Add Column with p values - speed efficient - r

Question

I have a large table with several thousand values for which I would like to compute the p-values using binom.test. As an example:
test <- data.frame("a" = c(4,8,8,4), "b" = c(2,3,8,0))
to add a third column called "pval" I use:
test$pval <- apply(test, 1, function(x) binom.test(x[2],x[1],p=0.05)$p.value)
This works fine for a small test sample such as above, however when I try to use this for my actual dataset the speed is way too slow. Any suggestions?

Answer 1

If you are just using the p-value, and always using two-sided tests, then simply extract that part of the code from the existing binom.test function.
simple.binom.test <- function(x, n)
{
p <- 0.5
relErr <- 1 + 1e-07
d <- dbinom(x, n, p)
m <- n * p
if (x == m) 1 else if (x < m) {
i <- seq.int(from = ceiling(m), to = n)
y <- sum(dbinom(i, n, p) <= d * relErr)
pbinom(x, n, p) + pbinom(n - y, n, p, lower.tail = FALSE)
} else {
i <- seq.int(from = 0, to = floor(m))
y <- sum(dbinom(i, n, p) <= d * relErr)
pbinom(y - 1, n, p) + pbinom(x - 1, n, p, lower.tail = FALSE)
}
}
Now test that it gives the same values as before:
library(testthat)
test_that(
"simple.binom.test works",
{
#some test data
xn_pairs <- subset(
expand.grid(x = 1:50, n = 1:50),
n >= x
)
#test that simple.binom.test and binom.test give the same answer for each row.
with(
xn_pairs,
invisible(
mapply(
function(x, n)
{
expect_equal(
simple.binom.test(x, n),
binom.test(x, n)$p.value
)
},
x,
n
)
)
)
}
)
Now see how fast it is:
xn_pairs <- subset(
expand.grid(x = 1:50, n = 1:50),
n >= x
)
system.time(
with(
xn_pairs,
mapply(
function(x, n)
{
binom.test(x, n)$p.value
},
x,
n
)
)
)
## user system elapsed
## 0.52 0.00 0.52
system.time(
with(
xn_pairs,
mapply(
function(x, n)
{
simple.binom.test(x, n)
},
x,
n
)
)
)
## user system elapsed
## 0.09 0.00 0.09
A five-fold speed up.