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#'Calculates the Estimating Equation to be solved in RPSFTM models
#'
#' @title Estimating Equations for \code{rpsftm()}
#' @name est_eqn
#' @inheritParams untreated
#' @param data the data set that contains the variables.
#' Must contain columns named: time, censor_time,rx, arm.
#' Optionally a column named: treat_modifier
#' @param formula a formula object of covariates to adjust for: \code{~strata(A)+B*C}
#' @param target the value to subtract off from the z-statistic
#' @param test the survival regression function to calculate the z-statistic: survdiff, coxph, survreg
#' @param ... arguments to supply to the test function.
#' @return A scalar value of the estimating equation: the z-statistics from a test minus a target value
#' @seealso \code{\link{untreated}}
#' @author Simon Bond
#' @importFrom stats update
#' @keywords internal
est_eqn <- function(psi, data, formula, target = 0, test = "survdiff",
autoswitch, ...) {
if ("(treat_modifier)" %in% names(data)) {
psi <- psi * data[, "(treat_modifier)"]
}
Sstar <- untreated(psi, data[, "time"], data[, "status"], data[,"(censor_time)"],
data[,".rx"], data[, ".arm"], autoswitch)
data <- cbind(Sstar, data)
# build a formula object,
fit_formula <- update(formula, Sstar ~ .)
# allow different methods to test the independence of arm constrained
# to be from the survival package.
functionName <- get(test, asNamespace("survival"))
fit <- do.call(functionName, list(fit_formula, data, ...))
# a 'cheat' to enable this to plugged into uniroot as a function that
# returns a number AND store the fit object.
.value <- extract_z(fit, arm = ".arm") - target
attr(.value, "fit") <- fit
.value
}
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