# [R] vectorizing sapply() code (Modified by Aaron J. Mackey)

Aaron J. Mackey amackey at pcbi.upenn.edu
Tue Jul 6 14:17:55 CEST 2004

```[ Not sure why, but the first time I sent this it never seemed to go
through; apologies if you're seeing this twice ... ]

I have some fully functional code that I'm guessing can be done
better/quicker with some savvy R vector tricks; any help to make this
run a bit faster would be greatly appreciated; I'm particularly stuck
on how to calculate using "row-wise" vectors without iterating
explicitly over the dataframe or table ...

library(stats4);
d <- data.frame( ix=c(0,1,2,3,4,5,6,7),
ct=c(253987,  9596, 18680,  2630,  8224,  3590,  5534,
18937),
A=c(      0,     1,     0,     1,     0,     1,     0,
1),
B=c(      0,     0,     1,     1,     0,     0,     1,
1),
C=c(      0,     0,     0,     0,     1,     1,     1,
1)
);
ct <- round(logb(length(d\$ix), 2))
ll <- function( th=0.5,
a1=log(0.5), a2=log(0.5), a3=log(0.5),
b1=log(0.5), b2=log(0.5), b3=log(0.5)
) {
a <- exp(sapply(1:ct, function (x) { get(paste("a", x, sep="")) }));
b <- exp(sapply(1:ct, function (x) { get(paste("b", x, sep="")) }));
-sum( d\$ct * log( sapply( d\$ix,
function (ix, th, a, b) {
x <- d[ix+1,3:(ct+2)]
(th     * prod((b ^ (1-x)) * ((1-b) ^ x
))) +
((1-th) * prod((a ^ x    ) * ((1-a) ^
(1-x))))
},
th, a, b
)
)
);
}

ml <- mle(ll,
lower=c(0+1e-5, rep(log(0+1e-8), 2*ct)),
upper=c(1-1e-5, rep(log(1-1e-8), 2*ct)),
method="L-BFGS-B"
);

For those interested in the math, this is the MLE procedure to estimate
the false positive/false negative rates (a and b) of three diagnostic
(A, B and C) tests that have the observed performance recapitulated in
dataframe "d", but no "gold standard" (sometimes called "latent class
analysis", or LCA).

Thanks for any help,

-Aaron

```