge in factorial designs.
Consider the simple 3x2 design illustrated above in this document. There are six cells in the factorial. Control of Type I error rate inflation might consider controlling for comparisons of all possible pairs of means…… six means, therefore 15 possible pairs to compare. But not all of thes pairs will be of interest. Specifically comparisons that change levels of both factors simultaneously are not typically interesting and therefore, alpha rate control may not be necessary. This topic has been discussed by Cichetti as seen on our class work. The interesting pairwise comparisons are either within rows or within columns of the design or among one or both sets of marginals. These comprise different families of comparisons and one strategy is to control the alpha rate inflation separately within each family. The reader will recognize that comparisons within rows or within columns would be a way to follow up simple main effects described above.
This section begins with the possible use of the Tukey HSD test. Tukey’s HSD is easily accomplished on an AOV fit via a function in the base stats package that is loaded upon startup. Note that this compares all pairs of marginals and all pairs of cell means. it would severely over correct for alpha inflation with the cell means pairs since not all of the pairs of cell means are interesting comparisons.
Application of all the other post hoc comparison methods (e.g., Dunnett, REGWQ, etc) within various “families” is problematic for direct approaches to factorial designs in R. Manual computation may be the best solution, although we will return to a general strategy later once higher order factorials are covered.
The overkill “all possible pairs” approach using ‘TukeyHSD’ is shown here, although not recommended, especially for the fifteen pairwise comparisons among all six cells. Of these the only intersting comparisons would be within rows or columns. That yields a set of only nine. But the overkill comes from the fact that the TukeyHSD adjustment presumes all 15 should be figured into the p value adjustment and that is an overcorrection.
There is also a question of whether this application of TukeyHSD for main effect comparisons uses weighted or unweighted marginal means (I think it uses weighted, unfortunately)
The application of TukeyHSD here provides all pairwise comparisons among both sets of marginals (main effects) and among the six cell means. For comparison with later analysis, it would be helpful to make a note of the p-value for the first of the fifteen cell comparisons, Praise vs Reproof in Children (.0008949).
tukey <- TukeyHSD(fit.1aov, conf.level=.95)
print(tukey) Tukey multiple comparisons of means
95% family-wise confidence level
Fit: aov(formula = errors ~ group * age, data = prn.data)
$group
diff lwr upr p adj
REPROOF-PRAISE 0.57142857 -0.8541144 1.996972 0.5941713
NONE-PRAISE 0.50000000 -0.9255430 1.925543 0.6702507
NONE-REPROOF -0.07142857 -1.4969716 1.354114 0.9917663
$age
diff lwr upr p adj
ADULTS-CHILDREN -2.285714 -3.251473 -1.319955 2.76e-05
$`group:age`
diff lwr upr p adj
REPROOF:CHILDREN-PRAISE:CHILDREN 3.714286e+00 1.2328551 6.19571631 0.0008949
NONE:CHILDREN-PRAISE:CHILDREN 1.428571e+00 -1.0528592 3.91000202 0.5205520
PRAISE:ADULTS-PRAISE:CHILDREN 4.285714e-01 -2.0528592 2.91000202 0.9950433
REPROOF:ADULTS-PRAISE:CHILDREN -2.142857e+00 -4.6242877 0.33857345 0.1238955
NONE:ADULTS-PRAISE:CHILDREN 3.552714e-15 -2.4814306 2.48143060 1.0000000
NONE:CHILDREN-REPROOF:CHILDREN -2.285714e+00 -4.7671449 0.19571631 0.0857309
PRAISE:ADULTS-REPROOF:CHILDREN -3.285714e+00 -5.7671449 -0.80428369 0.0039684
REPROOF:ADULTS-REPROOF:CHILDREN -5.857143e+00 -8.3385735 -3.37571226 0.0000003
NONE:ADULTS-REPROOF:CHILDREN -3.714286e+00 -6.1957163 -1.23285512 0.0008949
PRAISE:ADULTS-NONE:CHILDREN -1.000000e+00 -3.4814306 1.48143060 0.8279471
REPROOF:ADULTS-NONE:CHILDREN -3.571429e+00 -6.0528592 -1.08999798 0.0014811
NONE:ADULTS-NONE:CHILDREN -1.428571e+00 -3.9100020 1.05285917 0.5205520
REPROOF:ADULTS-PRAISE:ADULTS -2.571429e+00 -5.0528592 -0.08999798 0.0385500
NONE:ADULTS-PRAISE:ADULTS -4.285714e-01 -2.9100020 2.05285917 0.9950433
NONE:ADULTS-REPROOF:ADULTS 2.142857e+00 -0.3385735 4.62428774 0.1238955# the plot(tukey) function gives some helpful plots that don't print well in this markdown doc.
####### plot(tukey)
# Use the next code if you want tukey test on only the main effect means:
# actually makes no sense here since we have an interaction,
# plus the main effect is negligible
#tukey2 <- TukeyHSD(fit.1aov, "group", conf.level=.95)
#tukey2A better way of approaching the question of Tukey test application would be to start with understanding where the Tukey test is to be applied. Main effect comparisons? Simple main effect comparisons (within “rows” or “columns”)? This is the question of which “family” the familywise error is to be controlled for.
In the section above, we illustrated emmeans as a way to do this for contrasts using the test function and also saw a brief application of the Tukey adjustment to comparisons among the marginal set of group means using the pairs function. I will repeat that application to the marginals here and add a way to obtain Tukey tests withing rows or columns - essentially following up on Simple Main Effects.
# comparisons among marginals for group
# first, establish the grid of means to be evaluated and then test with `pairs`
fit1.emm.a <- emmeans(fit.2aov, "group", data=prn.data)NOTE: Results may be misleading due to involvement in interactionspairs(fit1.emm.a, adjust="tukey") contrast estimate SE df t.ratio p.value
PRAISE - REPROOF -0.5714 0.583 36 -0.980 0.5942
PRAISE - NONE -0.5000 0.583 36 -0.857 0.6703
REPROOF - NONE 0.0714 0.583 36 0.122 0.9918
Results are averaged over the levels of: age
P value adjustment: tukey method for comparing a family of 3 estimates Next, evaluate within the two families defined by the two rows of the design. This implies following up on the SME of group at the two levels of age.
# Use emmeans to extract grid of means that reflect the two
# SME of group at levls of age
fit1.emm.g <- emmeans(fit.2aov, "group", by="age")
fit1.emm.gage = CHILDREN:
group emmean SE df lower.CL upper.CL
PRAISE 5.00 0.583 36 3.82 6.18
REPROOF 8.71 0.583 36 7.53 9.90
NONE 6.43 0.583 36 5.25 7.61
age = ADULTS:
group emmean SE df lower.CL upper.CL
PRAISE 5.43 0.583 36 4.25 6.61
REPROOF 2.86 0.583 36 1.67 4.04
NONE 5.00 0.583 36 3.82 6.18
Confidence level used: 0.95 And apply the Tukey test to those two sets of means. It is important to note that the p value adjustment by the tukey method (using the ptukey function) is done by the pairs function separately within the two rows - separately for children and adults. Therefore it is a “familywise” adjustment. I have not been able to find a way to make pairs do it exhaustively for all six comparisons found within the two rows, but the “familywise” approach is defensible. For comparison’s sake I also included code to provide the Sidak adjustment or no adjustment. From this we can see that the Tukey adjustment is slightly less severe than the Sidak adjustment, but both substantially increment p values over the unadjusted ones.
pairs(fit1.emm.g, adjust="tukey")age = CHILDREN:
contrast estimate SE df t.ratio p.value
PRAISE - REPROOF -3.714 0.825 36 -4.503 0.0002
PRAISE - NONE -1.429 0.825 36 -1.732 0.2073
REPROOF - NONE 2.286 0.825 36 2.771 0.0233
age = ADULTS:
contrast estimate SE df t.ratio p.value
PRAISE - REPROOF 2.571 0.825 36 3.118 0.0098
PRAISE - NONE 0.429 0.825 36 0.520 0.8623
REPROOF - NONE -2.143 0.825 36 -2.598 0.0352
P value adjustment: tukey method for comparing a family of 3 estimates pairs(fit1.emm.g, adjust="sidak")age = CHILDREN:
contrast estimate SE df t.ratio p.value
PRAISE - REPROOF -3.714 0.825 36 -4.503 0.0002
PRAISE - NONE -1.429 0.825 36 -1.732 0.2510
REPROOF - NONE 2.286 0.825 36 2.771 0.0261
age = ADULTS:
contrast estimate SE df t.ratio p.value
PRAISE - REPROOF 2.571 0.825 36 3.118 0.0107
PRAISE - NONE 0.429 0.825 36 0.520 0.9391
REPROOF - NONE -2.143 0.825 36 -2.598 0.0400
P value adjustment: sidak method for 3 tests pairs(fit1.emm.g, adjust="none")age = CHILDREN:
contrast estimate SE df t.ratio p.value
PRAISE - REPROOF -3.714 0.825 36 -4.503 0.0001
PRAISE - NONE -1.429 0.825 36 -1.732 0.0918
REPROOF - NONE 2.286 0.825 36 2.771 0.0088
age = ADULTS:
contrast estimate SE df t.ratio p.value
PRAISE - REPROOF 2.571 0.825 36 3.118 0.0036
PRAISE - NONE 0.429 0.825 36 0.520 0.6065
REPROOF - NONE -2.143 0.825 36 -2.598 0.0135Finally, note that the p value for the Tukey adjusted comparison of Praise and Reproof in Children is .0002 in this approach. The comparable p value above in the analysis that included all 15 pairwise comparisons was .0008. This verifies that the overkill approach shown first is much more conservative than the familywise approach outline here (the reader can compare other p values too).
The approach of the phia and emmeans packages from above were not included in this document when the approach below was developed. This “manual” approach does separate anovas for the two age levels, and then uses the overall MSerror from the full factorial to test the effects. It is not necessary since the other two packages handle the analysis, but perhaps the manual approach has some value for a student in the learning curve of both R and ANOVA.
In these analyses, the MS for the SME effects are derived as separately done AOV models, but their MS are correct. This approach is a bit of a kludge. R doesn’t make direct partitioning of SS easy. To complete this we would still need to partition the SME into their contrasts, but this was already done above with emmeans and phia.
In part, I show this approach because I have seen recommendations that SME be evaluated by doing this kind of “subsetting” - separate ANOVAs on each level of the “other” factor. It is a poor way of doing SME analysis since it does not use the overall error term from the full factorial. That is why I showed the kludge here, but manually computed the F values using MSe from the full factorial.
dfresid <-anova(fit.1aov)[4,1]
dfresid[1] 36MSresid <-anova(fit.1aov)[4,3]
MSresid[1] 2.380952# subset data on age to two new dataframes
prn.data.child <- subset(prn.data,age=="CHILDREN",select=group:errors)
prn.data.adult <- subset(prn.data,age=="ADULTS",select=group:errors)
#prn.data.child
#prn.data.adult
fit.smechild <- aov(errors~group,data=prn.data.child)
df.group.child <-anova(fit.smechild)[1,1]
df.group.child[1] 2ms.group.child <- anova(fit.smechild)[1,3]
ms.group.child[1] 24.57143f.sme.group_at_child <- ms.group.child/MSresid
f.sme.group_at_child[1] 10.32pf(f.sme.group_at_child,df.group.child,dfresid,lower.tail=F)[1] 0.0002865673# only did it with effect of group at child. group at adult can be done the same wayThe ezAnova ez package was designed to simplify ANOVA types of analyses. The extension to 2way designs is best seen by going back to where we used it for 1-ways and to compare the code.
First, rework the data frame so that a subject/case number variable is present, and describe the data set.
#require(ez)
# in order to use ez functions we need a subject number variable in the data frame
snum <- ordered(rownames(prn.data)) #arbitrary snum values
prn2.data <- cbind(snum,prn.data)
# and look at a summary of the dataframe
ez::ezPrecis(prn2.data)Data frame dimensions: 42 rows, 4 columns type missing values min max
snum factor 0 42 1 9
group factor 0 3 PRAISE NONE
age factor 0 2 CHILDREN ADULTS
errors numeric 0 10 1 10Next obtain descriptive statistics. Note that FLSD is Fisher’s LSD critical value. Also note that Fisher’s Least Significant Difference Threshold is computed as qt(.975,DFerror)sqrt(2MSerror/N), where N is taken as the mean N per group in cases of unbalanced designs. We can check this computation before working with *ez**. But realize that FLSD approach is a pairwise comparison approach among all six means, an approach that may not be desireable and the LSD test is also not without heavy criticism as a method of controlling Type I error rates.
MSerror <- fit.1SS["Residuals", "Mean Sq"] # from work above in this doc
DFerror <- 36
N <- 7
qt(.975,DFerror)*sqrt((2*MSerror)/N)[1] 1.672744descript1 <- ez::ezStats(
data = prn2.data,
dv = .(errors),
wid= .(snum),
between= .(group,age))Coefficient covariances computed by hccm()print(descript1) group age N Mean SD FLSD
1 PRAISE CHILDREN 7 5.000000 1.414214 1.672744
2 PRAISE ADULTS 7 5.428571 1.718249 1.672744
3 REPROOF CHILDREN 7 8.714286 1.112697 1.672744
4 REPROOF ADULTS 7 2.857143 1.345185 1.672744
5 NONE CHILDREN 7 6.428571 1.511858 1.672744
6 NONE ADULTS 7 5.000000 2.000000 1.672744Next, we can use ez to draw a figure that is of the standard type. It uses ggplot2.
# now plot the cell means and error bars.
# error bars based on GSEM as described above for Fishers LSD
plot1 <- ez::ezPlot(
data = prn2.data,
dv = .(errors),
wid= .(snum),
between= .(group,age),
split=.(age),
x = .(group),
do_lines=FALSE, do_bars=TRUE,
x_lab= 'Treatment Group',
y_lab= 'Mean DV Score')Coefficient covariances computed by hccm()print(plot1)
Now fit the omnibus model. The ezANOVA function produces effect sizes called “generalized” effect sizes - see the Bakeman 2005 article for details. In our equal N, completely randomized two-factor design, the ges will equal the partial eta squareds we have seen above.
# now do the 2way ANOVA
#
# note that ezANOVA also does a Levene test of
# the homogeneity of variance assumption (median centered!!)
# it also does type III SS, if requested as I did here.
fit1.ez <- ez::ezANOVA(
data = prn2.data,
dv = .(errors),
wid= .(snum),
between= .(group,age),
detailed=TRUE,
type=3)Coefficient covariances computed by hccm()print(fit1.ez)$ANOVA
Effect DFn DFd SSn SSd F p p<.05 ges
1 (Intercept) 1 36 1303.714286 85.71429 547.56 2.279069e-23 * 0.93830969
2 group 2 36 2.714286 85.71429 0.57 5.705462e-01 0.03069467
3 age 1 36 54.857143 85.71429 23.04 2.763958e-05 * 0.39024390
4 group:age 2 36 73.000000 85.71429 15.33 1.527107e-05 * 0.45994599
$`Levene's Test for Homogeneity of Variance`
DFn DFd SSn SSd F p p<.05
1 5 36 2 41.14286 0.35 0.8788518 ezDesign gives a snapshot of the location of the cases in the data file.
ez::ezDesign(
data = prn2.data,
x= .(group),
y= .(snum),
row=NULL,col=.(age),
cell_border_size=8)
The ez package also has facilities for doing permutation testing and bootstrapping. I show the code here but suppress the extensive output to save space.
# careful. ezPerm may not handle the interaction well.
# it is probably ok for main effects. monitor later versions of
# the package to see if an improved version exists.
fit2.ezperm <- ez::ezPerm(
data = prn2.data,
dv = .(errors),
wid= .(snum),
between= .(group,age),
perm= 1e3)
print(fit2.ezperm)# now do a bootstrap approach
ezboot1 <- ez::ezBoot(
data = prn2.data,
dv = .(errors),
wid= .(snum),
between= .(group,age),
resample_within = FALSE,
iterations = 1e2, # use ie3 or higher for publication
lmer = FALSE,
alarm = TRUE)
plot2ezboot <- ez::ezPlot2(
ezboot1,x=.(group),split=.(age))
print(plot2ezboot)The afex package provides a fairly direct set of methods for obtaining ANOVA analyses of a variety of types. Its goal is to simplify the analysis and to provide information above any beyond what the base system aov, anova and summary.lm functions provide - including effect size estimates. It is also designed to work with code structures that mimic one of three approaches to linear modeling that users might already be familiar with. One is similar to model specification with lm and utilizes car facilities to produce Type III SS tests by default. Others mimic model specification with the ez package functions and lme4 package functions. These differences are for convenience of the user for code writing. The output is the same for all.
The illustrations here use a data frame that contains a subject/case identifier. We created such a data frame for use with the ez package functions.
The three code chunks here implement the three different approaches. Notice that the ANOVA summary table is the same for all three. Note that the default is production of TYPE III SS and tests, but that can be changed with a “type” argument that permits change to TYPE II SS and tests since it uses the car::Anova function which does the same.
fit1.afex <- afex::aov_car(errors ~ group*age + Error(snum),
data=prn2.data)Contrasts set to contr.sum for the following variables: group, agefit1.afexAnova Table (Type 3 tests)
Response: errors
Effect df MSE F ges p.value
1 group 2, 36 2.38 0.57 .031 .571
2 age 1, 36 2.38 23.04 *** .390 <.001
3 group:age 2, 36 2.38 15.33 *** .460 <.001
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1We could look at partial eta squareds for this model and they are the same as the GES:
effectsize(fit1.afex, partial=TRUE, generalized=FALSE)# Effect Size for ANOVA (Type III)
Parameter | Eta2 (partial) | 95% CI
-----------------------------------------
group | 0.03 | [0.00, 1.00]
age | 0.39 | [0.19, 1.00]
group:age | 0.46 | [0.25, 1.00]
- One-sided CIs: upper bound fixed at [1.00].afex::aov_ez(id="snum",
dv="errors",
between = c("group", "age"),
data=prn2.data)Contrasts set to contr.sum for the following variables: group, ageAnova Table (Type 3 tests)
Response: errors
Effect df MSE F ges p.value
1 group 2, 36 2.38 0.57 .031 .571
2 age 1, 36 2.38 23.04 *** .390 <.001
3 group:age 2, 36 2.38 15.33 *** .460 <.001
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1afex::aov_4(errors ~ group*age + (1|snum),
data=prn2.data)Contrasts set to contr.sum for the following variables: group, ageAnova Table (Type 3 tests)
Response: errors
Effect df MSE F ges p.value
1 group 2, 36 2.38 0.57 .031 .571
2 age 1, 36 2.38 23.04 *** .390 <.001
3 group:age 2, 36 2.38 15.33 *** .460 <.001
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '+' 0.1 ' ' 1Note that the p values are rounded to three decimal places. More precise values can be found by passing the df and F values to the pF function.
For all three afex models, the results are the same, including the GES. But see the next section…..
emmeans on afex objects for followup analysesThese three afex functions are designed to work well with the emmeans package functions reviewed earlier in this document. If, for example an aov_car function is saved as an object, emmeans can operate on that object to produce the usable grid of means that we saw employed in earlier sections in this document.
fit1.afexcar <- afex::aov_car(errors ~ group*age + Error(snum),
data=prn2.data)Contrasts set to contr.sum for the following variables: group, ageemmgrid.g_a <- emmeans::emmeans(fit1.afexcar, ~group*age)
emmgrid.g_a group age emmean SE df lower.CL upper.CL
PRAISE CHILDREN 5.00 0.583 36 3.82 6.18
REPROOF CHILDREN 8.71 0.583 36 7.53 9.90
NONE CHILDREN 6.43 0.583 36 5.25 7.61
PRAISE ADULTS 5.43 0.583 36 4.25 6.61
REPROOF ADULTS 2.86 0.583 36 1.67 4.04
NONE ADULTS 5.00 0.583 36 3.82 6.18
Confidence level used: 0.95 Then, followup work could be done with that grid. For example, here are the interaction contrasts that we worked with previously - except I illustrate how to use a bonferroni p value adjustment for the two tests.
lincombs_int2 <- emmeans::contrast(emmgrid.g_a,
list(intc1=c(-.5,1, -.5, .5, -1, .5),
intc2=c(1,0,-1, -1,0,1)))
emmeans::test(lincombs_int2, adjust="bonf") contrast estimate SE df t.ratio p.value
intc1 5.36 1.01 36 5.303 <.0001
intc2 -1.86 1.17 36 -1.592 0.2402
P value adjustment: bonferroni method for 2 tests These are the same two t value outcomes we saw with the test function and an emmeans grid in the earlier section (except for the p value bonferroni adjustments).
A brief introduction to obtaining BayesFactor modeling here shows how simple it is to do the initial modeling. The anovaBF function from the BayesFactor package, permits assessment of Bayes Factors comparing various models to an intercept-only model. Thus each model considered leaves out one or more effects. The full rank model with both main effects and the interaction has the largest BF, by far, and this is not surprising given what we already know about the data set. With further theoretical work, one can understand how to take ratios of these BF to compare models.
More detailed approaches to Bayesian inference of factorial designs can be done later after theoretical/conceptual development of Bayesian methods is covered.
All of the BF functions in this section come from the BayesFactor package
BayesFactor::anovaBF(errors ~ group*age, data=prn2.data)Bayes factor analysis
--------------
[1] group : 0.2040232 ±0.01%
[2] age : 43.23484 ±0%
[3] group + age : 9.019154 ±1.19%
[4] group + age + group:age : 12651.32 ±2.84%
Against denominator:
Intercept only
---
Bayes factor type: BFlinearModel, JZSBayesFactor::lmBF(errors~ group + age + group:age, data=prn2.data)Bayes factor analysis
--------------
[1] group + age + group:age : 12854.08 ±2.39%
Against denominator:
Intercept only
---
Bayes factor type: BFlinearModel, JZSBayesFactor::generalTestBF(errors~ group + age + group:age, data=prn2.data)Bayes factor analysis
--------------
[1] group : 0.2040232 ±0.01%
[2] age : 43.23484 ±0%
[3] group + age : 9.204724 ±1.89%
[4] group + age + group:age : 12464.59 ±1.42%
Against denominator:
Intercept only
---
Bayes factor type: BFlinearModel, JZSThis result indicates that the full rank interaction model is supported best, a result that is not surprising for this simple data set.
In order to examine contrasts, in a BF framework, we need to first create five variables for the main effect contrasts and interaction contrasts. Then we can use a gene
In order to avoid lengthening this document, I previously created the coding vectors and included them in a different .csv file. It is useful to examine the whole data frame here.
prn2coding.data <- read.csv("2way_coding.csv", stringsAsFactors=TRUE)
#psych::headTail(prn2coding.data)
knitr::kable(prn2coding.data)| group | age | errors | gc1 | gc2 | age1 | gc1byage | gc2byage |
|---|---|---|---|---|---|---|---|
| PRAISE | CHILDREN | 4 | -1 | 1 | 1 | -1 | 1 |
| PRAISE | CHILDREN | 7 | -1 | 1 | 1 | -1 | 1 |
| PRAISE | CHILDREN | 5 | -1 | 1 | 1 | -1 | 1 |
| PRAISE | CHILDREN | 3 | -1 | 1 | 1 | -1 | 1 |
| PRAISE | CHILDREN | 6 | -1 | 1 | 1 | -1 | 1 |
| PRAISE | CHILDREN | 4 | -1 | 1 | 1 | -1 | 1 |
| PRAISE | CHILDREN | 6 | -1 | 1 | 1 | -1 | 1 |
| REPROOF | CHILDREN | 8 | 2 | 0 | 1 | 2 | 0 |
| REPROOF | CHILDREN | 9 | 2 | 0 | 1 | 2 | 0 |
| REPROOF | CHILDREN | 10 | 2 | 0 | 1 | 2 | 0 |
| REPROOF | CHILDREN | 8 | 2 | 0 | 1 | 2 | 0 |
| REPROOF | CHILDREN | 9 | 2 | 0 | 1 | 2 | 0 |
| REPROOF | CHILDREN | 7 | 2 | 0 | 1 | 2 | 0 |
| REPROOF | CHILDREN | 10 | 2 | 0 | 1 | 2 | 0 |
| NONE | CHILDREN | 8 | -1 | -1 | 1 | -1 | -1 |
| NONE | CHILDREN | 4 | -1 | -1 | 1 | -1 | -1 |
| NONE | CHILDREN | 7 | -1 | -1 | 1 | -1 | -1 |
| NONE | CHILDREN | 5 | -1 | -1 | 1 | -1 | -1 |
| NONE | CHILDREN | 8 | -1 | -1 | 1 | -1 | -1 |
| NONE | CHILDREN | 6 | -1 | -1 | 1 | -1 | -1 |
| NONE | CHILDREN | 7 | -1 | -1 | 1 | -1 | -1 |
| PRAISE | ADULTS | 2 | -1 | 1 | -1 | 1 | -1 |
| PRAISE | ADULTS | 5 | -1 | 1 | -1 | 1 | -1 |
| PRAISE | ADULTS | 7 | -1 | 1 | -1 | 1 | -1 |
| PRAISE | ADULTS | 6 | -1 | 1 | -1 | 1 | -1 |
| PRAISE | ADULTS | 5 | -1 | 1 | -1 | 1 | -1 |
| PRAISE | ADULTS | 7 | -1 | 1 | -1 | 1 | -1 |
| PRAISE | ADULTS | 6 | -1 | 1 | -1 | 1 | -1 |
| REPROOF | ADULTS | 2 | 2 | 0 | -1 | -2 | 0 |
| REPROOF | ADULTS | 3 | 2 | 0 | -1 | -2 | 0 |
| REPROOF | ADULTS | 4 | 2 | 0 | -1 | -2 | 0 |
| REPROOF | ADULTS | 5 | 2 | 0 | -1 | -2 | 0 |
| REPROOF | ADULTS | 1 | 2 | 0 | -1 | -2 | 0 |
| REPROOF | ADULTS | 2 | 2 | 0 | -1 | -2 | 0 |
| REPROOF | ADULTS | 3 | 2 | 0 | -1 | -2 | 0 |
| NONE | ADULTS | 4 | -1 | -1 | -1 | 1 | 1 |
| NONE | ADULTS | 3 | -1 | -1 | -1 | 1 | 1 |
| NONE | ADULTS | 8 | -1 | -1 | -1 | 1 | 1 |
| NONE | ADULTS | 6 | -1 | -1 | -1 | 1 | 1 |
| NONE | ADULTS | 4 | -1 | -1 | -1 | 1 | 1 |
| NONE | ADULTS | 3 | -1 | -1 | -1 | 1 | 1 |
| NONE | ADULTS | 7 | -1 | -1 | -1 | 1 | 1 |
Now we can submit those six coding vectors as IVs in the regression modeling permitted by regressionBF. The result is an extensive listing of all combinations of variables in the model, many of which would be uninteresting. We first must emphasize that any model that has a higher order effect (interaction contrast) should also have the lower order components of that interaction - the marginality principle. This eliminates quite a few models from consideration.
set.seed(1415)
mod2BF <- BayesFactor::regressionBF(errors ~ gc1 + gc2 + age1 + gc1byage + gc2byage, data=prn2coding.data)
mod2BFBayes factor analysis
--------------
[1] gc1 : 0.3255552 ±0%
[2] gc2 : 0.3453192 ±0%
[3] age1 : 43.23484 ±0.01%
[4] gc1byage : 173.492 ±0%
[5] gc2byage : 0.4788822 ±0%
[6] gc1 + gc2 : 0.1567661 ±0.01%
[7] gc1 + age1 : 14.13678 ±0%
[8] gc1 + gc1byage : 52.35517 ±0%
[9] gc1 + gc2byage : 0.2122527 ±0.01%
[10] gc2 + age1 : 15.33147 ±0%
[11] gc2 + gc1byage : 57.26007 ±0%
[12] gc2 + gc2byage : 0.2245944 ±0.01%
[13] age1 + gc1byage : 142666.9 ±0%
[14] age1 + gc2byage : 24.08401 ±0%
[15] gc1byage + gc2byage : 94.31213 ±0%
[16] gc1 + gc2 + age1 : 6.156068 ±0%
[17] gc1 + gc2 + gc1byage : 21.39544 ±0.01%
[18] gc1 + gc2 + gc2byage : 0.1161826 ±0%
[19] gc1 + age1 + gc1byage : 38163.8 ±0%
[20] gc1 + age1 + gc2byage : 9.430086 ±0%
[21] gc1 + gc1byage + gc2byage : 34.33434 ±0.01%
[22] gc2 + age1 + gc1byage : 44138.43 ±0%
[23] gc2 + age1 + gc2byage : 10.21405 ±0%
[24] gc2 + gc1byage + gc2byage : 37.51754 ±0.01%
[25] age1 + gc1byage + gc2byage : 100027.9 ±0%
[26] gc1 + gc2 + age1 + gc1byage : 13763.27 ±0%
[27] gc1 + gc2 + age1 + gc2byage : 4.565146 ±0%
[28] gc1 + gc2 + gc1byage + gc2byage : 15.69602 ±0%
[29] gc1 + age1 + gc1byage + gc2byage : 30357.08 ±0%
[30] gc2 + age1 + gc1byage + gc2byage : 35261.01 ±0%
[31] gc1 + gc2 + age1 + gc1byage + gc2byage : 11990.66 ±0%
Against denominator:
Intercept only
---
Bayes factor type: BFlinearModel, JZSWith the which.max function we can find the model that has the largest BF.
which.max(mod2BF)age1 + gc1byage
13 But this model violates the marginality principle since it does not contain both lower order terms of the two components of the interaction contrast gc1byage. So, we can redo the analysis, sorting the models by size of BF to find valid models with the highest BF.
head(mod2BF, n=8)Bayes factor analysis
--------------
[1] age1 + gc1byage : 142666.9 ±0%
[2] age1 + gc1byage + gc2byage : 100027.9 ±0%
[3] gc2 + age1 + gc1byage : 44138.43 ±0%
[4] gc1 + age1 + gc1byage : 38163.8 ±0%
[5] gc2 + age1 + gc1byage + gc2byage : 35261.01 ±0%
[6] gc1 + age1 + gc1byage + gc2byage : 30357.08 ±0%
[7] gc1 + gc2 + age1 + gc1byage : 13763.27 ±0%
[8] gc1 + gc2 + age1 + gc1byage + gc2byage : 11990.66 ±0%
Against denominator:
Intercept only
---
Bayes factor type: BFlinearModel, JZSFrom this listing, only two models from this list do not violate the marginality principle, models 4 and 8. Model eight is the full rank model.
As an illustration of how to proceed, it is useful to compare model 4 in this highest ranking list with a model that omits one term. This will provide a way to evaluate the importance of a single term, which is equivalent to a single df contrast assessment. For our purposes lets compare model 4 (“gc1 + age1 + gc1byage”) to a model that has only the two main effect contrasts. This effectively permits evaluation of the importance of the first interaction contrast (gc1byage). The lmBF function permits assessment of only the one specified model, not all combinations as regressionBF did.
mod3BFa <- BayesFactor::lmBF(errors ~ gc1 + age1 + gc1byage,
data=prn2coding.data)
mod3BFb <- BayesFactor::lmBF(errors ~ gc1 +age1,
data=prn2coding.data)
mod3BFa/mod3BFbBayes factor analysis
--------------
[1] gc1 + age1 + gc1byage : 2699.609 ±0%
Against denominator:
errors ~ gc1 + age1
---
Bayes factor type: BFlinearModel, JZSThis value can be interpreted that the model that includes the first interaction contrast is has nearly 2700 times more support from the data than the model that lacks that that contrast. This is consistent with our earlier NHST and SS partitioning approach that found that the first interaction contrast was the most important effect in the model. The partial eta squared for that first interaction contrast was .46, a substantially large effect size and consistent with the visual impression of the data set seen in EDA work early in this document.
A Caveat: This rudimentary Bayesian approach can be extended in many ways that are outside the scope of this document. Considerations about choice of the prior distributions have not been considered and are one critical element left out. But also note that simple main effect analyses can also be accomplished in a similar manner by creation of simple main effect contrast coding vectors and including them in an orthogonal set of contrasts.
Rather than introduce a completely new data set for a brief examination of some consequences of an unbalanced design, I will use the same data set with artificially created unequal sample sizes. I have arbitrarily deleted the first case from the original prn.data set, a case in the Praise-Children group. A few code chunks are used to demonstrate differences between Type I and Type III SS approaches and comparative use of the “split” argument vs summary.lm. Since the data set is different, we cannot compare the outcomes with prior analyses done above. The goal is the comparison of different methods of analysis that converged on the same outcomes in analyses with the balanced, equal N, design.
The data are in a newly created .csv file.
prn3.data <- read.csv("2way_base_unbalanced.csv", stringsAsFactors=TRUE)In order to eliminate confusion with the “attached” data set used earlier, these code chunks always use the df$variable naming specification for this set of analyses, where necessary and the data frame cannot be specified within the function.
Since the levels of factors read by read.csv will be alphabetical, by default, we need to change them with this imported data frame to match the order worked with in all of the analyses in prior sections. First for the “group” variable.
# check the imported data frame order
levels(prn3.data$group)[1] "NONE" "PRAISE" "REPROOF"prn3.data$group <- factor(prn3.data$group,levels(prn3.data$group)[c(2,3,1)])
levels(prn3.data$group)[1] "PRAISE" "REPROOF" "NONE" Next, reorder the age variable
# check the imported data frame order
levels(prn3.data$age)[1] "ADULTS" "CHILDREN"prn3.data$age <- factor(prn3.data$age,levels(prn3.data$age)[c(2,1)])
levels(prn3.data$age)[1] "CHILDREN" "ADULTS" We also need to specify the same set of orthogonal/analytical contrasts that we have been using all along.
contrasts.group <- matrix(c(-1,2,-1,1,0,-1),ncol=2)
contrasts(prn3.data$group) <- contrasts.group
contrasts(prn3.data$group) [,1] [,2]
PRAISE -1 1
REPROOF 2 0
NONE -1 -1It is helpful, for consistency, to change the age factor as well even though it only has two levels and therefore one contrast.
contrasts.age <- matrix(c(1,-1),ncol=1)
contrasts(prn3.data$age) <- contrasts.age
contrasts(prn3.data$age) [,1]
CHILDREN 1
ADULTS -1We can examine the means of the six groups and we see that only group Praise-Children has changed, with the diminished sample size.
db1 <- psych::describeBy(prn3.data$errors,list(prn3.data$group,prn3.data$age),mat=TRUE,type=2, digits=3)
db1 item group1 group2 vars n mean sd median trimmed mad min max range
X11 1 PRAISE CHILDREN 1 6 5.167 1.472 5.5 5.167 1.483 3 7 4
X12 2 REPROOF CHILDREN 1 7 8.714 1.113 9.0 8.714 1.483 7 10 3
X13 3 NONE CHILDREN 1 7 6.429 1.512 7.0 6.429 1.483 4 8 4
X14 4 PRAISE ADULTS 1 7 5.429 1.718 6.0 5.429 1.483 2 7 5
X15 5 REPROOF ADULTS 1 7 2.857 1.345 3.0 2.857 1.483 1 5 4
X16 6 NONE ADULTS 1 7 5.000 2.000 4.0 5.000 1.483 3 8 5
skew kurtosis se
X11 -0.418 -0.859 0.601
X12 -0.249 -0.944 0.421
X13 -0.620 -0.809 0.571
X14 -1.487 2.666 0.649
X15 0.352 -0.302 0.508
X16 0.525 -1.550 0.756aov object for this 2-way factorial on the unbalanced data setHere, we create the core model with aov and compare the summary tables produced by anova and Anova:
fit.4aov <- aov(errors ~ group*age, data=prn3.data)
anova(fit.4aov)Analysis of Variance Table
Response: errors
Df Sum Sq Mean Sq F value Pr(>F)
group 2 1.773 0.886 0.3669 0.6955
age 1 59.410 59.410 24.5939 1.814e-05 ***
group:age 2 68.026 34.013 14.0802 3.261e-05 ***
Residuals 35 84.548 2.416
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1car::Anova(fit.4aov, type=3)Anova Table (Type III tests)
Response: errors
Sum Sq Df F value Pr(>F)
(Intercept) 1281.16 1 530.3587 < 2.2e-16 ***
group 1.85 2 0.3819 0.6853
age 56.00 1 23.1825 2.803e-05 ***
group:age 68.03 2 14.0802 3.261e-05 ***
Residuals 84.55 35
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1Note that the Type III SS changed. This is why it is recommended to use “sum to zero” contrasts with ANOVA (contr.sum or effect coding, or orthogonal coding as we have done). The dummy coded variables produce somewhat uninterpretable outcomes with factorial designs that are unbalanced.
summary.lmThe most important comparison to be made here is the comparison of the methods for evaluation of the main effect and interaction contrasts. Two methods we used were the inclusion of the “split” argument in the anova function and use of the summary.lm function.
attach(prn3.data)
summary(fit.4aov, split=list(group=list(group.ac1=1, group.ac2=2))) Df Sum Sq Mean Sq F value Pr(>F)
group 2 1.77 0.89 0.367 0.696
group: group.ac1 1 0.66 0.66 0.272 0.605
group: group.ac2 1 1.11 1.11 0.461 0.501
age 1 59.41 59.41 24.594 1.81e-05 ***
group:age 2 68.03 34.01 14.080 3.26e-05 ***
group:age: group.ac1 1 63.22 63.22 26.173 1.13e-05 ***
group:age: group.ac2 1 4.80 4.80 1.987 0.167
Residuals 35 84.55 2.42
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1summary.lm(fit.4aov)
Call:
aov(formula = errors ~ group * age, data = prn3.data)
Residuals:
Min 1Q Median 3Q Max
-3.4286 -1.0000 0.1429 1.1429 3.0000
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 5.59921 0.24313 23.030 < 2e-16 ***
group1 0.09325 0.17075 0.546 0.588
group2 -0.20833 0.29978 -0.695 0.492
age1 1.17063 0.24313 4.815 2.80e-05 ***
group1:age1 0.87897 0.17075 5.148 1.03e-05 ***
group2:age1 -0.42262 0.29978 -1.410 0.167
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Residual standard error: 1.554 on 35 degrees of freedom
Multiple R-squared: 0.6045, Adjusted R-squared: 0.548
F-statistic: 10.7 on 5 and 35 DF, p-value: 2.7e-06First, realize that the F tests for the omnibus main effects and interactions produced in the “split” table match what was produced by anova, using Type I SS.
When we did this comparison with the balanced design above, we found that the t-tests from summary.lm were the exact square roots of the F values produced from summary when “split” was used. Now they don’t match. The conclusion is that use of the “split” technique in summary on an aov object produces Type I SS and summary.lm on the same object produces individual regression coefficients whose t-tests are equivalent to Type III SS tests. Therefore, use of the “split” approach is generally not recommended for experimental designs where Type III SS are recommended because they produce tests of unweighted marginal means.
At this point, it is helpful to recall that for Type III tests, each vector is treated as if it were the last to enter the model, thus accounting for only unique variation available at that point. In Type I models each variable accounts for variation available at the stage it enters the model and the order is implied by the listing in the model statement. So for the Type I table, group (with its two contrasts) is entered first, age second, and the interaction third because of the “group*age” specification in the model statement for fit.4aov.
In order to bring another more comparison into the mix, we will now obtain the same contrasts using the approach we worked through previously with the phia package.
## NOTE: **phia** requires the contrasts in a different format than we used above
# create the contrasts on the treatment group factor and make them usable objects
ac1 <- list(group=c(-.5, 1, -.5)) # define first contrast on factor A which is group
ac2 <- list(group=c(1,0,-1)) # define second contrast on factor A which is groupEvaluate the two main effect contrasts
# First, main effect contrasts on group.
# These will be will be type III SS if unequal N
phia::testInteractions(fit.4aov, custom=ac1, adjustment="none")Warning in rbind(deparse.level, ...): number of columns of result, 6, is not a
multiple of vector length 5 of arg 2F Test:
P-value adjustment method: none
Value SE Df Sum of Sq F Pr(>F)
group1 0.27976 0.512 1.000 0.72048 0.2983 0.5884
Residuals 35.000 84.548 phia::testInteractions(fit.4aov, custom=ac2, adjustment="none")Warning in rbind(deparse.level, ...): number of columns of result, 6, is not a
multiple of vector length 5 of arg 2F Test:
P-value adjustment method: none
Value SE Df Sum of Sq F Pr(>F)
group1 -0.41667 0.6 1.000 1.1667 0.483 0.4917
Residuals 35.0 84.548 Now Interaction contrasts:
# interaction contrasts. will be type III SS if unequal N
# do their SS sum to what they should?
phia::testInteractions(fit.4aov, custom=ac1, adjustment="none", across="age")Warning in rbind(deparse.level, ...): number of columns of result, 6, is not a
multiple of vector length 5 of arg 2F Test:
P-value adjustment method: none
Value SE Df Sum of Sq F Pr(>F)
group1 5.2738 1.025 1.000 64.008 26.497 1.027e-05 ***
Residuals 35.000 84.548
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1phia::testInteractions(fit.4aov, custom=ac2, adjustment="none", across="age")Warning in rbind(deparse.level, ...): number of columns of result, 6, is not a
multiple of vector length 5 of arg 2F Test:
P-value adjustment method: none
Value SE Df Sum of Sq F Pr(>F)
group1 -1.6905 1.199 1.000 4.801 1.9874 0.1674
Residuals 35.000 84.548 The square roots of the F values of each of these four contrasts equal the t values from the summary.lm table for the “fit.4aov” object. We can conclude that the phia testInteractions function uses Type III SS.
A brief examination of the main effect contrasts for the group factor using emmeans reveals that it too employs Type III SS approaches.
First, create the grid of means for the group main effect, collapsing on age.
fit4.emm.a <- emmeans::emmeans(fit.4aov, "group", data=prn3.data)NOTE: Results may be misleading due to involvement in interactionsAnd here, we can create the same contrasts used all along for the group factor and
lincombs_g4 <- emmeans::contrast(fit4.emm.a,
list(ac1=c(-.5,1, -.5), ac2=c(1,0,-1))) # second one not changed
emmeans::test(lincombs_g4, adjust="none") contrast estimate SE df t.ratio p.value
ac1 0.280 0.512 35 0.546 0.5884
ac2 -0.417 0.600 35 -0.695 0.4917
Results are averaged over the levels of: age These t values match those from the summary.lm approach which we concluded are Type III SS based. The same will be true for interaction contrasts, so that illustration is not included for the sake of brevity.
Next, we examine the interaction contrasts. The results match what we derived using summary.lm and phia.
fit4.emm <- emmeans::emmeans(fit.4aov, c("group","age"),data=prn3.data)
fit4.emm group age emmean SE df lower.CL upper.CL
PRAISE CHILDREN 5.17 0.635 35 3.88 6.45
REPROOF CHILDREN 8.71 0.587 35 7.52 9.91
NONE CHILDREN 6.43 0.587 35 5.24 7.62
PRAISE ADULTS 5.43 0.587 35 4.24 6.62
REPROOF ADULTS 2.86 0.587 35 1.66 4.05
NONE ADULTS 5.00 0.587 35 3.81 6.19
Confidence level used: 0.95 lincombs_int <- emmeans::contrast(fit4.emm,
list(intc1=c(-.5,1, -.5, .5, -1, .5),
intc2=c(1,0,-1, -1,0,1)))
emmeans::test(lincombs_int, adjust="none") contrast estimate SE df t.ratio p.value
intc1 5.27 1.02 35 5.148 <.0001
intc2 -1.69 1.20 35 -1.410 0.1674The simple main effect approach is also the same as for the equal N design. The approach for simple main effect contrasts is also identical to that shown above for the equal N design, so is omitted here.
fit4.emm.g <- emmeans::emmeans(fit.4aov, "group", by="age")
fit4.emm.gage = CHILDREN:
group emmean SE df lower.CL upper.CL
PRAISE 5.17 0.635 35 3.88 6.45
REPROOF 8.71 0.587 35 7.52 9.91
NONE 6.43 0.587 35 5.24 7.62
age = ADULTS:
group emmean SE df lower.CL upper.CL
PRAISE 5.43 0.587 35 4.24 6.62
REPROOF 2.86 0.587 35 1.66 4.05
NONE 5.00 0.587 35 3.81 6.19
Confidence level used: 0.95 emmeans::test(emmeans::contrast(fit4.emm.g, "eff"), joint=TRUE) age df1 df2 F.ratio p.value note
CHILDREN 2 35 8.812 0.0008 d
ADULTS 2 35 5.500 0.0084 d
d: df1 reduced due to linear dependence A variety of methods exist for handling Multiple comparison tests in unequal N designs. Most of those post hoc tests are not well adapted and user friendly (or even findable) for use in factorial designs with the R ecosystem. The Tukey test can, however, be used with confidence for unequal N designs using the same emmeans approach see above for the equal N design.
The computation approach is to compute a “t” test statistic using the same methods that a standard independent samples t-test uses when there is unequal N, but using the overall MSe from the ANOVA rather than error from only the two groups being tested. This test statistic value is then passed to the ptukey function in order to obtain the Tukey adjusted p value. This approach is exactly what is found with the functions in the emmeans package and is employed identically to how it was above with the equal N design. The illustration here is for the effect of the grouping/treatment variable within each row, separately - thus following up on the two families of SME of group at levels of age.
The Sidak and unadjusted p-values are also provided for comparison.
pairs(fit4.emm.g, adjust="tukey")age = CHILDREN:
contrast estimate SE df t.ratio p.value
PRAISE - REPROOF -3.548 0.865 35 -4.103 0.0007
PRAISE - NONE -1.262 0.865 35 -1.459 0.3225
REPROOF - NONE 2.286 0.831 35 2.751 0.0247
age = ADULTS:
contrast estimate SE df t.ratio p.value
PRAISE - REPROOF 2.571 0.831 35 3.095 0.0105
PRAISE - NONE 0.429 0.831 35 0.516 0.8641
REPROOF - NONE -2.143 0.831 35 -2.579 0.0370
P value adjustment: tukey method for comparing a family of 3 estimates pairs(fit4.emm.g, adjust="sidak")age = CHILDREN:
contrast estimate SE df t.ratio p.value
PRAISE - REPROOF -3.548 0.865 35 -4.103 0.0007
PRAISE - NONE -1.262 0.865 35 -1.459 0.3932
REPROOF - NONE 2.286 0.831 35 2.751 0.0278
age = ADULTS:
contrast estimate SE df t.ratio p.value
PRAISE - REPROOF 2.571 0.831 35 3.095 0.0115
PRAISE - NONE 0.429 0.831 35 0.516 0.9403
REPROOF - NONE -2.143 0.831 35 -2.579 0.0422
P value adjustment: sidak method for 3 tests pairs(fit4.emm.g, adjust="none")age = CHILDREN:
contrast estimate SE df t.ratio p.value
PRAISE - REPROOF -3.548 0.865 35 -4.103 0.0002
PRAISE - NONE -1.262 0.865 35 -1.459 0.1534
REPROOF - NONE 2.286 0.831 35 2.751 0.0093
age = ADULTS:
contrast estimate SE df t.ratio p.value
PRAISE - REPROOF 2.571 0.831 35 3.095 0.0039
PRAISE - NONE 0.429 0.831 35 0.516 0.6092
REPROOF - NONE -2.143 0.831 35 -2.579 0.0143This section is under development.
library(WRS2)
WRS2::t2way(errors~group*age, data=prn.data,tr=0,nboot=5000)
WRS2::mcp2atm(errors~group*age, data=prn.data)
WRS2::mcp2a(errors~group*age, data=prn.data)The long litany of optional approaches found in this document makes it hard to have a clear vision of the best approach. I will provide a recommendation, but it is tied to this 2-way factorial design, and some recommendations may not extend well to larger factorials, repeated measures designs, etc.
Assuming that the approach to 2-way analysis intends to use analytical/planned/orthogonal contrasts, this is a reasonable flow:
Perform the standard EDA numerical and graphical techniques to become familiar with the data set.
Set the orthogonal contrast set for each factor using cbind or matrix and the contrasts functions.
Perform the full factorial omnibus ANOVA. Whether one uses aov or lm, or instead uses afex (aov_car). The advantages of aov_car are the immediate use of Type III SS, the GES effect size statistic, and its ease of use with emmeans
Evaluate assumptions of homoscedasticity (homogeneity of variance) and residual normality using the graphical and inferential methods outlined above.
If assumption violations are not present, proceed to evaluate the aov object with anova for the full set of omnibus F tests if sample sizes are equal or if Type I SS are preferred when sample sizes are unequal.
If Type III SS are preferred in unbalanced designs (unequal N) use the Anova function on an aov fit or use aov_car.
Examine main effect contrasts and interaction contrasts using the summary.lm function on the aov model object. These effects can also be obtained with the phia package or emmeans approach if F tests are preferred to the t-tests produced by summary.lm applied to an aov fit..
Examine Simple Main Effects and their contrasts using phia or emmeans functions. After a good bit of work with both packages I find the logic and coding slightly easier with emmeans.
Examine effect sizes using the anova_stats function, effectsize, and manual use of relevant SS from phia or emmeans for contrasts and SME.
If planned contrasts are not employed, use the methods in the post hoc section of this document to guide followup analyses after the omnibus model is evaluated. Again, use of emmeans gives access to a good implementation of the Tukey test.
If assumptions are violated, consider permutation or bootstrapping methods, although extension of these methods to contrasts and SME is not outlined in this document and I am unaware of direct methods in R for their evaluation.
If Bayesian inference is preferred, consider the BayesFactor functions illustrated here, although criticisms of that approach were discussed in the 1way implementation document and alternatives suggested there.
sessionInfo()R version 4.4.2 (2024-10-31 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 26100)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.utf8
[2] LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8
time zone: America/New_York
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Rmisc_1.5.1 lattice_0.22-6 BayesFactor_0.9.12-4.7
[4] coda_0.19-4.1 nortest_1.0-4 gt_0.11.0
[7] knitr_1.48 effectsize_0.8.9 sjstats_0.19.0
[10] emmeans_1.10.3 afex_1.3-1 lme4_1.1-35.5
[13] Matrix_1.7-0 granova_2.2 sciplot_1.2-0
[16] ez_4.4-0 plyr_1.8.9 phia_0.3-1
[19] ggridges_0.5.6 ggdist_3.3.2 ggthemes_5.1.0
[22] ggplot2_3.5.1 foreign_0.8-87 car_3.1-2
[25] carData_3.0-5 psych_2.4.6.26
loaded via a namespace (and not attached):
[1] mnormt_2.1.1 pbapply_1.7-2 polynom_1.4-1
[4] sandwich_3.1-0 rlang_1.1.4 magrittr_2.0.3
[7] multcomp_1.4-26 compiler_4.4.2 mgcv_1.9-1
[10] vctrs_0.6.5 reshape2_1.4.4 quadprog_1.5-8
[13] stringr_1.5.1 pkgconfig_2.0.3 fastmap_1.2.0
[16] labeling_0.4.3 utf8_1.2.4 rmarkdown_2.27
[19] ggrain_0.0.4 nloptr_2.1.1 MatrixModels_0.5-3
[22] purrr_1.0.2 xfun_0.46 jsonlite_1.8.8
[25] gghalves_0.1.4 parallel_4.4.2 R6_2.5.1
[28] stringi_1.8.4 RColorBrewer_1.1-3 boot_1.3-30
[31] extrafontdb_1.0 numDeriv_2016.8-1.1 estimability_1.5.1
[34] Rcpp_1.0.13 zoo_1.8-12 base64enc_0.1-3
[37] parameters_0.22.1 extrafont_0.19 splines_4.4.2
[40] tidyselect_1.2.1 rstudioapi_0.16.0 abind_1.4-5
[43] yaml_2.3.10 codetools_0.2-20 tibble_3.2.1
[46] lmerTest_3.1-3 withr_3.0.1 bayestestR_0.14.0
[49] evaluate_0.24.0 survival_3.7-0 ggpp_0.5.8-1
[52] xml2_1.3.6 pillar_1.9.0 insight_0.20.2
[55] distributional_0.4.0 generics_0.1.3 munsell_0.5.1
[58] scales_1.3.0 minqa_1.2.7 xtable_1.8-4
[61] glue_1.7.0 tools_4.4.2 mvtnorm_1.2-5
[64] rgl_1.3.1 grid_4.4.2 Rttf2pt1_1.3.12
[67] datawizard_0.12.2 colorspace_2.1-1 nlme_3.1-165
[70] performance_0.12.2 cli_3.6.3 fansi_1.0.6
[73] dplyr_1.1.4 gtable_0.3.5 sass_0.4.9
[76] digest_0.6.36 TH.data_1.1-2 htmlwidgets_1.6.4
[79] farver_2.1.2 htmltools_0.5.8.1 lifecycle_1.0.4
[82] MASS_7.3-61
11.1 comment on the “generalized effect size” statistic
The GES statistic provided here and with the ez package functions is a generalized effect size indicator. It is conceptually similar to partial eta squared and numerically identical in standard/basic situations. Where it becomes very valuable is when one or more IVs in ANOVA designs is a measured rather than manipulated variable - such as an intact groups variable like gender. The best advice would be to report partial eta squareds plus GES.
afex permits specification of whether an IV is manipulated or observed. In the primary illustration data set use here, the “age” independent variable is actually observed. We can rerun the ANOVA with a specification that makes this clear. The GES reported in the first
aov_caranalysis above are equivalent to partial eta squareds, but in the analysis here, with “age” treated as an observed/measured variable, the GES are different.Show/Hide Code
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And if we had run the afex model with the observed variable specified, we could always compute the partial eta squareds with
effectsize.Show/Hide Code
The same GES values could also be obtained via
effectsize:Show/Hide Code
Also note that assumptions can be evaluated for
aov_carmodels:Show/Hide Code
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Note that the
car::leveneTestis evaluated on the raw data from the data frame, not on an anova model (see above), so it is not necessary to duplicate that here.