Tukey HSD for categorical and continuous variables in R

Question

I want to do a post-hoc test for a significant ANOVA I've done successfully.

I have 5 conditions (target_onset) across which I want to compare reaction times (key_resp.rt) in a df called data_clean. target_onset and key_resp.rt are columns.

This is how I did the ANOVA, which worked fine:

cond.aov <- aov(data_clean$target_onset ~ data_clean$key_resp.rt)
summary(cond.aov)

Next, I want to see what a post-hoc test says to find out which differences between the 5 conditions are significant.

I know that TukeyHSD only takes factors. So I factorized my columns of interest:

data_clean$target_onset <- factor(data_clean$target_onset)
data_clean$key_resp.rt <- factor(data_clean$key_resp.rt)

TukeyHSD(aov(data_clean$target_onset ~ data_clean$key_resp.rt))

However, when I run this code, I get the following error:

Error in class(y) <- oldClass(x): adding class "factor" to an invalid object In addition: Warning messages: 1: In model.response(mf, "numeric"): using type = "numeric" with a factor response will be ignored 2: In Ops.factor(y, z$residuals): '-' not meaningful for factors

Any suggestions would be helpful. Thanks in advance.

Answer 1

EDIT first time through I missed the fact you had the formula backwards as well!

You need to make target_onset a factor before issuing the aov function. You do not want to make key_resp.rt a factor at all.

So the sequence should be...

data_clean$target_onset <- factor(data_clean$target_onset)

cond.aov <- aov(key_resp.rt ~ target_onset, data = data_clean)

summary(cond.aov)

TukeyHSD(cond.aov)

The dependent variable (the response time goes on the left of the tilde and the independent grouping variable to the right.

If you don't make the condition/grouping variable a factor aov which actually do an lm using the numbers you have in the grouping column you can see that reflected in the degrees of freedom for the cond.aov .

As long as you already have an aov object might as well make the call to TukeyHSD as simple as possible