./blupsurv/R/bivrec.r

1. ZZdebug/fmkprofgr

ZZdebug/fmkprofgr [ Functions ]

NAME

    fmkprofgr --- profile likelihood gradient

FUNCTION

Compute the gradient of the profile likelihood, conditional on the frailties and other parameters. This is superseded by the faster wrapper fmkproflik2, but is still in the code for debugging.

SYNOPSIS

1764 fmkprofgr <- function(m, Ji, betahat, datamat, as, Uijmat)

INPUTS

    m          number of clusters
    Ji         cluster sizes
    betahat    regression coefficient estimates 
    datamat    data matrix generated by makedatamat 
    as         matrix of discretization breakpoints 
    Uijmat     matrix of frailty estimates

OUTPUTS

    likgr      gradient of profile loglikelihood of betahat conditional 
               on the other parameters

SOURCE

1767 {
1768     # Prepare data for Fortran computation
1769     gr <- rep(0, length(betahat))
1770     covs <- matrix(datamat[, -(1:8)], dim(datamat)[1], dim(datamat)[2] - 8)
1771     ncovs <- dim(covs)[2]
1772     d <- dim(covs)[1]    
1773     index <- datamat[, c("i", "j", "k", "r", "smin", "smax")]
1774     delta <- datamat[, "delta"]
1775     times <- datamat[, "time"]    
1776     # Fortran call
1777     out <- .Fortran("fprofgr",
1778             betahat = as.double(betahat),
1779             index = as.integer(index),
1780             delta = as.double(delta),
1781             times = as.double(times),
1782             Z = as.double(covs),
1783             as = as.double(as),
1784             Uijmat = as.double(Uijmat),
1785             d = as.integer(d),
1786             ncovs = as.integer(ncovs),
1787             nr = as.integer(dim(as)[1]),
1788             ns = as.integer(dim(as)[2]),
1789             m = as.integer(m),
1790             maxj = as.integer(max(Ji)),
1791             gr = as.double(gr))
1792     return(out$gr)  
1793 }

TABLE OF CONTENTS

ZZdebug/fmkprofgr [ Functions ]