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#' Best Fit Using Estimated Expected Effects
#'
#' Use simple linear regression to describe
#' a hand draw line fit to a dose-effect experiment.
#' @param handDose
#' A numeric vector of doses for which expected effects are estimated from
#' a hand drawn line, must have at least two unique values.
#' @param handPct
#' A numeric vector of the expected percent affected corresponding to (and
#' the same length as) \code{handDose}.
#' @return
#' A numeric vector of length two, the estimated intercept and slope of the
#' dose-response curve on the log10-probit scale,
#' @import
#' stats
#' @export
#' @examples
#' d <- c(0.0625, 0.125, 0.25, 0.5, 1)
#' p <- c(9.5, 34, 67, 90.5, 98.6)
#' fitHand(handDose=d, handPct=p)
fitHand <- function(handDose, handPct) {
if(length(unique(handDose))<2) {
stop("handDose must have at least 2 unique values")
}
if(class(handDose)!="numeric" | class(handPct)!="numeric") {
stop("handDose and handPct must both be numeric")
}
if(length(handDose)!=length(handPct)) {
stop("handDose and handPct must be the same length")
}
# get the paramaters for the hand drawn line
out <- lm(probit(handPct/100) ~ log10(handDose))$coef
names(out) <- c("Intercept", "Slope")
return(out)
}
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