predict_ode.survFit {morse} | R Documentation |
Predict method for survFit
objects
Description
This is the generic predict
S3 method for the survFit
class.
It provides predicted survival rate for "SD" or "IT" models under constant or time-variable exposure.
Usage
## S3 method for class 'survFit'
predict_ode(
object,
data_predict = NULL,
spaghetti = FALSE,
mcmc_size = 1000,
hb_value = TRUE,
interpolate_length = 100,
interpolate_method = "linear",
hb_valueFORCED = NA,
...
)
Arguments
object |
An object of class |
data_predict |
A dataframe with three columns |
spaghetti |
If |
mcmc_size |
Can be used to reduce the number of mcmc samples in order to speed up
the computation. |
hb_value |
If |
interpolate_length |
Length of the time sequence for which output is wanted. |
interpolate_method |
The interpolation method for concentration. See package |
hb_valueFORCED |
If |
... |
Further arguments to be passed to generic methods |
Value
a list
of data.frame
with the quantiles of outputs in
df_quantiles
or all the MCMC chaines df_spaghetti
Examples
# (1) Load the survival data
data("propiconazole_pulse_exposure")
# (2) Create an object of class "survData"
dataset <- survData(propiconazole_pulse_exposure)
# (3) Run the survFit function
out <- survFit(dataset , model_type = "SD")
# (4) Create a new data table for prediction
data_4prediction <- data.frame(time = 1:10,
conc = c(0,5,30,30,0,0,5,30,15,0),
replicate= rep("predict", 10))
# (5) Predict on a new data set
predict_out <- predict_ode(object = out, data_predict = data_4prediction,
mcmc_size = 1000, spaghetti = TRUE)