From: Aaron J. Mackey <amackey_at_pcbi.upenn.edu>

Date: Tue 06 Jul 2004 - 22:17:55 EST

ct <- round(logb(length(d$ix), 2))

ll <- function( th=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,

);

}

R-help@stat.math.ethz.ch mailing list

https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Jul 07 21:58:40 2004

Date: Tue 06 Jul 2004 - 22:17:55 EST

[ 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

R-help@stat.math.ethz.ch mailing list

https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Wed Jul 07 21:58:40 2004

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