R help - Dec 2007 - two-way categorical anova post-hoc data extraction

Hi list,

I have a question regarding post-hoc extraction of data from a two-way
categorical anova.

I have a categorical anova of this form:

width ~ steepness + patchiness (4 steepness levels, 4 patchiness levels)

This simple setup answers if for the widths I collected across different levels
of steepness and patchiness significant differences can be found. Is there a way
to look at these differences in detail. Lets say that the steepness parameter is
significant, then I would like to know between which levels they are significant
or if there are levels where this isn't the case.

It's a basic question but my R knowledge has faded somewhat...

Kind regards,
Koen

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> Hi list,
>
> I have a question regarding post-hoc extraction of data from a two-way
categorical anova.
>
> I have a categorical anova of this form:
>
> width ~ steepness + patchiness (4 steepness levels, 4 patchiness levels)
>
> This simple setup answers if for the widths I collected across different
levels of steepness and patchiness significant differences can be found. Is
there a way to look at these differences in detail. Lets say that the steepness
parameter is significant, then I would like to know between which levels they
are significant or if there are levels where this isn't the case.
>
> It's a basic question but my R knowledge has faded somewhat...
I've never found this particularly easy to do.  For a recent client I wrote:

library(effects)
library(multcomp)
library(multcompView)


effectsum <- function(model, effect) {
  effects <- as.data.frame(all.effects(model)[[effect]])

  mcp <- list("Tukey")
  names(mcp) <- effect
  class(mcp) <- "mcp"

  glht_sum <- summary(glht(model, linfct = mcp))
  p <- as.vector(glht_sum$test$pvalues)
  names(p) <- gsub(" ", "", names(glht_sum$test$tstat))

  groups <- multcompLetters(p)
  effects[, 1] <- factor(effects[, 1])
  effects$group <- groups$Letters[as.character(effects[, 1])]

  effects
}


which allows you to do:

mtcars$cyl <- factor(mtcars$cyl)
simple <- lm(mpg ~ wt + cyl, data=mtcars)
effects <- effectsum(simple, "cyl")

library(ggplot2)
qplot(cyl, fit, data=effects, min = lower, max = upper,
geom="pointrange", ylab="Mean effect") + geom_text(aes(label
= group,
y = min(lower) - diff(range(lower)) * 0.07))

ggplot(mtcars, aes(x = cyl, y = mpg)) + geom_crossbar(aes(min=lower,
max=upper, y=fit), data=effects, width=0.2) +
geom_point(aes(colour=wt))

This gives lsmeans (aka population marginal means) with their standard
errors, and groups generated from all pairwise comparisons adjusted
for multiple comparisons.

I would love to improve this code: to deal with all factors in a model
automatically.  Additionally, all.effects and multcompLetters are
rather fragile with respect to level names - if you get an error try
removing any non-alphanumeric characters,

Hadley

-- 
http://had.co.nz/