R/aftp0.R

In goranbrostrom/eha: Event History Analysis

aftp0 <- function(printlevel, ns, nn, id,
                  strata, Y, X, offset, dis, means){
    Fmin <- function(beta){

        fit <- .C("aftsup",
                  as.integer(printlevel),
                  ##
                  as.integer(ns), # No. of strata
                  as.integer(nn),
                  as.integer(ncov),
                  as.integer(bdim),
                  ##
                  as.integer(id),
                  as.integer(strata - 1), # 0, ..., (ns-1); C-style!
                  as.double(Y[, 1]),  ## 'enter'
                  as.double(Y[, 2]),  ## 'exit'
                  as.integer(Y[, 3]), ## 'event'
                  ##
                  ##as.double(t(scale(X, center = TRUE, scale = FALSE))),
                  as.double(t(X)), # NOTE; scaling already done!
                  ## NOTE transpose!
                  as.double(offset),
                  as.integer(dis),     # baseline distribution
                  as.double(beta),
                                        # results -->
                  loglik = double(1),  # function value at beta
                  fail = integer(1), # = 0: No failure
                  ## DUP = FALSE,
                  PACKAGE = "eha"
                  )
        if (fit$fail) stop("Error in likelihood calculation")
        return(fit$loglik)
    }
    ncov <- NCOL(X)
    ## Start values (no covariates):
    bdim <- 2 * ns
    ncov.save <- ncov
    ncov <- 0
    beta <- numeric(bdim)
    for (i in seq_len(ns)){
        beta[2 * i - 1] <- log(sum(Y[, 2] - Y[, 1]) / sum(Y[, 3]))
        beta[2*i] <- 0
    }
    
    loglik.start <- -optim(beta, Fmin, method = "BFGS", hessian = FALSE)$value
    ## And now the real thing:
    ncov <- ncov.save
    bdim <- ncov + 2 * ns
    beta <- c(rep(0, ncov), beta)
    res <- optim(beta, Fmin, method = "BFGS",
                 control = list(trace = as.integer(printlevel)),
                 hessian = TRUE)
    fit <- list(beta = res$par, loglik = c(loglik.start, -res$value))
    
    fit$var <- try(solve(res$hessian))
    fit$fail <- res$convergence != 0
    fit$coef.names <- names(fit$beta)

    fit$ncov <- ncov
    fit
}