R/main.R
In ahwbest/DR4PL: Dose Response Data Analysis using the 4 Parameter Logistic (4PL) Model
#' @name dr4pl
#'
#' @docType package
#'
#' @import graphics
#' @import stats
#' @import ggplot2
#' @import tensor
#' @import Rdpack
NULL
#' @title Fitting 4 Parameter Logistic (4PL) models to dose-response data.
#'
#' @description This function fits a 4PL model to dose-response data. Users can
#' obtain fitted parameter estimates as return values. Using auxiliary functions
#' provided by this R package, users can plot a fitted dose-response curve and
#' obtain confidence intervals of true parameters. In addition, the goodness-of-fit
#' test for model adequacy of the 4PL models can be performed when replicates are
#' available for each dose level.
#'
#' @export
dr4pl <- function(...) UseMethod("dr4pl")
#' @describeIn dr4pl General 4PL model fitting function for analysis of
#' dose-response relation.
#'
#' @param formula Symbolic description of the model to be fit. Either of the
#' form 'response ~ dose' or as a data frame with response values in first
#' column and dose values in second column.
#' @param data Data frame containing variables in the model.
#' @param init.parm Vector of initial parameters to be optimized in the model.
#' @param trend Indicator of whether a dose-response curve is a decreasing
#' \eqn{\theta[3]<0} or increasing curve \eqn{\theta[3]>0}. The default is "auto"
#' which indicates that the trend of the curve is automatically determined by
#' data. The option "decreasing" will impose a restriction \eqn{\theta[3]<=0}
#' while the option "increasing" will impose a restriction \eqn{\theta[3]>=0} in an
#' optimization process.
#' @param method.init Method of obtaining initial values of the parameters.
#' If it is NULL, a default "Mead" method will be used. Assign
#' "logistic" to use the logistic method.
#' @param method.optim Method of optimization of the loss function specified by
#' \code{method.robust}. This function argument is directly passed to the function
#' \code{\link[stats]{constrOptim}} which is provided in the \pkg{base} package of R.
#' @param method.robust Parameter to select loss function for the robust estimation
#' method to be used to fit a model. The argument NULL indicates the sum of squares
#' loss, "absolute" indicates the absolute deviation loss, "Huber" indicates Huber's
#' loss and "Tukey" indicates Tukey's biweight loss.
#' @param use.Hessian Indicator of whether the Hessian matrix (TRUE) or the
#' gradient vector is used in the Hill bounds.
#' @param level Confidence level to be used in Hill bounds computation.
#' @param failure.message Indicator of whether a message indicating attainment of
#' the Hill bounds and possible resolutions will be printed to the console (TRUE)
#' or hidden (FALSE).
#' @param ... Further arguments to be passed to \code{constrOptim}.
#'
#' @return A 'dr4pl' object for which "confint", "gof", "print" and "summary"
#' methods are implemented. For details, see the help page of each method.
#' For example, type \code{?confint.dr4pl} to obtain the confidence intervals
#' of parameters of the 'dr4pl' object.
#'
#' @details This function fits a 4 parameter logistic (4PL) model to dose-response
#' data. A formula of the model is
#' \deqn{\theta[1]+(\theta[4]-\theta[1])/(1+(z/\theta[2])^\theta[3])}
#'
#' \code{method.init} specifies an initialization method to get initial parameter
#' estimates based on data. The currently supported initialization methods are
#' "logistic" and 'Mead'. For further details, see the vignette.
#'
#' \code{method.optim} specifies an optimization method to be used in
#' "constrOptim" function. The currently supported optimization techniques
#' include "Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN" and "Brent". For
#' further details, see the help page of \code{\link[stats]{optim}}.
#'
#' \code{method.robust} chooses a robust estimation method among 4 methods.
#' The method of estimation is usually identified by the loss function of the
#' method. This package supports 4 types of loss functions: sum-of-squares loss,
#' absolute deviation loss, Huber's loss and Tukey's biweight loss. Each of
#' loss function is explained in detail in the vignette.
#'
#' @author Hyowon An, \email{ahwbest@gmail.com}
#' @author Justin T. Landis, \email{jtlandis314@gmail.com}
#' @author Aubrey G. Bailey, \email{aubreybailey@gmail.com}
#' @seealso \code{\link{confint.dr4pl}}, \code{\link{gof.dr4pl}},
#' \code{\link{print.dr4pl}}, \code{\link{summary.dr4pl}}
#' @export
dr4pl.formula <- function(formula,
data = list(),
init.parm = NULL,
trend = "auto",
method.init = "Mead",
method.robust = NULL,
method.optim = "Nelder-Mead",
use.Hessian = FALSE,
level = 0.9999,
failure.message = FALSE,
...) {
mf <- model.frame(formula = formula, data = data)
dose <- model.matrix(attr(mf, "terms"), data = mf)[, 2]
response <- model.response(mf)
obj <- dr4pl.default(dose = dose,
response = response,
init.parm = init.parm,
trend = trend,
method.init = method.init,
method.robust = method.robust,
method.optim = method.optim,
use.Hessian = use.Hessian,
level = level,
failure.message = failure.message,
...)
obj$call <- match.call()
obj$formula <- formula
return(obj)
}
#' @describeIn dr4pl Used in the default case, supplying a single dose and
#' response variable
#'
#' @param dose Vector of dose levels
#' @param response Vector of responses
#'
#' @examples
#' a <- dr4pl(dose = sample_data_1$Dose,
#' response = sample_data_1$Response,
#' method.init = "logistic")
#' plot(a)
#'
#' ##Assign method.init = "Mead" to use Mead's method of estimation.
#' # Use method.robust to select desired loss function
#' b <- dr4pl(Response~Dose,
#' data = sample_data_4,
#' method.init = "Mead",
#' method.robust = "Tukey" )
#' plot(b)
#'
#' ##compatable with ggplot
#' library(ggplot2)
#' c <- dr4pl(Response~Dose,
#' data = drc_error_2,
#' method.init = "Mead",
#' method.robust = "absolute" )
#' d <- plot(c)
#' d + scale_x_log10(breaks = c(.00135, .0135, .135, 1.35, 13.5))
#' @export
dr4pl.default <- function(dose,
response,
init.parm = NULL,
trend = "auto",
method.init = "Mead",
method.robust = NULL,
method.optim = "Nelder-Mead",
use.Hessian = FALSE,
level = 0.9999,
failure.message = FALSE,
...) {
types.trend <- c("auto", "decreasing", "increasing")
types.method.init <- c("logistic", "Mead")
types.method.optim <- c("Nelder-Mead", "BFGS", "CG", "SANN")
### Check errors in functions arguments
if(!is.numeric(dose)||!is.numeric(response)) {
stop("Both doses and responses should be numeric.")
}
if(any(dose<0)) {
stop("Dose levels should be nonnegative.")
}
if(length(dose) == 0 || length(response) == 0 || length(dose) != length(response)) {
stop("The same numbers of dose and response values should be supplied.")
}
if(!is.element(method.init, types.method.init)) {
stop("The initialization method name should be one of \"logistic\" and \"Mead\".")
}
if(!is.element(method.optim, types.method.optim)) {
stop("The optimization method name should be one of \"Nelder-Mead\", \"BFGS\",
\"CG\", \"L-BFGS-B\" and \"SANN\".")
}
if(!is.element(trend, types.trend)) {
stop("The type of the \"trend\" parameter should be one of \"auto\", \"decreasing\" and \"increasing\".")
}
# Fit a 4PL model
obj <- dr4plEst(dose = dose,
response = response,
init.parm = init.parm,
trend = trend,
method.init = method.init,
method.robust = method.robust,
method.optim = method.optim,
use.Hessian = use.Hessian,
level = level)
obj$call <- match.call()
class(obj) <- "dr4pl"
# If any robust estimation method is indicated, report outliers to a user.
if(!is.null(method.robust)) {
theta <- obj$parameters # Robust parameter estimates
residuals <- Residual(theta, dose, response) # Residuals
indices.outlier <- OutlierDetection(residuals)
obj$idx.outlier <- indices.outlier
obj$robust.plot <- plot(obj, indices.outlier = indices.outlier)
}
message.diagnosis <- NULL
### When convergence failure happens.
if(obj$convergence == FALSE) {
## Decide the method of robust estimation which is more robust than the method
## input by a user.
if(is.null(method.robust)) {
method.robust.new <- "absolute"
} else if(is.element(method.robust, c("absolute", "Huber"))) {
method.robust.new <- "Tukey"
} else {
stop("Convergence failure happened but no resolution could be found.")
}
n <- obj$sample.size # Number of data points
# Fit a 4PL model to data
obj.robust <- dr4plEst(dose = dose,
response = response,
init.parm = init.parm,
trend = trend,
method.init = method.init,
method.robust = method.robust.new,
method.optim = method.optim,
use.Hessian = use.Hessian,
level = level)
obj.robust$call <- match.call()
class(obj.robust) <- "dr4pl"
## Detect outliers and report them.
theta <- obj.robust$parameters # Robust parameter estimates
residuals <- Residual(theta, dose, response) # Residuals
indices.outlier <- OutlierDetection(residuals)
obj.robust$idx.outlier <- indices.outlier
obj.robust$robust.plot <- plot(obj.robust, indices.outlier = indices.outlier)
## Print different messages to the console depending on the convergence success
## of a robust fit.
if(obj.robust$convergence) {
message.diagnosis <-
paste("The Hill bounds have been hit during optimization, but other robust ",
"estimation was succesful.\n",
"Please refer to \"dr4pl.robust\" variable for diagnosis.\n",
sep = "")
} else {
message.diagnosis <-
paste("The Hill bounds have been hit during optimization with ",
obj$method.robust, " and ", obj.robust$method.robust, " methods.\n",
"Please try other initialization and robust estimation methods.\n",
sep = "")
}
obj$dr4pl.robust <- obj.robust
obj$message.diagnosis <- message.diagnosis
}
if(failure.message&&!is.null(message.diagnosis)) {
cat(message.diagnosis)
}
return(obj)
}
#' @title Private function to fit the 4PL model to dose-response data
#'
#' @description Private function that actually fits the 4PL model to data. If the
#' Hill bounds are attained at the end of optimization processes, then an
#' indicator of convergence failure so that \code{\link{dr4pl.default}} can
#' look for a remedy for convergence failure.
#'
#' @name dr4plEst
#'
#' @param dose Vector of dose levels
#' @param response Vector of responses
#' @param init.parm Vector of initial parameters of the 4PL model supplied by a
#' user.
#' @param trend Indicator of whether a dose-response curve is a decreasing
#' \eqn{\theta[3]<0} or increasing curve \eqn{\theta[3]>0}. The default is "auto"
#' which indicates that the trend of the curve is automatically determined by
#' data. The option "decreasing" will impose a restriction \eqn{\theta[3]<=0}
#' while the option "increasing" will impose a restriction \eqn{\theta[3]>=0} in an
#' optimization process.
#' @param method.init Method of obtaining initial values of the parameters.
#' Should be one of "logistic" for the logistic method or "Mead" for the Mead
#' method. The default option is the Mead method.
#' @param method.robust Parameter to select loss function for the robust estimation
#' method to be used to fit a model. The argument NULL indicates the sum of squares
#' loss, "absolute" indicates the absolute deviation loss, "Huber" indicates Huber's
#' loss and "Tukey" indicates Tukey's biweight loss.
#' @param method.optim Method of optimization of the parameters. This argument
#' is directly delivered to the \code{constrOptim} function provided in the
#' "base" package of R.
#' @param use.Hessian Indicator of whether the Hessian matrix (TRUE) or the
#' gradient vector is used in the Hill bounds.
#' @param level Confidence level to be used in Hill bounds computation.
#'
#' @return List of final parameter estimates, name of robust estimation, loss value
#' and so on.
dr4plEst <- function(dose, response,
init.parm,
trend,
method.init,
method.optim,
method.robust,
use.Hessian,
level) {
convergence <- TRUE
x <- dose # Vector of dose values
y <- response # Vector of responses
n <- length(x) # Number of observations
# Choose the loss function depending on the robust estimation method
loss.fcn <- ErrFcn(method.robust)
# Currently only the gradient function for the squared loss is implemented
grad <- GradientSquaredLossLogIC50
tuning.barrier <- 1e-04 # Tuning parameter for the log Barrier method
### When initial parameter estimates are given
if(!is.null(init.parm)) {
# Check whether initial parameter estimates satisfy constraints
if(init.parm[2] <= 0) {
stop("The IC50 parameter should be positive.")
}
# Use given initial parameter estimates
retheta.init <- init.parm
retheta.init[2] <- log10(init.parm[2])
names(retheta.init) <- c("Upper limit", "Log10(IC50)", "Slope", "Lower limit")
constr.mat <- matrix(c(1, 0, 0, -1), nrow = 1, ncol = 4)
constr.vec <- 0
# Impose a constraint on the slope parameter based on the function argument
# "trend".
if(trend == "decreasing") {
constr.mat <- rbind(constr.mat, matrix(c(0, 0, -1, 0), nrow = 1, ncol = 4))
constr.vec <- c(constr.vec, 0)
} else if(trend == "increasing") {
constr.mat <- rbind(constr.mat, matrix(c(0, 0, 1, 0), nrow = 1, ncol = 4))
constr.vec <- c(constr.vec, 0)
}
# Fit a 4PL model to data
optim.dr4pl <- constrOptim(theta = retheta.init,
f = loss.fcn,
grad = grad,
ui = constr.mat,
ci = constr.vec,
method = method.optim,
hessian = TRUE,
x = x,
y = y)
loss <- optim.dr4pl$value
hessian <- optim.dr4pl$hessian
retheta <- optim.dr4pl$par
theta <- retheta
theta[2] <- 10^retheta[2]
### When initial parameter values are not given.
} else {
## Obtain initial parameter estimates.
theta.init <- FindInitialParms(x, y, trend, method.init, method.robust)
retheta.init <- ParmToLog(theta.init)
Hill.bounds <- FindHillBounds(x, y, theta.init, use.Hessian, level)
constr.mat <- matrix(rbind(c(1, 0, 0, -1),
c(0, 1, 0, 0),
c(0, -1, 0, 0),
c(0, 0, 1, 0),
c(0, 0, -1, 0)),
nrow = 5,
ncol = 4)
constr.vec <- c(0, Hill.bounds$LogTheta2[1], -Hill.bounds$LogTheta2[2],
Hill.bounds$Theta3[1], -Hill.bounds$Theta3[2])
# Impose a constraint on the slope parameter based on the function argument
# "trend".
if(trend == "decreasing") {
constr.mat <- rbind(constr.mat, matrix(c(0, 0, -1, 0), nrow = 1, ncol = 4))
constr.vec <- c(constr.vec, 0)
} else if(trend == "increasing") {
constr.mat <- rbind(constr.mat, matrix(c(0, 0, 1, 0), nrow = 1, ncol = 4))
constr.vec <- c(constr.vec, 0)
}
if(any(constr.mat%*%retheta.init<constr.vec)) {
stop("Initial parameter values are not in the interior of the feasible region.")
}
# Fit the 4PL model
optim.dr4pl <- constrOptim(theta = retheta.init,
f = loss.fcn,
grad = grad,
ui = constr.mat,
ci = constr.vec,
method = method.optim,
hessian = TRUE,
mu = tuning.barrier,
x = x,
y = y)
loss <- optim.dr4pl$value
hessian <- optim.dr4pl$hessian
retheta <- optim.dr4pl$par
theta <- LogToParm(retheta)
}
### If the Hill bounds are hit.
if(any(abs(constr.mat%*%retheta - constr.vec)<tuning.barrier)) {
convergence <- FALSE
}
# Data frame consisting of doses and responses
data.dr4pl <- data.frame(Dose = dose, Response = response)
name.robust <- method.robust
if(is.null(method.robust)) {
name.robust <- "squared"
}
list(convergence = convergence,
data = data.dr4pl,
hessian = hessian,
loss.value = loss,
method.robust = name.robust,
parameters = theta,
sample.size = n)
}
ahwbest/DR4PL documentation built on May 20, 2019, 6:43 p.m.
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#' @name dr4pl
#'
#' @docType package
#'
#' @import graphics
#' @import stats
#' @import ggplot2
#' @import tensor
#' @import Rdpack
NULL
#' @title Fitting 4 Parameter Logistic (4PL) models to dose-response data.
#'
#' @description This function fits a 4PL model to dose-response data. Users can
#' obtain fitted parameter estimates as return values. Using auxiliary functions
#' provided by this R package, users can plot a fitted dose-response curve and
#' obtain confidence intervals of true parameters. In addition, the goodness-of-fit
#' test for model adequacy of the 4PL models can be performed when replicates are
#' available for each dose level.
#'
#' @export
dr4pl <- function(...) UseMethod("dr4pl")
#' @describeIn dr4pl General 4PL model fitting function for analysis of
#' dose-response relation.
#'
#' @param formula Symbolic description of the model to be fit. Either of the
#' form 'response ~ dose' or as a data frame with response values in first
#' column and dose values in second column.
#' @param data Data frame containing variables in the model.
#' @param init.parm Vector of initial parameters to be optimized in the model.
#' @param trend Indicator of whether a dose-response curve is a decreasing
#' \eqn{\theta[3]<0} or increasing curve \eqn{\theta[3]>0}. The default is "auto"
#' which indicates that the trend of the curve is automatically determined by
#' data. The option "decreasing" will impose a restriction \eqn{\theta[3]<=0}
#' while the option "increasing" will impose a restriction \eqn{\theta[3]>=0} in an
#' optimization process.
#' @param method.init Method of obtaining initial values of the parameters.
#' If it is NULL, a default "Mead" method will be used. Assign
#' "logistic" to use the logistic method.
#' @param method.optim Method of optimization of the loss function specified by
#' \code{method.robust}. This function argument is directly passed to the function
#' \code{\link[stats]{constrOptim}} which is provided in the \pkg{base} package of R.
#' @param method.robust Parameter to select loss function for the robust estimation
#' method to be used to fit a model. The argument NULL indicates the sum of squares
#' loss, "absolute" indicates the absolute deviation loss, "Huber" indicates Huber's
#' loss and "Tukey" indicates Tukey's biweight loss.
#' @param use.Hessian Indicator of whether the Hessian matrix (TRUE) or the
#' gradient vector is used in the Hill bounds.
#' @param level Confidence level to be used in Hill bounds computation.
#' @param failure.message Indicator of whether a message indicating attainment of
#' the Hill bounds and possible resolutions will be printed to the console (TRUE)
#' or hidden (FALSE).
#' @param ... Further arguments to be passed to \code{constrOptim}.
#'
#' @return A 'dr4pl' object for which "confint", "gof", "print" and "summary"
#' methods are implemented. For details, see the help page of each method.
#' For example, type \code{?confint.dr4pl} to obtain the confidence intervals
#' of parameters of the 'dr4pl' object.
#'
#' @details This function fits a 4 parameter logistic (4PL) model to dose-response
#' data. A formula of the model is
#' \deqn{\theta[1]+(\theta[4]-\theta[1])/(1+(z/\theta[2])^\theta[3])}
#'
#' \code{method.init} specifies an initialization method to get initial parameter
#' estimates based on data. The currently supported initialization methods are
#' "logistic" and 'Mead'. For further details, see the vignette.
#'
#' \code{method.optim} specifies an optimization method to be used in
#' "constrOptim" function. The currently supported optimization techniques
#' include "Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN" and "Brent". For
#' further details, see the help page of \code{\link[stats]{optim}}.
#'
#' \code{method.robust} chooses a robust estimation method among 4 methods.
#' The method of estimation is usually identified by the loss function of the
#' method. This package supports 4 types of loss functions: sum-of-squares loss,
#' absolute deviation loss, Huber's loss and Tukey's biweight loss. Each of
#' loss function is explained in detail in the vignette.
#'
#' @author Hyowon An, \email{ahwbest@gmail.com}
#' @author Justin T. Landis, \email{jtlandis314@gmail.com}
#' @author Aubrey G. Bailey, \email{aubreybailey@gmail.com}
#' @seealso \code{\link{confint.dr4pl}}, \code{\link{gof.dr4pl}},
#' \code{\link{print.dr4pl}}, \code{\link{summary.dr4pl}}
#' @export
dr4pl.formula <- function(formula,
data = list(),
init.parm = NULL,
trend = "auto",
method.init = "Mead",
method.robust = NULL,
method.optim = "Nelder-Mead",
use.Hessian = FALSE,
level = 0.9999,
failure.message = FALSE,
...) {
mf <- model.frame(formula = formula, data = data)
dose <- model.matrix(attr(mf, "terms"), data = mf)[, 2]
response <- model.response(mf)
obj <- dr4pl.default(dose = dose,
response = response,
init.parm = init.parm,
trend = trend,
method.init = method.init,
method.robust = method.robust,
method.optim = method.optim,
use.Hessian = use.Hessian,
level = level,
failure.message = failure.message,
...)
obj$call <- match.call()
obj$formula <- formula
return(obj)
}
#' @describeIn dr4pl Used in the default case, supplying a single dose and
#' response variable
#'
#' @param dose Vector of dose levels
#' @param response Vector of responses
#'
#' @examples
#' a <- dr4pl(dose = sample_data_1$Dose,
#' response = sample_data_1$Response,
#' method.init = "logistic")
#' plot(a)
#'
#' ##Assign method.init = "Mead" to use Mead's method of estimation.
#' # Use method.robust to select desired loss function
#' b <- dr4pl(Response~Dose,
#' data = sample_data_4,
#' method.init = "Mead",
#' method.robust = "Tukey" )
#' plot(b)
#'
#' ##compatable with ggplot
#' library(ggplot2)
#' c <- dr4pl(Response~Dose,
#' data = drc_error_2,
#' method.init = "Mead",
#' method.robust = "absolute" )
#' d <- plot(c)
#' d + scale_x_log10(breaks = c(.00135, .0135, .135, 1.35, 13.5))
#' @export
dr4pl.default <- function(dose,
response,
init.parm = NULL,
trend = "auto",
method.init = "Mead",
method.robust = NULL,
method.optim = "Nelder-Mead",
use.Hessian = FALSE,
level = 0.9999,
failure.message = FALSE,
...) {
types.trend <- c("auto", "decreasing", "increasing")
types.method.init <- c("logistic", "Mead")
types.method.optim <- c("Nelder-Mead", "BFGS", "CG", "SANN")
### Check errors in functions arguments
if(!is.numeric(dose)||!is.numeric(response)) {
stop("Both doses and responses should be numeric.")
}
if(any(dose<0)) {
stop("Dose levels should be nonnegative.")
}
if(length(dose) == 0 || length(response) == 0 || length(dose) != length(response)) {
stop("The same numbers of dose and response values should be supplied.")
}
if(!is.element(method.init, types.method.init)) {
stop("The initialization method name should be one of \"logistic\" and \"Mead\".")
}
if(!is.element(method.optim, types.method.optim)) {
stop("The optimization method name should be one of \"Nelder-Mead\", \"BFGS\",
\"CG\", \"L-BFGS-B\" and \"SANN\".")
}
if(!is.element(trend, types.trend)) {
stop("The type of the \"trend\" parameter should be one of \"auto\", \"decreasing\" and \"increasing\".")
}
# Fit a 4PL model
obj <- dr4plEst(dose = dose,
response = response,
init.parm = init.parm,
trend = trend,
method.init = method.init,
method.robust = method.robust,
method.optim = method.optim,
use.Hessian = use.Hessian,
level = level)
obj$call <- match.call()
class(obj) <- "dr4pl"
# If any robust estimation method is indicated, report outliers to a user.
if(!is.null(method.robust)) {
theta <- obj$parameters # Robust parameter estimates
residuals <- Residual(theta, dose, response) # Residuals
indices.outlier <- OutlierDetection(residuals)
obj$idx.outlier <- indices.outlier
obj$robust.plot <- plot(obj, indices.outlier = indices.outlier)
}
message.diagnosis <- NULL
### When convergence failure happens.
if(obj$convergence == FALSE) {
## Decide the method of robust estimation which is more robust than the method
## input by a user.
if(is.null(method.robust)) {
method.robust.new <- "absolute"
} else if(is.element(method.robust, c("absolute", "Huber"))) {
method.robust.new <- "Tukey"
} else {
stop("Convergence failure happened but no resolution could be found.")
}
n <- obj$sample.size # Number of data points
# Fit a 4PL model to data
obj.robust <- dr4plEst(dose = dose,
response = response,
init.parm = init.parm,
trend = trend,
method.init = method.init,
method.robust = method.robust.new,
method.optim = method.optim,
use.Hessian = use.Hessian,
level = level)
obj.robust$call <- match.call()
class(obj.robust) <- "dr4pl"
## Detect outliers and report them.
theta <- obj.robust$parameters # Robust parameter estimates
residuals <- Residual(theta, dose, response) # Residuals
indices.outlier <- OutlierDetection(residuals)
obj.robust$idx.outlier <- indices.outlier
obj.robust$robust.plot <- plot(obj.robust, indices.outlier = indices.outlier)
## Print different messages to the console depending on the convergence success
## of a robust fit.
if(obj.robust$convergence) {
message.diagnosis <-
paste("The Hill bounds have been hit during optimization, but other robust ",
"estimation was succesful.\n",
"Please refer to \"dr4pl.robust\" variable for diagnosis.\n",
sep = "")
} else {
message.diagnosis <-
paste("The Hill bounds have been hit during optimization with ",
obj$method.robust, " and ", obj.robust$method.robust, " methods.\n",
"Please try other initialization and robust estimation methods.\n",
sep = "")
}
obj$dr4pl.robust <- obj.robust
obj$message.diagnosis <- message.diagnosis
}
if(failure.message&&!is.null(message.diagnosis)) {
cat(message.diagnosis)
}
return(obj)
}
#' @title Private function to fit the 4PL model to dose-response data
#'
#' @description Private function that actually fits the 4PL model to data. If the
#' Hill bounds are attained at the end of optimization processes, then an
#' indicator of convergence failure so that \code{\link{dr4pl.default}} can
#' look for a remedy for convergence failure.
#'
#' @name dr4plEst
#'
#' @param dose Vector of dose levels
#' @param response Vector of responses
#' @param init.parm Vector of initial parameters of the 4PL model supplied by a
#' user.
#' @param trend Indicator of whether a dose-response curve is a decreasing
#' \eqn{\theta[3]<0} or increasing curve \eqn{\theta[3]>0}. The default is "auto"
#' which indicates that the trend of the curve is automatically determined by
#' data. The option "decreasing" will impose a restriction \eqn{\theta[3]<=0}
#' while the option "increasing" will impose a restriction \eqn{\theta[3]>=0} in an
#' optimization process.
#' @param method.init Method of obtaining initial values of the parameters.
#' Should be one of "logistic" for the logistic method or "Mead" for the Mead
#' method. The default option is the Mead method.
#' @param method.robust Parameter to select loss function for the robust estimation
#' method to be used to fit a model. The argument NULL indicates the sum of squares
#' loss, "absolute" indicates the absolute deviation loss, "Huber" indicates Huber's
#' loss and "Tukey" indicates Tukey's biweight loss.
#' @param method.optim Method of optimization of the parameters. This argument
#' is directly delivered to the \code{constrOptim} function provided in the
#' "base" package of R.
#' @param use.Hessian Indicator of whether the Hessian matrix (TRUE) or the
#' gradient vector is used in the Hill bounds.
#' @param level Confidence level to be used in Hill bounds computation.
#'
#' @return List of final parameter estimates, name of robust estimation, loss value
#' and so on.
dr4plEst <- function(dose, response,
init.parm,
trend,
method.init,
method.optim,
method.robust,
use.Hessian,
level) {
convergence <- TRUE
x <- dose # Vector of dose values
y <- response # Vector of responses
n <- length(x) # Number of observations
# Choose the loss function depending on the robust estimation method
loss.fcn <- ErrFcn(method.robust)
# Currently only the gradient function for the squared loss is implemented
grad <- GradientSquaredLossLogIC50
tuning.barrier <- 1e-04 # Tuning parameter for the log Barrier method
### When initial parameter estimates are given
if(!is.null(init.parm)) {
# Check whether initial parameter estimates satisfy constraints
if(init.parm[2] <= 0) {
stop("The IC50 parameter should be positive.")
}
# Use given initial parameter estimates
retheta.init <- init.parm
retheta.init[2] <- log10(init.parm[2])
names(retheta.init) <- c("Upper limit", "Log10(IC50)", "Slope", "Lower limit")
constr.mat <- matrix(c(1, 0, 0, -1), nrow = 1, ncol = 4)
constr.vec <- 0
# Impose a constraint on the slope parameter based on the function argument
# "trend".
if(trend == "decreasing") {
constr.mat <- rbind(constr.mat, matrix(c(0, 0, -1, 0), nrow = 1, ncol = 4))
constr.vec <- c(constr.vec, 0)
} else if(trend == "increasing") {
constr.mat <- rbind(constr.mat, matrix(c(0, 0, 1, 0), nrow = 1, ncol = 4))
constr.vec <- c(constr.vec, 0)
}
# Fit a 4PL model to data
optim.dr4pl <- constrOptim(theta = retheta.init,
f = loss.fcn,
grad = grad,
ui = constr.mat,
ci = constr.vec,
method = method.optim,
hessian = TRUE,
x = x,
y = y)
loss <- optim.dr4pl$value
hessian <- optim.dr4pl$hessian
retheta <- optim.dr4pl$par
theta <- retheta
theta[2] <- 10^retheta[2]
### When initial parameter values are not given.
} else {
## Obtain initial parameter estimates.
theta.init <- FindInitialParms(x, y, trend, method.init, method.robust)
retheta.init <- ParmToLog(theta.init)
Hill.bounds <- FindHillBounds(x, y, theta.init, use.Hessian, level)
constr.mat <- matrix(rbind(c(1, 0, 0, -1),
c(0, 1, 0, 0),
c(0, -1, 0, 0),
c(0, 0, 1, 0),
c(0, 0, -1, 0)),
nrow = 5,
ncol = 4)
constr.vec <- c(0, Hill.bounds$LogTheta2[1], -Hill.bounds$LogTheta2[2],
Hill.bounds$Theta3[1], -Hill.bounds$Theta3[2])
# Impose a constraint on the slope parameter based on the function argument
# "trend".
if(trend == "decreasing") {
constr.mat <- rbind(constr.mat, matrix(c(0, 0, -1, 0), nrow = 1, ncol = 4))
constr.vec <- c(constr.vec, 0)
} else if(trend == "increasing") {
constr.mat <- rbind(constr.mat, matrix(c(0, 0, 1, 0), nrow = 1, ncol = 4))
constr.vec <- c(constr.vec, 0)
}
if(any(constr.mat%*%retheta.init<constr.vec)) {
stop("Initial parameter values are not in the interior of the feasible region.")
}
# Fit the 4PL model
optim.dr4pl <- constrOptim(theta = retheta.init,
f = loss.fcn,
grad = grad,
ui = constr.mat,
ci = constr.vec,
method = method.optim,
hessian = TRUE,
mu = tuning.barrier,
x = x,
y = y)
loss <- optim.dr4pl$value
hessian <- optim.dr4pl$hessian
retheta <- optim.dr4pl$par
theta <- LogToParm(retheta)
}
### If the Hill bounds are hit.
if(any(abs(constr.mat%*%retheta - constr.vec)<tuning.barrier)) {
convergence <- FALSE
}
# Data frame consisting of doses and responses
data.dr4pl <- data.frame(Dose = dose, Response = response)
name.robust <- method.robust
if(is.null(method.robust)) {
name.robust <- "squared"
}
list(convergence = convergence,
data = data.dr4pl,
hessian = hessian,
loss.value = loss,
method.robust = name.robust,
parameters = theta,
sample.size = n)
}
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