R help - Jul 2004 - vectorizing sapply() code (Modified by Aaron J. Mackey)

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

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

[ 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