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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/CFR_estimation.R
\name{EMforCFR}
\alias{EMforCFR}
\title{A function to estimate the relative case fatality ratio when reporting rates
are time-varying and deaths are lagged because of survival time.}
\usage{
EMforCFR(assumed.nu, alpha.start.values, full.data, max.iter = 50,
verb = FALSE, tol = 1e-10, SEM.var = TRUE)
}
\arguments{
\item{assumed.nu}{a vector of probabilities corresponding to the survival
distribution, i.e. nu[i]=Pr(surviving i days | fatal case)}
\item{alpha.start.values}{a vector starting values for the reporting rate
parameter of the GLM model. This must have length which corresponds to one
less than the number of unique integer values of full.dat[,"new.times"].}
\item{full.data}{A matrix of observed data. See description below.}
\item{max.iter}{The maximum number of iterations for the EM algorithm and
the accompanying SEM algorithm (if used).}
\item{verb}{An indicator for whether the function should print results as it
runs.}
\item{tol}{A tolerance to use to test for convergence of the EM algorithm.}
\item{SEM.var}{If TRUE, the SEM algorithm will be run in addition to the EM
algorithm to calculate the variance of the parameter estimates.}
}
\value{
A list with the following elements \describe{ \item{naive.rel.cfr
}{the naive estimate of the relative case fatality ratio}
\item{glm.rel.cfr }{the reporting-rate-adjusted estimate of the relative
case fatality ratio} \item{EM.rel.cfr }{the lag-adjusted estimate of the
relative case fatality ratio} \item{EM.re.cfr.var }{the variance for the
log-scale lag-adjusted estimator taken from the final M-step}
\item{EM.rel.cfr.var.SEM }{ the Supplemented EM algorithm variance for the
log-scale lag-adjusted estimator} \item{EM.rel.cfr.chain }{a vector of the
EM algorithm iterates of the lag-adjusted relative CFR estimates}
\item{EMiter}{the number of iterations needed for the EM algorithm to
converge} \item{EMconv}{indicator for convergence of the EM algorithm. 0
indicates all parameters converged within max.iter iterations. 1 indicates
that the estimate of the relative case fatality ratio converged but other
did not. 2 indicates that the relative case fatality ratio did not
converge.} \item{SEMconv}{indicator for convergence of SEM algorithm.
Same scheme as EMconv.} \item{ests}{ the coefficient estimates for the
model } \item{ests.chain}{ a matrix with all of the coefficient estimates,
at each EM iteration} \item{DM}{the DM matrix from the SEM algorithm}
\item{DMiter}{a vector showing how many iterations it took for the
variance component to converge in the SEM algorithm} }
}
\description{
This function implements an EM algorithm to estimate the relative case
fatality ratio between two groups when reporting rates are time-varying and
deaths are lagged because of survival time.
}
\details{
The data matrix full.data must have the following columns:
\describe{ \item{grp}{a 1 or a 2 indicating which of the two groups, j,
the observation is for.} \item{new.times}{an integer value representing
the time, t, of observation.} \item{R}{the count of recovered cases with
onset at time t in group j.} \item{D}{the count of deaths which occurred at
time t in groupo j (note that these deaths did not have disease onset at
time t but rather died at time t).} \item{N}{the total cases at t, j, or
the sum of R and D columns.} }
}
\examples{
## This is code from the CFR vignette provided in the documentation.
data(simulated.outbreak.deaths)
min.cases <- 10
N.1 <- simulated.outbreak.deaths[1:60, "N"]
N.2 <- simulated.outbreak.deaths[61:120, "N"]
first.t <- min(which(N.1 > min.cases & N.2 > min.cases))
last.t <- max(which(N.1 > min.cases & N.2 > min.cases))
idx.for.Estep <- first.t:last.t
new.times <- 1:length(idx.for.Estep)
simulated.outbreak.deaths <- cbind(simulated.outbreak.deaths, new.times = NA)
simulated.outbreak.deaths[c(idx.for.Estep, idx.for.Estep + 60), "new.times"] <- rep(new.times, + 2)
assumed.nu = c(0, 0.3, 0.4, 0.3)
alpha.start <- rep(0, 22)
## caution! this next line may take several minutes (5-10, depanding on
## the speed of your machine) to run.
\dontrun{cfr.ests <- EMforCFR(assumed.nu = assumed.nu,
alpha.start.values = alpha.start,
full.data = simulated.outbreak.deaths,
verb = FALSE,
SEM.var = TRUE,
max.iter = 500,
tol = 1e-05)}
}
\keyword{case}
\keyword{coarse}
\keyword{data}
\keyword{disease}
\keyword{fatality}
\keyword{incomplete}
\keyword{infectious}
\keyword{ratio}
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