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# From morse package v3.3.1 29.06.2021
# Functions imported due to dependency issues with rjags
# morse package under GPL (>=2) https://cran.r-project.org/package=morse
#' Checking goodness-of-fit method for \code{survFitPredict} and
#' \code{survFitPredict_Nsurv} objects
#'
#' Function from the \code{morse v 3.3.1} package.
#' It returns measures of goodness-of-fit for predictions.
#'
#' @rdname predict_check
#'
#' @param object an object used to select a method \code{predict_Nsurv_check}
#' @param \dots Further arguments to be passed to generic methods
#'
#' @export
predict_Nsurv_check <- function(object, ...){
UseMethod("predict_Nsurv_check")
}
#' Compute criteria to check model performance
#'
#' Function from the \code{morse v 3.3.1} package.
#' Provide various criteria for assessment of the model performance:
#' (i) percentage of observation within the 95\% credible
#' interval of the Posterior Prediction Check (PPC), the Normalised Root Mean
#' Square Error (NRMSE) and the Survival Probability Prediction Error (SPPE) as
#' recommended by the recent Scientific Opinion from EFSA (2018).
#'
#' @rdname predict_check
#'
#' @param object an object of class \code{survFitPredict_Nsurv}
#' @param \dots Further arguments to be passed to generic methods
#'
#' @return The function return a list with three items:
#' \item{PPC}{The criterion, in percent, compares the predicted median numbers
#' of survivors associated to their uncertainty limits with the observed numbers
#' of survivors. Based on experience, PPC resulting in less than \eqn{50\%} of the
#' observations within the uncertainty limits indicate poor model performance. A fit of
#' \eqn{100\%} may hide too large uncertainties of prediction (so covering all data).}
#' \item{PPC_global}{percentage of PPC for the whole data set by gathering replicates.}
#' \item{NRMSE}{The criterion, in percent, is based on the classical root-mean-square error (RMSE),
#' used to aggregate the magnitudes of the errors in predictions for various time-points
#' into a single measure of predictive power. In order to provide a criterion expressed
#' as a percentage, NRMSE is the normalised RMSE by the mean of the observations.}
#' \item{NRMSE_global}{NRMSE for the whole data set by gathering replicates.}
#' \item{SPPE}{The SPPE indicator, in percent, is negative (between \eqn{0} and \eqn{-100\%}) for an
#' underestimation of effects, and positive (between \eqn{0} and \eqn{100}) for an
#' overestimation of effects. An SPPE value of \eqn{0} means an exact prediction
#' of the observed survival probability at the end of the exposure profile.}
#'
#' @references
#' EFSA PPR Scientific Opinion (2018)
#' \emph{Scientific Opinion on the state of the art of Toxicokinetic/Toxicodynamic (TKTD) effect models for regulatory risk assessment of pesticides for aquatic organisms}
#' \url{https://www.efsa.europa.eu/en/efsajournal/pub/5377}
#'
#' @export
predict_Nsurv_check.survFitPredict_Nsurv <- function(object, ...){
df_global <- object$df_quantile %>%
dplyr::mutate(#ppc_matching_check = ifelse(Nsurv_qinf95_check > Nsurv | Nsurv_qsup95_check < Nsurv, 0, 1),
ppc_matching_valid = ifelse(Nsurv_qinf95_valid > Nsurv | Nsurv_qsup95_valid < Nsurv, 0, 1),
SE_id = (Nsurv - Nsurv_q50_valid)^2)
#percent_ppc_graphic <- sum(df_global$ppc_matching_check) / nrow(df_global) * 100
df_ppc <- df_global %>%
dplyr::select(Nsurv, time, Nsurv_q50_valid, Nsurv_q50_check, replicate, ppc_matching_valid) %>%
dplyr::group_by(replicate) %>%
dplyr::summarise(PPC = mean(ppc_matching_valid)*100)
percent_ppc_timeserie <- sum(df_global$ppc_matching_valid) / nrow(df_global) * 100
# --- NRMSE
df_nrmse <- df_global %>%
dplyr::select(Nsurv, time, Nsurv_q50_valid, Nsurv_q50_check, replicate, SE_id) %>%
dplyr::group_by(replicate) %>%
dplyr::summarise(NRMSE = sqrt(mean(SE_id)) / mean(Nsurv) * 100)
nrmse <- sqrt(mean(df_global$SE_id)) / mean(df_global$Nsurv) * 100
## version with distribution
# rmse <- (jags.data$Nsurv - t(df_sim))^2
# y_mean <- mean(jags.data$Nsurv)
# nrmse <- rmse / y_mean
# --- SPPE
df_sppe <- df_global %>%
dplyr::select(Nsurv, time, Nsurv_q50_valid, Nsurv_q50_check, replicate) %>%
dplyr::group_by(replicate) %>%
dplyr::arrange(replicate,time) %>%
dplyr::summarise(SPPE = (dplyr::last(Nsurv) - dplyr::last(Nsurv_q50_valid)) / dplyr::first(Nsurv) * 100 )
# summarise(sppe_check = (dplyr::last(Nsurv) - dplyr::last(Nsurv_q50_check)) / dplyr::first(Nsurv) * 100,
# sppe_valid = (dplyr::last(Nsurv) - dplyr::last(Nsurv_q50_valid)) / dplyr::first(Nsurv) * 100 )
return( list(Percent_PPC = as.data.frame(df_ppc),
Percent_PPC_global = percent_ppc_timeserie,
Percent_NRMSE = as.data.frame(df_nrmse),
Percent_NRMSE_global = nrmse,
Percent_SPPE = as.data.frame(df_sppe))
)
}
#' Predict method for \code{survFit} objects
#'
#' Function from the \code{morse v 3.3.1} package.
#' This is a \code{method} to replace function \code{predict} used on \code{survFit}
#' object when computing issues happen. \code{predict_ode} uses the \code{deSolve}
#' library to improve robustness. However, time to compute may be longer.
#'
#' @rdname predict
#'
#' @param object an object used to select a method \code{ppc}
#' @param \dots Further arguments to be passed to generic methods
#' @export
predict_ode <- function(object, ...){
UseMethod("predict_ode")
}
#' Predict method for \code{survFit} objects
#'
#' Function from the \code{morse v 3.3.1} package.
#' This is the generic \code{predict} S3 method for the \code{survFit} class.
#' It provides predicted survival rate for "SD" or "IT" models under constant or time-variable exposure.
#'
#' @rdname predict
#'
#' @param object An object of class \code{survFit}.
#' @param data_predict A dataframe with three columns \code{time}, \code{conc} and \code{replicate}
#' used for prediction. If \code{NULL}, prediction is based on \code{x} object of
#' class \code{survFit} used for fitting.
#' @param spaghetti If \code{TRUE}, return a set of survival curves using
#' parameters drawn from the posterior distribution.
#' @param mcmc_size Can be used to reduce the number of mcmc samples in order to speed up
#' the computation. \code{mcmc_size} is the number of selected iterations for one chain. Default
#' is 1000. If all MCMC is wanted, set argument to \code{NULL}.
#' @param hb_value If \code{TRUE}, the background mortality \code{hb} is taken into account from the posterior.
#' If \code{FALSE}, parameter \code{hb} is set to a fixed value. The default is \code{FALSE}.
#' @param interpolate_length Length of the time sequence for which output is wanted.
#' @param interpolate_method The interpolation method for concentration. See package \code{deSolve} for details.
#' Default is \code{linear}.
#' @param hb_valueFORCED If \code{hb_value} is \code{FALSE}, it fix \code{hb}. Default is \code{0}.
#' @param \dots Further arguments to be passed to generic methods
#'
#' @return The function returns an object of class \code{survFitPredict} or
#' \code{survFitPredict_Nsurv} with two items:
#' \item{df_quantile}{Predicted quantiles (q50, qinf95, and qsup95)}
#' \item{df_spaghetti}{Predicted survival curve (if spaghetti = \code{TRUE})}
#'
#' @examples
#' library("odeGUTS")
#' data(fit_odeGUTS)
#'
#' data_4prediction <- data.frame(time = 1:10,
#' conc = c(0,5,30,30,0,0,5,30,15,0),
#' replicate= rep("predict", 10))
#'
#' predict_out <- predict_ode(object = fit_odeGUTS, data_predict = data_4prediction,
#' mcmc_size = 200, spaghetti = FALSE)
#'
#'
#'
#' @import deSolve
#' @importFrom stats approxfun
#'
#' @export
predict_ode.survFit <- function(object,
data_predict = NULL,
spaghetti = FALSE,
mcmc_size = 1000,
hb_value = FALSE,
interpolate_length = 100,
interpolate_method = "linear",
hb_valueFORCED = 0,
...) {
x <- object # Renaming to satisfy CRAN checks on S3 methods
# arguments should be named the same when declaring a
# method and its instantiations
# Initialisation
mcmc <- x$mcmc
model_type <- x$model_type
if(is.null(data_predict)){
x_interpolate = data.frame(
time = x$jags.data$time,
conc = x$jags.data$conc,
replicate = x$jags.data$replicate)
}
if(!is.null(data_predict)){
x_interpolate <- data_predict
}
df <- data.frame(
time = x_interpolate$time,
conc = x_interpolate$conc,
replicate = x_interpolate$replicate)
unique_replicate <- unique(df$replicate)
ls_time <- list()
ls_conc <- list()
for(i in 1:length(unique_replicate)){
ls_time[[i]] <- dplyr::filter(df, replicate == unique_replicate[i])$time
ls_conc[[i]] <- dplyr::filter(df, replicate == unique_replicate[i])$conc
}
# ------- Computing
mcmc.samples = mcmc
if(!is.null(mcmc_size)){
reduc_tab = lapply(mcmc.samples, "[",
seq(1, nrow(mcmc.samples[[1]]), length = mcmc_size),
1:ncol(mcmc.samples[[1]]))
mcmc.samples = reduc_tab
}
mctot = do.call("rbind", mcmc.samples)
#if(is.null(mcmc_size)){
mcmc_size = nrow(mctot)
#}
kd = 10^mctot[, "kd_log10"]
if(hb_value == TRUE){
hb <- 10^mctot[, "hb_log10"]
} else if(hb_value == FALSE){
if(is.na(hb_valueFORCED)){
if(is.na(x$hb_valueFIXED)){
stop("Please provide value for `hb` using `hb_valueFORCED`.")
} else{
hb <- rep(x$hb_valueFIXED, nrow(mctot))
}
} else{
hb <- rep(hb_valueFORCED, nrow(mctot))
}
}
k = 1:length(unique_replicate)
if(model_type == "SD"){
kk <- 10^mctot[, "kk_log10"]
z <- 10^mctot[, "z_log10"]
dtheo = lapply(k, function(kit) { # For each replicate
SurvSD_ode(Cw = ls_conc[[kit]],
time = ls_time[[kit]],
replicate = unique_replicate[kit],
kk=kk,
kd=kd,
hb=hb,
z=z,
mcmc_size = mcmc_size,
interpolate_length = interpolate_length,
interpolate_method = interpolate_method)
})
}
if(model_type == "IT"){
alpha <- 10^mctot[, "alpha_log10"]
beta <- 10^mctot[, "beta_log10"]
dtheo = lapply(k, function(kit) { # For each replicate
SurvIT_ode(Cw = ls_conc[[kit]],
time = ls_time[[kit]],
replicate = unique_replicate[kit],
kd = kd,
hb = hb,
alpha = alpha,
beta = beta,
mcmc_size = mcmc_size,
interpolate_length = interpolate_length,
interpolate_method = interpolate_method)
})
}
# Transpose
df_theo <- do.call("rbind", dtheo)
df_quantile = dplyr::select(df_theo, time, conc, replicate, q50, qinf95, qsup95)
if(spaghetti == TRUE){
df_spaghetti <- df_theo
} else df_spaghetti <- NULL
return_object <- list(df_quantile = df_quantile,
df_spaghetti = df_spaghetti)
class(return_object) <- c(class(return_object), "survFitPredict")
return(return_object)
}
# Survival function for "IT" model with external concentration changing with time
#
# Function from the \code{morse v 3.3.1} package.
# @param Cw A scalar of external concentration
# @param time A vector of time
# @param kk a vector of parameter
# @param kd a vector of parameter
# @param z a vector of parameter
# @param hb a vector of parameter
#
#
# @return A matrix generate with coda.samples() function
#
SurvSD_ode <- function(Cw, time, replicate, kk, kd, z, hb, mcmc_size = 1000, interpolate_length = NULL, interpolate_method=c("linear","constant")) {
interpolate_method <- match.arg(interpolate_method)
## external signal with several rectangle impulses
signal <- data.frame(times=time,import=Cw)
if(!is.null(interpolate_length)){
times <- seq(min(time), max(time), length = interpolate_length)
} else{
times <- signal$times
}
xstart <- c(rep(c(D=0),mcmc_size),rep(c(H=0),mcmc_size))
# ordering of parameters required by compiled function
parms <- c(mcmc_size,kd,hb,z,kk)
# solve model
on.exit(.C("gutsredsd_free")) # clean up
deSolve::ode(y=xstart,
times=times,
parms=parms,
method="lsoda",
dllname="odeGUTS",
initfunc="gutsredsd_init",
func="gutsredsd_func",
initforc="gutsredsd_forc",
forcings=signal,
fcontrol=list(method=interpolate_method,rule=2,ties="ordered"),
nout=1
) -> out
dtheo <- exp(-out[,grep("H",colnames(out))])
# Manage vector case
if(mcmc_size == 1){
q50 = dtheo
qinf95 = dtheo
qsup95 = dtheo
} else{
qs <- apply(as.matrix(dtheo), 1, quantile, probs=c(0.5,0.025,0.975), names=FALSE, na.rm=TRUE)
q50 = qs[1,]
qinf95 = qs[2,]
qsup95 = qs[3,]
}
dtheo <- as.data.frame(dtheo)
names(dtheo) <- paste0("H",seq(1,mcmc_size))
dtheo <- dtheo %>%
dplyr::mutate(time = times,
conc = out[,ncol(out)],
replicate = c(replicate),
q50 = q50,
qinf95 = qinf95,
qsup95 = qsup95)
return(dtheo)
}
# Survival function for "IT" model with external concentration changing with time
#
# Function from the \code{morse v 3.3.1} package.
# @param Cw A vector of external concentration
# @param time A vector of time
# @param replicate A scalar of char
# @param kk a vector of parameter
# @param kd a vector of parameter
# @param z a vector of parameter
# @param hb a vector of parameter
#
#
# @return A matrix generate with coda.samples() function
#
SurvIT_ode <- function(Cw, time, replicate, kd, hb, alpha, beta, mcmc_size = NULL, interpolate_length = NULL, interpolate_method=c("linear","constant")){
interpolate_method <- match.arg(interpolate_method)
## external signal with several rectangle impulses
signal <- data.frame(times=time,import=Cw)
if(!is.null(interpolate_length)){
times <- seq(min(time), max(time), length = interpolate_length)
} else{
times <- signal$times
}
## The parameters
parms <- c(mcmc_size,kd,hb)
## Start values for steady state
xstart <- c(rep(c(D=0),mcmc_size),rep(c(H=0),mcmc_size))
## Solve model
on.exit(.C("gutsredit_free")) # clean up
deSolve::ode(y=xstart,
times=times,
parms=parms,
method="lsoda",
dllname="odeGUTS",
initfunc="gutsredit_init",
func="gutsredit_func",
initforc="gutsredit_forc",
forcings=signal,
fcontrol=list(method=interpolate_method,rule=2,ties="ordered"),
nout=1
) -> out
D <- out[,grep("D",colnames(out))]
cumMax_D <- if(is.null(dim(D))) cummax(D) else apply(D, 2, cummax)
thresholdIT <- t(1 / (1 + (t(cumMax_D) / alpha)^(-beta)))
dtheo <- (1 - thresholdIT) * exp(times %*% t(-hb))
# Manage vector case
if(mcmc_size == 1){
q50 = dtheo
qinf95 = dtheo
qsup95 = dtheo
} else{
qs <- apply(as.matrix(dtheo), 1, quantile, probs=c(0.5,0.025,0.975), names=FALSE, na.rm=TRUE)
q50 = qs[1,]
qinf95 = qs[2,]
qsup95 = qs[3,]
}
dtheo <- as.data.frame(dtheo)
names(dtheo) <- paste0("H",seq(1,mcmc_size))
dtheo <- dtheo %>%
dplyr::mutate(time = out[, "time"],
conc = out[,ncol(out)],
replicate = c(replicate),
q50 = q50,
qinf95 = qinf95,
qsup95 = qsup95)
return(dtheo)
}
#' Predict method for \code{survFit} objects
#'
#' Function from the \code{morse v 3.3.1} package.
#' This is a \code{method} to replace function \code{predict_Nsurv} used on \code{survFit}
#' object when computing issues happen. \code{predict_nsurv_ode} uses the \code{deSolve}
#' library to improve robustness. However, time to compute may be longer.
#'
#' @rdname predict
#'
#' @param object An object of class \code{survFit}.
#' @param data_predict A dataframe with three columns \code{time}, \code{conc} and \code{replicate}
#' used for prediction. If \code{NULL}, prediction is based on \code{x} object of
#' class \code{survFit} used for fitting.
#' @param spaghetti If \code{TRUE}, return a set of survival curves using
#' parameters drawn from the posterior distribution.
#' @param mcmc_size Can be used to reduce the number of mcmc samples in order to speed up
#' the computation. \code{mcmc_size} is the number of selected iterations for one chain. Default
#' is 1000. If all MCMC is wanted, set argument to \code{NULL}.
#' @param hb_value If \code{TRUE}, the background mortality \code{hb} is taken into account from the posterior.
#' If \code{FALSE}, parameter \code{hb} is set to 0. The default is \code{FALSE}.
#' @param hb_valueFORCED If \code{hb_value} is \code{FALSE}, it fix \code{hb}. Default is \code{0}
#' @param extend_time Length of time points interpolated with variable exposure profiles.
#' @param interpolate_length Length of the time sequence for which output is wanted.
#' @param interpolate_method The interpolation method for concentration. See package \code{deSolve} for details.
#' Default is \code{linear}.
#' @param \dots Further arguments to be passed to generic methods
#'
#' @export
predict_Nsurv_ode <- function(object,
data_predict,
spaghetti,
mcmc_size,
hb_value,
hb_valueFORCED,
extend_time,
interpolate_length,
interpolate_method,
...){
UseMethod("predict_Nsurv_ode")
}
#' @rdname predict
#' @import deSolve
#' @importFrom stats approxfun
#'
#' @export
predict_Nsurv_ode.survFit <- function(object,
data_predict = NULL,
spaghetti = FALSE,
mcmc_size = 1000,
hb_value = FALSE,
hb_valueFORCED = 0,
extend_time = 100,
interpolate_length = NULL,
interpolate_method = "linear",
...) {
x <- object # Renaming to satisfy CRAN checks on S3 methods
# arguments should be named the same when declaring a
# method and its instantiations
if(!("Nsurv" %in% colnames(data_predict))){
warning("Please provide a column 'Nsurv' in the 'data_predict' argument to have
prediction on the Number of survivor.")
}
message("Note that computing can be quite long (several minutes).
Tips: To reduce that time you can reduce Number of MCMC chains (default mcmc_size is set to 1000).")
# Initialisation
mcmc <- x$mcmc
model_type <- x$model_type
extend_time <- extend_time
if(is.null(data_predict)){
if("survFitVarExp" %in% class(x)){
x_interpolate = data.frame(
time = x$jags.data$time_long,
conc = x$jags.data$conc_long,
replicate = x$jags.data$replicate_long)
} else{
data_predict = data.frame(
time = x$jags.data$time,
conc = x$jags.data$conc,
replicate = x$jags.data$replicate,
Nsurv = x$jags.data$Nsurv)
x_interpolate <- predict_interpolate(data_predict, extend_time = extend_time) %>%
dplyr::arrange(replicate, time)
}
}
if(!is.null(data_predict)){
x_interpolate <- predict_interpolate(data_predict, extend_time = extend_time) %>%
dplyr::arrange(replicate, time)
}
df <- data.frame(
time = x_interpolate$time,
conc = x_interpolate$conc,
replicate = x_interpolate$replicate)
unique_replicate <- unique(df$replicate)
ls_time <- list()
ls_conc <- list()
for(i in 1:length(unique_replicate)){
ls_time[[i]] <- dplyr::filter(df, replicate == unique_replicate[i])$time
ls_conc[[i]] <- dplyr::filter(df, replicate == unique_replicate[i])$conc
}
# ------- Computing
mcmc.samples = mcmc
if(!is.null(mcmc_size)){
reduc_tab = lapply(mcmc.samples, "[",
seq(1, nrow(mcmc.samples[[1]]), length = mcmc_size),
1:ncol(mcmc.samples[[1]]))
mcmc.samples = reduc_tab
}
mctot = do.call("rbind", mcmc.samples)
mcmc_size = nrow(mctot)
kd = 10^mctot[, "kd_log10"]
if(hb_value == TRUE){
# "hb" is not in survFit object of morse <v3.2.0
if("hb" %in% colnames(mctot)){
hb <- mctot[, "hb"]
} else{ hb <- 10^mctot[, "hb_log10"] }
} else if(hb_value == FALSE){
if(is.na(hb_valueFORCED)){
if(is.na(x$hb_valueFIXED)){
stop("Please provide value for `hb` using `hb_valueFORCED`.")
} else{
hb <- rep(x$hb_valueFIXED, nrow(mctot))
}
} else{
hb <- rep(hb_valueFORCED, nrow(mctot))
}
}
k = 1:length(unique_replicate)
if(model_type == "SD"){
kk <- 10^mctot[, "kk_log10"]
z <- 10^mctot[, "z_log10"]
dtheo = lapply(k, function(kit) { # For each replicate
SurvSD_ode(Cw = ls_conc[[kit]],
time = ls_time[[kit]],
replicate = unique_replicate[kit],
kk=kk,
kd=kd,
hb=hb,
z=z,
mcmc_size = mcmc_size,
interpolate_length = interpolate_length,
interpolate_method = interpolate_method)
})
}
if(model_type == "IT"){
alpha <- 10^mctot[, "alpha_log10"]
beta <- 10^mctot[, "beta_log10"]
dtheo = lapply(k, function(kit) { # For each replicate
SurvIT_ode(Cw = ls_conc[[kit]],
time = ls_time[[kit]],
replicate = unique_replicate[kit],
kd = kd,
hb = hb,
alpha = alpha,
beta = beta,
mcmc_size = mcmc_size,
interpolate_length = interpolate_length,
interpolate_method = interpolate_method)
})
}
# Transpose
df_theo <- do.call("rbind", dtheo)
# dtheo <- do.call("rbind", lapply(dtheo, t))
# Computing Nsurv
df_mcmc <- tidyr::as_tibble(do.call("rbind", x$mcmc))
NsurvPred_valid <- dplyr::select(df_mcmc, dplyr::contains("Nsurv_sim"))
NsurvPred_check <- dplyr::select(df_mcmc, dplyr::contains("Nsurv_ppc"))
if(is.null(data_predict) &
# The following condition are always true for survFit done after morse v3.2.0 !
ncol(NsurvPred_valid) > 0 &
ncol(NsurvPred_check) > 0){
df_quantile <- data.frame(
time = data_predict$time,
conc = data_predict$conc,
replicate = data_predict$replicate,
Nsurv = data_predict$Nsurv,
Nsurv_q50_check = apply(NsurvPred_check, 1, quantile, probs = 0.5, na.rm = TRUE),
Nsurv_qinf95_check = apply(NsurvPred_check, 1, quantile, probs = 0.025, na.rm = TRUE),
Nsurv_qsup95_check = apply(NsurvPred_check, 1, quantile, probs = 0.975, na.rm = TRUE),
Nsurv_q50_valid = apply(NsurvPred_valid, 1, quantile, probs = 0.5, na.rm = TRUE),
Nsurv_qinf95_valid = apply(NsurvPred_valid, 1, quantile, probs = 0.025, na.rm = TRUE),
Nsurv_qsup95_valid = apply(NsurvPred_valid, 1, quantile, probs = 0.975, na.rm = TRUE))
} else{
# --------------------
df_psurv <- tidyr::as_tibble(df_theo) %>%
dplyr::select(-conc) %>%
dplyr::mutate(time = df$time,
replicate = df$replicate)
df_filter <- dplyr::inner_join(df_psurv, data_predict, by = c("replicate", "time")) %>%
dplyr::filter(!is.na(Nsurv)) %>%
dplyr::group_by(replicate) %>%
dplyr::arrange(replicate, time) %>%
dplyr::mutate(Nprec = ifelse(time == min(time), Nsurv, dplyr::lag(Nsurv)),
iter = dplyr::row_number(),
iter_prec = ifelse(time == min(time), iter, dplyr::lag(iter))) %>%
dplyr::ungroup()
mat_psurv <- df_filter %>%
dplyr::select(- c("time", "conc", "replicate",
"q50", "qinf95", "qsup95",
"Nsurv", "Nprec", "iter", "iter_prec")) %>%
as.matrix()
ncol_NsurvPred <- ncol(mat_psurv)
nrow_NsurvPred <- nrow(mat_psurv)
iter = df_filter$iter
iter_prec = df_filter$iter_prec
NsurvPred_valid <- matrix(ncol = ncol_NsurvPred, nrow = nrow(mat_psurv))
Nprec <- cbind(df_filter$Nprec)[, rep(1,ncol_NsurvPred)]
mat_psurv_prec = matrix(ncol = ncol_NsurvPred, nrow = nrow_NsurvPred)
for(i in 1:nrow_NsurvPred){
if(iter[i] == iter_prec[i]){
mat_psurv_prec[i,] = mat_psurv[i,]
} else{
mat_psurv_prec[i,] = mat_psurv[i-1,]
}
}
mat_pSurv_ratio = mat_psurv / mat_psurv_prec
NsurvPred_check_vector = rbinom(ncol_NsurvPred*nrow_NsurvPred,
size = Nprec,
prob = mat_pSurv_ratio)
NsurvPred_check = matrix(NsurvPred_check_vector, byrow = FALSE, nrow = nrow_NsurvPred)
NsurvPred_valid[1, ] = rep(Nprec[1], ncol_NsurvPred)
for(i in 2:nrow(mat_psurv)){
if(iter[i] == iter_prec[i]){
NsurvPred_valid[i,] = NsurvPred_check[i,]
} else{
NsurvPred_valid[i,] = rbinom(ncol_NsurvPred,
size = NsurvPred_valid[i-1,],
prob = mat_pSurv_ratio[i,])
}
}
df_quantile <- data.frame(time = df_filter$time,
conc = df_filter$conc,
replicate = df_filter$replicate,
Nsurv = df_filter$Nsurv,
Nsurv_q50_check = apply(NsurvPred_check, 1, quantile, probs = 0.5, na.rm = TRUE),
Nsurv_qinf95_check = apply(NsurvPred_check, 1, quantile, probs = 0.025, na.rm = TRUE),
Nsurv_qsup95_check = apply(NsurvPred_check, 1, quantile, probs = 0.975, na.rm = TRUE),
Nsurv_q50_valid = apply(NsurvPred_valid, 1, quantile, probs = 0.5, na.rm = TRUE),
Nsurv_qinf95_valid = apply(NsurvPred_valid, 1, quantile, probs = 0.025, na.rm = TRUE),
Nsurv_qsup95_valid = apply(NsurvPred_valid, 1, quantile, probs = 0.975, na.rm = TRUE))
}
if(spaghetti == TRUE){
random_column <- sample(1:ncol(NsurvPred_valid), size = round(10/100 * ncol(NsurvPred_valid)))
df_spaghetti <- tidyr::as_tibble(NsurvPred_valid[, random_column]) %>%
dplyr::mutate(time = data_predict$time,
conc = data_predict$conc,
replicate = data_predict$replicate,
Nsurv = data_predict$Nsurv)
} else df_spaghetti <- NULL
#ls_check_on_Nsurv <- check_on_Nsurv(df_quantile)
return_object <- list(df_quantile = df_quantile,
df_spaghetti = df_spaghetti)
class(return_object) <- c(class(return_object), "survFitPredict_Nsurv")
return(return_object)
}
# Create a dataset for survival analysis when the replicate of concentration is variable
#
# Function from the \code{morse v 3.3.1} package.
# @param x An object of class \code{survData}
# @param extend_time length of time points interpolated with variable exposure profiles
#
# @return A dataframe
#
predict_interpolate <- function(x, extend_time = 100){
## data.frame with time
df_MinMax <- x %>%
dplyr::group_by(replicate) %>%
dplyr::summarise(min_time = min(time, na.rm = TRUE),
max_time = max(time, na.rm = TRUE)) %>%
dplyr::group_by(replicate) %>%
dplyr::do(dplyr::tibble(replicate = .$replicate, time = seq(.$min_time, .$max_time, length = extend_time)))
x_interpolate <- dplyr::full_join(df_MinMax, x,
by = c("replicate", "time")) %>%
dplyr::group_by(replicate) %>%
dplyr::arrange(replicate, time) %>% # organize in replicate and time
dplyr::mutate(conc = zoo::na.approx(conc, time, na.rm = FALSE)) %>%
# from package zoo : 'na.locf()' carry the last observation forward to replace your NA values.
dplyr::mutate(conc = ifelse(is.na(conc),zoo::na.locf(conc),conc) )
return(x_interpolate)
}
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