./blupsurv/R/bivrec.r

1. ZZdebug/makemrs

ZZdebug/makemrs [ Functions ]

NAME

    makemrs --- terms for profile likelihood

FUNCTION

Compute terms needed in the profile likelihood and gradient. This is an R implementation of the FORTRAN routine fmkmrs, used for debugging only.

SYNOPSIS

1479 makemrs <- function(m, Ji, datamat, as, betahat, Uijmat)

INPUTS

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

OUTPUTS

    mrs        matrix of terms needed by the profile likelihood
    mrsgr      array of terms needed by the profile gradient

SOURCE

1482 {
1483     # Initialize matrices
1484     mrs <- matrix(0, dim(as)[1], dim(as)[2] - 1)
1485     mrsgr <- array(0, c(dim(as)[1], dim(as)[2] - 1, length(betahat)))
1486     covs <- as.matrix(datamat[, -c(1:8)], dim(datamat)[1], length(betahat))
1487     # Loop through the data matrix
1488     for(ind in 1:dim(datamat)[1])
1489     {
1490         i <- datamat[ind, "i"]
1491         j <- datamat[ind, "j"]
1492         k <- datamat[ind, "k"]
1493         smax <- datamat[ind, "smax"]
1494         smin <- datamat[ind, "smin"]
1495         r <- datamat[ind, "r"]
1496         time <- datamat[ind, "time"]
1497         for(s in smin:smax){
1498             # At each entry in the data matrix, add the term to the 
1499             # appropriate component of the output matrices
1500             pred <- Uijmat[i, j] * exp(as.matrix(betahat)%*%covs[ind, ]) * 
1501                 A(time, as, r, s)
1502             mrs[r, s] <- mrs[r, s] + pred
1503             mrsgr[r, s, ] <- mrsgr[r, s, ] + covs[ind, ] * pred
1504         }
1505     }
1506     # Format for output
1507     return(list(mrs = mrs, mrsgr = mrsgr))
1508 }

TABLE OF CONTENTS

ZZdebug/makemrs [ Functions ]