R/tcplFit.R
In daynefiler/tcpl: ToxCast Data Analysis Pipeline
#-------------------------------------------------------------------------------
# tcplFit: Fit the data
#-------------------------------------------------------------------------------
#' @title Fit the data with the constant, hill, and gain-loss models
#'
#' @description
#' \code{tcplFit} fits the constant, hill, and gain-loss models to the given data
#' and returns some summary statistics and the fit parameters in a list.
#'
#' @param logc Numeric, log concentration values
#' @param resp Numeric, normalized response values
#' @param bmad Numeric, the baseline median absolute deviation for the entire
#' assay
#' @param force.fit Logical, TRUE indicates to attempt fitting every
#' concentration series
#' @param \dots Any other data to be included in list output.
#'
#' @details
#' By default, \code{tcplFit} will only attempt to fit concentration series
#' when at least one median value is greater than 3*bmad.
#'
#' @examples
#' logc <- 1:10
#' resp <- sapply(1:10, tcplHillVal, ga = 5, tp = 50, gw = 0.5)
#' params <- tcplFit(logc = logc, resp = resp, bmad = 10)
#' plot(resp ~ logc)
#' tcplAddModel(pars = params, modl = "hill")
#'
#' @return List of summary values and fit parameters for the given data.
#'
#' @seealso \code{\link{tcplObjCnst}}, \code{\link{tcplObjHill}},
#' \code{\link{tcplObjGnls}}, \code{\link{constrOptim}}
#'
#' @importFrom numDeriv hessian
#' @importFrom stats optim constrOptim median mad
#' @importFrom methods is
#' @export
tcplFit <- function(logc, resp, bmad, force.fit = FALSE, ...) {
## Variable-binding to pass R CMD Check
hill_tp <- hill_ga <- hill_gw <- gnls_ga <- gnls_gw <- gnls_la <- NULL
gnls_lw <- gnls_tp <- hill_tp_sd <- hill_ga_sd <- hill_gw_sd <- NULL
hill_er <- hill_er_sd <- gnls_tp_sd <- gnls_ga_sd <- gnls_gw_sd <- NULL
gnls_la_sd <- gnls_lw_sd <- gnls_er <- gnls_er_sd <- NULL
fenv <- environment()
bmad <- min(bmad)
rmns <- tapply(resp, logc, mean)
rmds <- tapply(resp, logc, median)
mmed <- max(rmds)
mmed_conc <- as.numeric(names(which.max(rmds)))
hprs <- paste0("hill_", c("tp", "ga", "gw", "er"))
hsds <- paste0("hill_", c("tp", "ga", "gw", "er"), "_sd")
gprs <- paste0("gnls_", c("tp", "ga", "gw", "la", "lw", "er"))
gsds <- paste0("gnls_", c("tp", "ga", "gw", "la", "lw", "er"), "_sd")
resp_max <- max(resp)
resp_min <- min(resp)
logc_med <- median(logc)
logc_min <- min(logc)
logc_max <- max(logc)
ncnc <- lu(logc)
npts <- length(resp)
nrep <- as.numeric(median(tapply(resp, logc, lu))) # Meidan number replicates
nmed_gtbl <- lw(rmds >= 3 * bmad) # Number of medians above 3 * bmad
## Do not fit anything with less than four concentrations of data.
if (length(rmds) >= 4) {
er_est <- if ((rmad <- mad(resp)) > 0) log(rmad) else log(1e-32)
###----------------------- Fit the Constant Model -----------------------###
cfit <- optim(er_est,
tcplObjCnst,
method = "Brent",
lower = er_est - 2,
upper = er_est + 2,
control = list(fnscale = -1,
reltol = 1e-4,
maxit = 500),
resp = resp)
if (!is(cfit, "try-error")) {
cnst <- 1L
cnst_er <- cfit$par
caic <- 2 - 2*cfit$value # 2*length(cfit$par) - 2*cfit$value
## Calculate the rmse for constant
crme <- sqrt(mean((0 - resp)^2, na.rm = TRUE))
} else {
cnst <- 0L
cnst_er <- NA_real_
caic <- NA_integer_
crme <- NA_real_
}
if (lw(rmds >= 3*bmad) > 0 | force.fit) {
###------------------------ Fit the Hill Model ------------------------###
## Starting parameters for the Hill Model
# cind <- (ceiling(length(meds)/2) + 1):length(meds)
h <- c(mmed, # top (tp)
mmed_conc - 1, # logAC50 (ga)
1.2, # hill coefficient (gw)
er_est) # logSigma (er)
if (h[1] == 0) h[1] <- 0.1
## Generate the bound matrices to constrain the model.
# tp ga gw er
hUi <- matrix(c( 1, 0, 0, 0,
-1, 0, 0, 0,
0, 1, 0, 0,
0, -1, 0, 0,
0, 0, 1, 0,
0, 0, -1, 0),
byrow = TRUE, nrow = 6, ncol = 4)
hbnds <- c(0, -1.2*resp_max, # tp bounds
logc_min - 2, -(logc_max + 0.5), # ga bounds
0.3, -8) # gw bounds
hCi <- matrix(hbnds, nrow = 6, ncol = 1)
## Optimize the hill model
hfit <- try(constrOptim(h,
tcplObjHill,
ui = hUi,
ci = hCi,
mu = 1e-6,
method = "Nelder-Mead",
control = list(fnscale = -1,
reltol = 1e-10,
maxit = 6000),
lconc = logc,
resp = resp),
silent = TRUE)
## Generate some summary statistics
if (!is(hfit, "try-error")) { # Hill model fit the data
hill <- 1L
haic <- 8 - 2*hfit$value # 2*length(hfit$par) - 2*hfit$value
mapply(assign,
c(hprs),
hfit$par,
MoreArgs = list(envir = fenv))
## Calculate rmse for hill
hill_modl <- hill_tp/(1 + 10^((hill_ga - logc)*hill_gw))
hrme <- sqrt(mean((hill_modl - resp)^2, na.rm = TRUE))
## Calculate the sd for the hill parameters
hfit$cov <- try(solve(-hessian(tcplObjHill,
hfit$par,
lconc = logc,
resp = resp)),
silent = TRUE)
if (!is(hfit$cov, "try-error")) { # Could invert hill Hessian
hcov <- 1L
hdiag_sqrt <- suppressWarnings(sqrt(diag(hfit$cov)))
if (any(is.nan(hdiag_sqrt))) {
mapply(assign,
hsds,
NaN,
MoreArgs = list(envir = fenv))
} else {
mapply(assign,
hsds,
hdiag_sqrt,
MoreArgs = list(envir = fenv))
}
} else { # Could not invert hill Hessian
hcov <- 0L
mapply(assign,
c(hsds),
NA_real_,
MoreArgs = list(envir = fenv))
}
} else { # Hill model did not fit the data
hill <- 0L
haic <- NA_real_
hcov <- NA_integer_
hrme <- NA_real_
mapply(assign,
c(hprs, hsds),
NA_real_,
MoreArgs = list(envir = fenv))
}
###--------------------- Fit the Gain-Loss Model ----------------------###
## Starting parameters for the Gain-Loss Model
# cind <- (ceiling(length(meds)/2) + 1):length(meds)
g <- c(mmed, # top (tp)
mmed_conc - 1, # gain logAC50 (ga)
1.2, # gain hill coefficient (gw)
mmed_conc + 0.1, # loss logAC50 (la)
5, # loss hill coefficient (lw)
er_est) # logSigma (er)
if (g[1] == 0) g[1] <- 0.1
## Generate the bound matrices to constrain the model.
# tp ga gw la lw er
gUi <- matrix(c( 1, 0, 0, 0, 0, 0,
-1, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0,
0, -1, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0,
0, 0, -1 , 0, 0, 0,
0, 0, 0, 1, 0, 0,
0, 0, 0, -1, 0, 0,
0, 0, 0, 0, 1, 0,
0, 0, 0, 0, -1, 0,
0, -1, 0, 1, 0, 0),
byrow = TRUE, nrow = 11, ncol = 6)
gbnds <- c(0, -1.2*resp_max, # tp bounds
logc_min - 2, -(logc_max), # ga bounds
0.3, -8, # gw bounds
logc_min - 2, -(logc_max + 2), # la bounds
0.3, -18, # lw bounds
0.25) # ga < la
# if (mmed_conc > logc_min) g[7] <- mmed_conc - 0.25
gCi <- matrix(gbnds, nrow = 11, ncol = 1)
## Optimize the gnls model
gfit <- try(constrOptim(g,
tcplObjGnls,
ui = gUi,
ci = gCi,
mu = 1e-6,
method = "Nelder-Mead",
control = list(fnscale = -1,
reltol = 1e-10,
maxit = 6000),
lconc = logc,
resp = resp),
silent = TRUE)
## Generate some summary statistics
if (!is(gfit, "try-error")) { # Gain-loss fit the data
gnls <- 1L
gaic <- 12 - 2*gfit$value # 2*length(gfit$par) - 2*gfit$value
mapply(assign,
c(gprs),
gfit$par,
MoreArgs = list(envir = fenv))
## Calculate rmse for gnls
gnls_gn <- (1/(1 + 10^((gnls_ga - logc)*gnls_gw)))
gnls_ls <- (1/(1 + 10^((logc - gnls_la)*gnls_lw)))
gnls_modl <- gnls_tp * gnls_gn * gnls_ls
grme <- sqrt(mean((gnls_modl - resp)^2, na.rm = TRUE))
## Calculate the sd for the gnls parameters
gfit$cov <- try(solve(-hessian(tcplObjGnls,
gfit$par,
lconc = logc,
resp = resp)),
silent = TRUE)
if (!is(gfit$cov, "try-error")) { # Could invert gnls Hessian
gcov <- 1L
gdiag_sqrt <- suppressWarnings(sqrt(diag(gfit$cov)))
if (any(is.nan(gdiag_sqrt))) {
mapply(assign,
gsds,
NaN,
MoreArgs = list(envir = fenv))
} else {
mapply(assign,
gsds,
gdiag_sqrt,
MoreArgs = list(envir = fenv))
}
} else { # Could not invert gnls Hessian
gcov <- 0L
mapply(assign,
c(gsds),
NA_real_,
MoreArgs = list(envir = fenv))
}
} else { # Gain-loss did not fit the data
gnls <- 0L
gaic <- NA_real_
gcov <- NA_integer_
grme <- NA_real_
mapply(assign,
c(gprs, gsds),
NA_real_,
MoreArgs = list(envir = fenv))
}
} else { # None of the response values fell outside 3*bmad
hill <- NA_integer_
haic <- NA_real_
hcov <- NA_integer_
hrme <- NA_real_
gnls <- NA_integer_
gaic <- NA_real_
gcov <- NA_integer_
grme <- NA_real_
mapply(assign,
c(hprs, hsds, gprs, gsds),
NA_real_,
MoreArgs = list(envir = fenv))
}
} else { # Data has response data for less than four concentrations.
cnst <- NA_integer_
cnst_er <- NA_real_
caic <- NA_real_
crme <- NA_real_
hill <- NA_integer_
haic <- NA_real_
hcov <- NA_integer_
hrme <- NA_real_
gnls <- NA_integer_
gaic <- NA_real_
gcov <- NA_integer_
grme <- NA_real_
mapply(assign,
c(hprs, hsds, gprs, gsds),
NA_real_,
MoreArgs = list(envir = fenv))
}
out <- list(resp_max = resp_max,
resp_min = resp_min,
max_mean = max(rmns),
max_mean_conc = as.numeric(names(which.max(rmns))),
max_med = mmed,
max_med_conc = mmed_conc,
logc_max = logc_max,
logc_min = logc_min,
cnst = cnst,
hill = hill,
hcov = hcov,
gnls = gnls,
gcov = gcov,
cnst_er = cnst_er,
cnst_aic = caic,
cnst_rmse = crme,
hill_tp = hill_tp,
hill_tp_sd = hill_tp_sd,
hill_ga = hill_ga,
hill_ga_sd = hill_ga_sd,
hill_gw = hill_gw,
hill_gw_sd = hill_gw_sd,
hill_er = hill_er,
hill_er_sd = hill_er_sd,
hill_aic = haic,
hill_rmse = hrme,
gnls_tp = gnls_tp,
gnls_tp_sd = gnls_tp_sd,
gnls_ga = gnls_ga,
gnls_ga_sd = gnls_ga_sd,
gnls_gw = gnls_gw,
gnls_gw_sd = gnls_gw_sd,
gnls_la = gnls_la,
gnls_la_sd = gnls_la_sd,
gnls_lw = gnls_lw,
gnls_lw_sd = gnls_lw_sd,
gnls_er = gnls_er,
gnls_er_sd = gnls_er_sd,
gnls_aic = gaic,
gnls_rmse = grme,
nconc = ncnc,
npts = npts,
nrep = nrep,
nmed_gtbl = nmed_gtbl,
...)
out
}
#-------------------------------------------------------------------------------
daynefiler/tcpl documentation built on May 15, 2019, 1:18 a.m.
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#-------------------------------------------------------------------------------
# tcplFit: Fit the data
#-------------------------------------------------------------------------------
#' @title Fit the data with the constant, hill, and gain-loss models
#'
#' @description
#' \code{tcplFit} fits the constant, hill, and gain-loss models to the given data
#' and returns some summary statistics and the fit parameters in a list.
#'
#' @param logc Numeric, log concentration values
#' @param resp Numeric, normalized response values
#' @param bmad Numeric, the baseline median absolute deviation for the entire
#' assay
#' @param force.fit Logical, TRUE indicates to attempt fitting every
#' concentration series
#' @param \dots Any other data to be included in list output.
#'
#' @details
#' By default, \code{tcplFit} will only attempt to fit concentration series
#' when at least one median value is greater than 3*bmad.
#'
#' @examples
#' logc <- 1:10
#' resp <- sapply(1:10, tcplHillVal, ga = 5, tp = 50, gw = 0.5)
#' params <- tcplFit(logc = logc, resp = resp, bmad = 10)
#' plot(resp ~ logc)
#' tcplAddModel(pars = params, modl = "hill")
#'
#' @return List of summary values and fit parameters for the given data.
#'
#' @seealso \code{\link{tcplObjCnst}}, \code{\link{tcplObjHill}},
#' \code{\link{tcplObjGnls}}, \code{\link{constrOptim}}
#'
#' @importFrom numDeriv hessian
#' @importFrom stats optim constrOptim median mad
#' @importFrom methods is
#' @export
tcplFit <- function(logc, resp, bmad, force.fit = FALSE, ...) {
## Variable-binding to pass R CMD Check
hill_tp <- hill_ga <- hill_gw <- gnls_ga <- gnls_gw <- gnls_la <- NULL
gnls_lw <- gnls_tp <- hill_tp_sd <- hill_ga_sd <- hill_gw_sd <- NULL
hill_er <- hill_er_sd <- gnls_tp_sd <- gnls_ga_sd <- gnls_gw_sd <- NULL
gnls_la_sd <- gnls_lw_sd <- gnls_er <- gnls_er_sd <- NULL
fenv <- environment()
bmad <- min(bmad)
rmns <- tapply(resp, logc, mean)
rmds <- tapply(resp, logc, median)
mmed <- max(rmds)
mmed_conc <- as.numeric(names(which.max(rmds)))
hprs <- paste0("hill_", c("tp", "ga", "gw", "er"))
hsds <- paste0("hill_", c("tp", "ga", "gw", "er"), "_sd")
gprs <- paste0("gnls_", c("tp", "ga", "gw", "la", "lw", "er"))
gsds <- paste0("gnls_", c("tp", "ga", "gw", "la", "lw", "er"), "_sd")
resp_max <- max(resp)
resp_min <- min(resp)
logc_med <- median(logc)
logc_min <- min(logc)
logc_max <- max(logc)
ncnc <- lu(logc)
npts <- length(resp)
nrep <- as.numeric(median(tapply(resp, logc, lu))) # Meidan number replicates
nmed_gtbl <- lw(rmds >= 3 * bmad) # Number of medians above 3 * bmad
## Do not fit anything with less than four concentrations of data.
if (length(rmds) >= 4) {
er_est <- if ((rmad <- mad(resp)) > 0) log(rmad) else log(1e-32)
###----------------------- Fit the Constant Model -----------------------###
cfit <- optim(er_est,
tcplObjCnst,
method = "Brent",
lower = er_est - 2,
upper = er_est + 2,
control = list(fnscale = -1,
reltol = 1e-4,
maxit = 500),
resp = resp)
if (!is(cfit, "try-error")) {
cnst <- 1L
cnst_er <- cfit$par
caic <- 2 - 2*cfit$value # 2*length(cfit$par) - 2*cfit$value
## Calculate the rmse for constant
crme <- sqrt(mean((0 - resp)^2, na.rm = TRUE))
} else {
cnst <- 0L
cnst_er <- NA_real_
caic <- NA_integer_
crme <- NA_real_
}
if (lw(rmds >= 3*bmad) > 0 | force.fit) {
###------------------------ Fit the Hill Model ------------------------###
## Starting parameters for the Hill Model
# cind <- (ceiling(length(meds)/2) + 1):length(meds)
h <- c(mmed, # top (tp)
mmed_conc - 1, # logAC50 (ga)
1.2, # hill coefficient (gw)
er_est) # logSigma (er)
if (h[1] == 0) h[1] <- 0.1
## Generate the bound matrices to constrain the model.
# tp ga gw er
hUi <- matrix(c( 1, 0, 0, 0,
-1, 0, 0, 0,
0, 1, 0, 0,
0, -1, 0, 0,
0, 0, 1, 0,
0, 0, -1, 0),
byrow = TRUE, nrow = 6, ncol = 4)
hbnds <- c(0, -1.2*resp_max, # tp bounds
logc_min - 2, -(logc_max + 0.5), # ga bounds
0.3, -8) # gw bounds
hCi <- matrix(hbnds, nrow = 6, ncol = 1)
## Optimize the hill model
hfit <- try(constrOptim(h,
tcplObjHill,
ui = hUi,
ci = hCi,
mu = 1e-6,
method = "Nelder-Mead",
control = list(fnscale = -1,
reltol = 1e-10,
maxit = 6000),
lconc = logc,
resp = resp),
silent = TRUE)
## Generate some summary statistics
if (!is(hfit, "try-error")) { # Hill model fit the data
hill <- 1L
haic <- 8 - 2*hfit$value # 2*length(hfit$par) - 2*hfit$value
mapply(assign,
c(hprs),
hfit$par,
MoreArgs = list(envir = fenv))
## Calculate rmse for hill
hill_modl <- hill_tp/(1 + 10^((hill_ga - logc)*hill_gw))
hrme <- sqrt(mean((hill_modl - resp)^2, na.rm = TRUE))
## Calculate the sd for the hill parameters
hfit$cov <- try(solve(-hessian(tcplObjHill,
hfit$par,
lconc = logc,
resp = resp)),
silent = TRUE)
if (!is(hfit$cov, "try-error")) { # Could invert hill Hessian
hcov <- 1L
hdiag_sqrt <- suppressWarnings(sqrt(diag(hfit$cov)))
if (any(is.nan(hdiag_sqrt))) {
mapply(assign,
hsds,
NaN,
MoreArgs = list(envir = fenv))
} else {
mapply(assign,
hsds,
hdiag_sqrt,
MoreArgs = list(envir = fenv))
}
} else { # Could not invert hill Hessian
hcov <- 0L
mapply(assign,
c(hsds),
NA_real_,
MoreArgs = list(envir = fenv))
}
} else { # Hill model did not fit the data
hill <- 0L
haic <- NA_real_
hcov <- NA_integer_
hrme <- NA_real_
mapply(assign,
c(hprs, hsds),
NA_real_,
MoreArgs = list(envir = fenv))
}
###--------------------- Fit the Gain-Loss Model ----------------------###
## Starting parameters for the Gain-Loss Model
# cind <- (ceiling(length(meds)/2) + 1):length(meds)
g <- c(mmed, # top (tp)
mmed_conc - 1, # gain logAC50 (ga)
1.2, # gain hill coefficient (gw)
mmed_conc + 0.1, # loss logAC50 (la)
5, # loss hill coefficient (lw)
er_est) # logSigma (er)
if (g[1] == 0) g[1] <- 0.1
## Generate the bound matrices to constrain the model.
# tp ga gw la lw er
gUi <- matrix(c( 1, 0, 0, 0, 0, 0,
-1, 0, 0, 0, 0, 0,
0, 1, 0, 0, 0, 0,
0, -1, 0, 0, 0, 0,
0, 0, 1, 0, 0, 0,
0, 0, -1 , 0, 0, 0,
0, 0, 0, 1, 0, 0,
0, 0, 0, -1, 0, 0,
0, 0, 0, 0, 1, 0,
0, 0, 0, 0, -1, 0,
0, -1, 0, 1, 0, 0),
byrow = TRUE, nrow = 11, ncol = 6)
gbnds <- c(0, -1.2*resp_max, # tp bounds
logc_min - 2, -(logc_max), # ga bounds
0.3, -8, # gw bounds
logc_min - 2, -(logc_max + 2), # la bounds
0.3, -18, # lw bounds
0.25) # ga < la
# if (mmed_conc > logc_min) g[7] <- mmed_conc - 0.25
gCi <- matrix(gbnds, nrow = 11, ncol = 1)
## Optimize the gnls model
gfit <- try(constrOptim(g,
tcplObjGnls,
ui = gUi,
ci = gCi,
mu = 1e-6,
method = "Nelder-Mead",
control = list(fnscale = -1,
reltol = 1e-10,
maxit = 6000),
lconc = logc,
resp = resp),
silent = TRUE)
## Generate some summary statistics
if (!is(gfit, "try-error")) { # Gain-loss fit the data
gnls <- 1L
gaic <- 12 - 2*gfit$value # 2*length(gfit$par) - 2*gfit$value
mapply(assign,
c(gprs),
gfit$par,
MoreArgs = list(envir = fenv))
## Calculate rmse for gnls
gnls_gn <- (1/(1 + 10^((gnls_ga - logc)*gnls_gw)))
gnls_ls <- (1/(1 + 10^((logc - gnls_la)*gnls_lw)))
gnls_modl <- gnls_tp * gnls_gn * gnls_ls
grme <- sqrt(mean((gnls_modl - resp)^2, na.rm = TRUE))
## Calculate the sd for the gnls parameters
gfit$cov <- try(solve(-hessian(tcplObjGnls,
gfit$par,
lconc = logc,
resp = resp)),
silent = TRUE)
if (!is(gfit$cov, "try-error")) { # Could invert gnls Hessian
gcov <- 1L
gdiag_sqrt <- suppressWarnings(sqrt(diag(gfit$cov)))
if (any(is.nan(gdiag_sqrt))) {
mapply(assign,
gsds,
NaN,
MoreArgs = list(envir = fenv))
} else {
mapply(assign,
gsds,
gdiag_sqrt,
MoreArgs = list(envir = fenv))
}
} else { # Could not invert gnls Hessian
gcov <- 0L
mapply(assign,
c(gsds),
NA_real_,
MoreArgs = list(envir = fenv))
}
} else { # Gain-loss did not fit the data
gnls <- 0L
gaic <- NA_real_
gcov <- NA_integer_
grme <- NA_real_
mapply(assign,
c(gprs, gsds),
NA_real_,
MoreArgs = list(envir = fenv))
}
} else { # None of the response values fell outside 3*bmad
hill <- NA_integer_
haic <- NA_real_
hcov <- NA_integer_
hrme <- NA_real_
gnls <- NA_integer_
gaic <- NA_real_
gcov <- NA_integer_
grme <- NA_real_
mapply(assign,
c(hprs, hsds, gprs, gsds),
NA_real_,
MoreArgs = list(envir = fenv))
}
} else { # Data has response data for less than four concentrations.
cnst <- NA_integer_
cnst_er <- NA_real_
caic <- NA_real_
crme <- NA_real_
hill <- NA_integer_
haic <- NA_real_
hcov <- NA_integer_
hrme <- NA_real_
gnls <- NA_integer_
gaic <- NA_real_
gcov <- NA_integer_
grme <- NA_real_
mapply(assign,
c(hprs, hsds, gprs, gsds),
NA_real_,
MoreArgs = list(envir = fenv))
}
out <- list(resp_max = resp_max,
resp_min = resp_min,
max_mean = max(rmns),
max_mean_conc = as.numeric(names(which.max(rmns))),
max_med = mmed,
max_med_conc = mmed_conc,
logc_max = logc_max,
logc_min = logc_min,
cnst = cnst,
hill = hill,
hcov = hcov,
gnls = gnls,
gcov = gcov,
cnst_er = cnst_er,
cnst_aic = caic,
cnst_rmse = crme,
hill_tp = hill_tp,
hill_tp_sd = hill_tp_sd,
hill_ga = hill_ga,
hill_ga_sd = hill_ga_sd,
hill_gw = hill_gw,
hill_gw_sd = hill_gw_sd,
hill_er = hill_er,
hill_er_sd = hill_er_sd,
hill_aic = haic,
hill_rmse = hrme,
gnls_tp = gnls_tp,
gnls_tp_sd = gnls_tp_sd,
gnls_ga = gnls_ga,
gnls_ga_sd = gnls_ga_sd,
gnls_gw = gnls_gw,
gnls_gw_sd = gnls_gw_sd,
gnls_la = gnls_la,
gnls_la_sd = gnls_la_sd,
gnls_lw = gnls_lw,
gnls_lw_sd = gnls_lw_sd,
gnls_er = gnls_er,
gnls_er_sd = gnls_er_sd,
gnls_aic = gaic,
gnls_rmse = grme,
nconc = ncnc,
npts = npts,
nrep = nrep,
nmed_gtbl = nmed_gtbl,
...)
out
}
#-------------------------------------------------------------------------------
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