ezANOVA and pairwise.t.test in R: output

Question

I\'ve been running ezANOVA using R followed by pairwise comparisons (pairwise.t.test...)

There seems to be output not included(?) once run so I have the following qu

Answer 1

In response to your second question, how to extract t-values from pairwise.t.test. It occurred to me that having the degrees of freedom from your data, and the precise p-values would allow you to use the qt() function to determine the t-values. Example:

myTTests<-pairwise.t.test(vector1,vector2
                ,p.adjust.method = "none"
                )
qt(myTTests[[3]],degreesOfFreedom)

Answer 2

Maybe this information from the help information for ezANOVA will be useful.

library(ez)
data(ANT)
rt_anova = ezANOVA(
    data = ANT[ANT$error==0,]
    , dv = rt
    , wid = subnum
    , within = .(cue,flank)
    , between = group
)

#Show the ANOVA and assumption tests.
print(rt_anova)

See this link for more examples.

The help page indicates the following Values are part of the output object:

DFn          Degrees of Freedom in the numerator (a.k.a. DFeffect).
DFd          Degrees of Freedom in the denominator (a.k.a. DFerror).
SSn          Sum of Squares in the numerator (a.k.a. SSeffect).
SSd          Sum of Squares in the denominator (a.k.a. SSerror).
F            F-value.
p            p-value (probability of the data given the null hypothesis).
p<.05        Highlights p-values less than the traditional alpha level of .05.
ges          Generalized Eta-Squared measure of effect size (see in references below: Bakeman, 2005).
GGe          Greenhouse-Geisser epsilon.
p[GGe]       p-value after correction using Greenhouse-Geisser epsilon.
p[GGe]<.05   Highlights p-values (after correction using Greenhouse-Geisser epsilon) less than the traditional alpha level of .05.
HFe          Huynh-Feldt epsilon.
p[HFe]       p-value after correction using Huynh-Feldt epsilon.
p[HFe]<.05   Highlights p-values (after correction using Huynh-Feldt epsilon) less than the traditional alpha level of .05.
W            Mauchly's W statistic

These columns should contain most of the raw material needed for calculating MSE... Though, I don't actually know how to calculate MSE in a repeated measures ANOVA...

As far as I know pairwise.t.test provides output that contains only p-values. From the help page:

attach(airquality)
Month <- factor(Month, labels = month.abb[5:9])
pairwise.t.test(Ozone, Month)

I did find this post on extracting t-values from pairwise.t.test, which may help. Essentially, in the post, the code for pairwise.t.test is modified. The line that calculates p-values is commented out (i.e. # 2 * pt(-abs(t.val), total.degf) and they add a line to return the t.val instead of p-values. You can see in that code that degrees of freedom are calculated and an object called se.diff, thus, you should be able to return them, too, by modifying the code.

Using the modified paired.t.test from the post I linked, I added a little bit to return total.degf. I'll leave it to you to include se.dif, if you want. Note that this code is for example purposes and needs some cleaning (e.g. the code recognizes the t.val values as being p-values. This wouldn't be hard to change.

my.pairded.t.test <- function (x, g, p.adjust.method = 
p.adjust.methods, pool.sd = TRUE,
...)
{
DNAME <- paste(deparse(substitute(x)), "and", 
deparse(substitute(g)))
g <- factor(g)
p.adjust.method <- match.arg(p.adjust.method)
if (pool.sd) {
    METHOD <- "t tests with pooled SD"
    xbar <- tapply(x, g, mean, na.rm = TRUE)
    s <- tapply(x, g, sd, na.rm = TRUE)
    n <- tapply(!is.na(x), g, sum)
    degf <- n - 1
    total.degf <- sum(degf)
    pooled.sd <- sqrt(sum(s^2 * degf)/total.degf)
    compare.levels <- function(i, j) {
        dif <- xbar[i] - xbar[j]
        se.dif <- pooled.sd * sqrt(1/n[i] + 1/n[j])
        t.val <- dif/se.dif
        #  2 * pt(-abs(t.val), total.degf)  this is commented out
        t.val    # this is added
    }
}
else {
    METHOD <- "t tests with non-pooled SD"
    compare.levels <- function(i, j) {
        xi <- x[as.integer(g) == i]
        xj <- x[as.integer(g) == j]
        t.test(xi, xj, ...)$statistic       # this is changed in case 
                            # pool.sd=F
    }
}
PVAL <- pairwise.table(compare.levels, levels(g), 
p.adjust.method)
ans <- list(method = METHOD, data.name = DNAME, p.value 
= PVAL,
    p.adjust.method = p.adjust.method, total.degf=total.degf) # I added the total.degf part
class(ans) <- "pairwise.htest"
ans
}

Put it to use.

a<-my.pairded.t.test(Ozone, Month)
a$total.degf