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#' fit Excess Relative Risk Model
#'
#' function that calls the optimization (mle from stats4 package, so use optim), and return a rERR object with the estimation and summary
#' @param formula Surv(entry_time,exit_time,outcome)~loglin(loglin_var1,..,loglin_varn)+\cr
#' lin(lin_var1,..,lin_varm)+strata(strat_var1,...strat_varp)
#' @param data data set returned from f_to_model_data
#' @param id_name name of variable containing the names of subjects
#' @param time_name name of the time variable
#' @param lag latency period
#' @return rERR object with the estimation
#' @examples \donttest{ f_fit_linERR_all(formula,data,id_name,time_name,lag)}
#' @export
f_fit_linERR_all <- function(formula,data,id_name,time_name,lag)
{
# keep just model vars and expand if categorical
dt2 <- f_to_model_data(formula,data,id_name,time_name)
n_lin_vars <- attr(dt2,"n_lin_vars")
n_loglin_vars <- attr(dt2,"n_loglin_vars")
# risksets
rsets <- f_risksets(formula,data=dt2,lag,id_name,time_name)
# fit the model
fit <- f_fit_linERR(formula,data=dt2,rsets,n_lin_vars,n_loglin_vars,id_name,time_name)
return(fit)
}
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