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#========== Function to compute summaries of objects in the joint model class ==========#
summary.jmodelTM <- function (object, ...)
{
fit = object
betas <- fit$coefficients$beta
Vcov <- fit$Vcov
ncx <- length(betas)
seB <- suppressWarnings(sqrt(diag(Vcov)[1 : ncx]))
infoB <- cbind("Estimate" = betas, "StdErr" = seB, "z.value" = betas / seB,
"p.value" = 2 * pnorm(abs(betas / seB), lower.tail = FALSE))
ncw <- length(fit$coefficients$phi)
phis <- c(fit$coefficients$phi, fit$coefficients$alpha)
seP <- suppressWarnings(sqrt(diag(Vcov)[(ncx + 1) : (ncx + ncw + 1)]))
infoP <- cbind("Estimate" = phis, "StdErr" = seP, "z.value" = phis / seP,
"p.value" = 2 * pnorm(abs(phis / seP), lower.tail = FALSE))
Bsigma <- fit$coefficients$Bsigma
ncz <- if(length(Bsigma) == 1) 1 else nrow(Bsigma)
npar <- ncx + ncw + ncz * (ncz + 1) / 2 + 2
logLik <- fit$logLik
AIC <- - 2 * logLik + 2 * npar
BIC <- - 2 * logLik + log(fit$n) * npar
result <- list("infoLong" = infoB, "infoSurv" = infoP, Bsigma = Bsigma,
sigma.e = fit$coefficients$Ysigma, logLik = logLik, AIC = AIC, BIC = BIC)
result$N <- fit$N
result$n <- fit$n
result$d <- fit$d
result$rho <- fit$rho
result$control <- fit$control
result$convergence <- fit$convergence
result$numIter <- fit$numIter
result$call <- fit$call
class(result) <- "summary.jmodelTM"
result
}
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