survFit objectsThis is the generic predict S3 method for the survFit class.
It provides simulation for "SD" or "IT" models under constant or time-variable exposure.
It provides the simulated number of survivors for "SD" or "IT" models under constant or time-variable exposure.
It provides the simulated number of survivors for "SD" or "IT" models under constant or time-variable exposure.
This is a method to replace function predict_Nsurv used on survFit
object when computing issues happen. predict_nsurv_ode uses the deSolve
library to improve robustness. However, time to compute may be longer.
# S3 method for survFit
predict(
object,
data_predict = NULL,
spaghetti = FALSE,
mcmc_size = NULL,
hb_value = TRUE,
ratio_no.NA = 0.95,
hb_valueFORCED = NA,
extend_time = 100,
...
)predict_Nsurv(object, ...)
# S3 method for survFit
predict_Nsurv(
object,
data_predict = NULL,
spaghetti = FALSE,
mcmc_size = NULL,
hb_value = TRUE,
hb_valueFORCED = NA,
extend_time = 100,
...
)
predict_Nsurv_ode(
object,
data_predict,
spaghetti,
mcmc_size,
hb_value,
hb_valueFORCED,
extend_time,
interpolate_length,
interpolate_method,
...
)
# S3 method for survFit
predict_Nsurv_ode(
object,
data_predict = NULL,
spaghetti = FALSE,
mcmc_size = 1000,
hb_value = TRUE,
hb_valueFORCED = NA,
extend_time = 100,
interpolate_length = NULL,
interpolate_method = "linear",
...
)
a list of data.frame with the quantiles of outputs in
df_quantiles or all the MCMC chaines df_spaghetti
an object of class predict_Nsurv.
The function returns an object of class survFitPredict_Nsurv, which is
a list with the two following data.frame:
A data.frame with 10 columns, time, conc,
replicate, Nsurv (observed number of survivors)
and other columns with median and 95% credible interval
of the number of survivors computed with 2 different way
refers as check and valid:
Nsurv_q50_check, Nsurv_qinf95_check,
Nsurv_qsup95_check, Nsurv_q50_valid, Nsurv_qinf95_valid,
Nsurv_qsup95_valid. The _check refers to the number of survivors
at time \(t\) predicted using the observed number
of survivors at time \(t-1\),
while the _valid refers to the number of survivors predicted at time
\(t\) based on the predicted number of survivors at time \(t-1\).
NULL if arguement spaghetti = FALSE. With spaghetti = TRUE, it returns a
dataframe with all simulations based on MCMC parameters from a survFit object.
an object of class predict_Nsurv_ode.
a list of data.frame with the quantiles of outputs in
df_quantiles or all the MCMC chaines df_spaghetti
An object of class survFit.
A dataframe with three columns time, conc and replicate
used for prediction. If NULL, prediction is based on x object of
class survFit used for fitting.
If TRUE, return a set of survival curves using
parameters drawn from the posterior distribution.
Can be used to reduce the number of mcmc samples in order to speed up
the computation. mcmc_size is the number of selected iterations for one chain. Default
is 1000. If all MCMC is wanted, set argument to NULL.
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.
A numeric between 0 and 1 standing for the proportion of non-NA values required to compute quantile. The default is \(0.95\).
If hb_value is FALSE, it fix hb.
Length of time points interpolated with variable exposure profiles.
Further arguments to be passed to generic methods
Length of the time sequence for which output is wanted.
The interpolation method for concentration. See package deSolve for details.
Default is linear.