Generic method for MFx, a function denoted \(MF(x,t)\) for
\(x\)% Multiplication Factor at time \(t\).
The function MFx, \(x\)% Multiplication Factor at time \(t\), (\(MF(x,t)\)),
is used to compute the multiplication factor
applied to the concentration exposure profile in order to
reduce by \(x\)% (argument X) the survival probability at a
specified test duration \(t\) (argument time_MFx) (default is the maximum
time point of the experiment).
Mathematical definition of \(x\)% Multiplication Factor at time \(t\) (at the end of a time series \(T = \{0, \dots, t\}\)), denoted \(MF(x,t)\), is given by:
\(S(MF(x,t) * C_w(\tau \in T), t) = S( C_w(\tau \in T), t)*(1- x/100)\),
where \(C_w(\tau \in T)\) is the initial exposure profile without multiplication factor. And so the expression \(S(MF(x,t)* C_w(\tau \in T), t)\) is the survival probability after an exposure profile \(MF(x,t)* C_w(\tau \in T)\) at time \(t\).
This is a method to replace function MFx used on survFit
object when computing issues happen. MFx_ode uses the deSolve
library to improve robustness. However, time to compute may be longer.
The function MFx_ode, \(x\)% Multiplication Factor at time \(t\), (\(MF(x,t)\)),
is used to compute the multiplication factor
applied to the concentration exposure profile in order to
reduce by \(x\)% (argument X) the survival probability at a
specified test duration \(t\) (argument time_MFx) (default is the maximum
time point of the experiment).
Mathematical definition of \(x\)% Multiplication Factor at time \(t\) (at the end of a time series \(T = \{0, \dots, t\}\)), denoted \(MF(x,t)\), is given by:
\(S(MF(x,t) * C_w(\tau \in T), t) = S( C_w(\tau \in T), t)*(1- x/100)\),
where \(C_w(\tau \in T)\) is the initial exposure profile without multiplication factor. And so the expression \(S(MF(x,t)* C_w(\tau \in T), t)\) is the survival probability after an exposure profile \(MF(x,t)* C_w(\tau \in T)\) at time \(t\).
MFx(object, ...)# S3 method for survFit
MFx(
object,
data_predict,
X = 50,
time_MFx = NULL,
MFx_range = c(0, 1000),
mcmc_size = 1000,
hb_value = TRUE,
spaghetti = FALSE,
accuracy = 0.01,
quiet = FALSE,
threshold_iter = 100,
hb_valueFORCED = 0,
ode = TRUE,
interpolate_length = NULL,
interpolate_method = "linear",
...
)
MFx_ode(object, ...)
# S3 method for survFit
MFx_ode(
object,
data_predict,
X = 50,
time_MFx = NULL,
MFx_range = c(0, 1000),
mcmc_size = 1000,
hb_value = TRUE,
spaghetti = FALSE,
accuracy = 0.01,
quiet = FALSE,
threshold_iter = 100,
hb_valueFORCED = 0,
interpolate_length = NULL,
interpolate_method = "linear",
...
)
returns an object of class MFx
The function returns an object of class MFx, which is a list
with the following information:
Survival probability for X percent of reduction of the initial median
survival probability at time time_MFx.
A number giving the proportion of reduction in survival.
A number giving the time at which \(MF(x,t)\) has to be estimated as provided in arguments or if NULL, the latest time point of the profile is used.
A data.frame with quantiles (median, 2.5% and 97.5%)
of \(MF(x,t)\) at time \(t\), time_MFx, for \(x\)% of survival reduction.
A data.frame with quantiles (median, 2.5% and 97.5%)
of survival probability along the computed multiplication factor and at time time_MFx.
A vector of all multiplication factors computed.
A list of all object of class survFitPredict obtained
from computing survival probability for every profiles build from the vector of
multiplication factors MFx_tested.
The function returns an object of class MFx, which is a list
with the following information:
Survival probability for X percent of reduction of the initial median
survival probability at time time_MFx.
A number giving the proportion of reduction in survival.
A number giving the time at which \(MF(x,t)\) has to be estimated as provided in arguments or if NULL, the latest time point of the profile is used.
A data.frame with quantiles (median, 2.5% and 97.5%)
of \(MF(x,t)\) at time \(t\), time_MFx, for \(x\)% of survival reduction.
A data.frame with quantiles (median, 2.5% and 97.5%)
of survival probability along the computed multiplication factor and at time time_MFx.
A vector of all multiplication factors computed.
A list of all object of class survFitPredict obtained
from computing survival probability for every profiles build from the vector of
multiplication factors MFx_tested.
An object of class survFit.
Further arguments to be passed to generic methods
A dataframe with two columns time and conc.
Percentage of survival change (e.g., \(50\) for survival decrease of 50%
, or \(-50\) for survival increase of 50%).The default is 50.
Only time series computed during the adaptation using a binary search in
\(O(log(n))\) are returned. However, if NULL, all time series
computed from the vector MFx_range are returned.
A number giving the time at which \(MF(x,t)\) has to be estimated. If NULL, the latest time point of the profile is used.
A vector from which lower and upper bound of the range of the
multiplication factor MFx are generated. The default is a vector c(0, 1000).
If argument X is NULL, then all the time series generated with
MFx_range are returned.
Can be used to reduce the number of MCMC samples in order to speed up the computation. The default is 1000.
If TRUE, the background mortality hb is taken into
account from the posterior.
If FALSE, parameter hb is set to 0. The default is TRUE.
If TRUE, return a set of survival curves using
parameters drawn from the posterior distribution.
Accuracy of the multiplication factor. The default is 0.01.
If FALSE, print the evolution of accuracy.
Threshold number of iteration.
If hb_value is FALSE, it fix hb.
IF ode is TRUE, algo use predict_ode rather than predict. Default is TRUE.
Length of the time sequence for which output is wanted.
The interpolation method for concentration. See package deSolve for details.
Default is linear.
When class of object is survFit, see MFx.survFit.