Fast post hoc computation using R - r

Question

I have a large dataset which I would like to perform post hoc computation:
dat = as.data.frame(matrix(runif(10000*300), ncol = 10000, nrow = 300))
dat$group = rep(letters[1:3], 100)
Here is my code:
start <- Sys.time()
vars <- names(dat)[-ncol(dat)]
aov.out <- lapply(vars, function(x) {
lm(substitute(i ~ group, list(i = as.name(x))), data = dat)})
TukeyHSD.out <- lapply(aov.out, function(x) TukeyHSD(aov(x)))
Sys.time() - start
Time difference of 4.033335 mins
It takes about 4 min, are there more efficient and elegant ways to perform post hoc using R?
Thanks a lot

Answer 1

Your example is too big. For illustration of the idea I use a small one.
set.seed(0)
dat = as.data.frame(matrix(runif(2*300), ncol = 2, nrow = 300))
dat$group = rep(letters[1:3], 100)
Why do you call aov on a fitted "lm" model? That basically refits the same model.
Have a read on Fitting a linear model with multiple LHS first. lm is the workhorse of aov, so you can pass a multiple LHS formula to aov. The model has class c("maov", "aov", "mlm", "lm").
response_names <- names(dat)[-ncol(dat)]
form <- as.formula(sprintf("cbind(%s) ~ group", toString(response_names)))
fit <- do.call("aov", list(formula = form, data = quote(dat)))
Now the issue is: there is no "maov" method for TuckyHSD. So we need a hacking.
TuckyHSD relies on the residuals of the fitted model. In c("aov", "lm") case the residuals is a vector, but in c("maov", "aov", "mlm", "lm") case it is a matrix. The following demonstrates the hacking.
aov_hack <- fit
aov_hack[c("coefficients", "fitted.values")] <- NULL ## don't need them
aov_hack[c("contrasts", "xlevels")] <- NULL ## don't need them either
attr(aov_hack$model, "terms") <- NULL ## don't need it
class(aov_hack) <- c("aov", "lm") ## drop "maov" and "mlm"
## the following elements are mandatory for `TukeyHSD`
## names(aov_hack)
#[1] "residuals" "effects" "rank" "assign" "qr"
#[6] "df.residual" "call" "terms" "model"
N <- length(response_names) ## number of response variables
result <- vector("list", N)
for (i in 1:N) {
## change response variable in the formula
aov_hack$call[[2]][[2]] <- as.name(response_names[i])
## change residuals
aov_hack$residuals <- fit$residuals[, i]
## change effects
aov_hack$effects <- fit$effects[, i]
## change "terms" object and attribute
old_tm <- terms(fit) ## old "terms" object in the model
old_tm[[2]] <- as.name(response_names[i]) ## change response name in terms
new_tm <- terms.formula(formula(old_tm)) ## new "terms" object
aov_hack$terms <- new_tm ## replace `aov_hack$terms`
## replace data in the model frame
aov_hack$model[1] <- data.frame(fit$model[[1]][, i])
names(aov_hack$model)[1] <- response_names[i]
## run `TukeyHSD` on `aov_hack`
result[[i]] <- TukeyHSD(aov_hack)
}
result[[1]] ## for example
# Tukey multiple comparisons of means
# 95% family-wise confidence level
#
#Fit: aov(formula = V1 ~ group, data = dat)
#
#$group
# diff lwr upr p adj
#b-a -0.012743870 -0.1043869 0.07889915 0.9425847
#c-a -0.022470482 -0.1141135 0.06917254 0.8322109
#c-b -0.009726611 -0.1013696 0.08191641 0.9661356
I have used a "for" loop. Replace it with a lapply if you want.