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How to generate a Minimal Working Example

Please report any issue

The only think that can help is to create a Minimal Working Example (MWE). And we provide here a set of MWE you can modify to obtain yours.

Feel free to ask question here: New Issue for morse R package

Below is reported a set of minimal examples where each chunk (piece of R code in grey area) work on its own.

Loading morse and its dependencies (JAGS, C++)

Use the two classical R commands:

install.packages(morse) # install the package
library(morse) # load the package

Recently, a lot of warning messages have appeared when downloading the morse package due to an update in its dependencies. We fix these warnings and we’ll try to do so as soon as possible when new ones will appear.

Again, feel free to report such messages if they disturb your data processing: New Issue for morse R package

The morse package is linked to JAGS and C++.

JAGS

JAGS is the Bayesian sampler used to make inference with survival models.

  1. Download JAGS: https://sourceforge.net/projects/mcmc-jags/

  2. Test and find JAGS

Once installed, JAGS can be lost in the PATH. To help solving this issue, you can use package runjags which is not within morse so you have to install it.

install.packages("runjags")
library("runjags")
testjags()

We assume hereafter that the morse package is already installed.

The output should look like this:

You are using R version 4.0.2 (2020-06-22) on a windows machine, with the RStudio GUI
JAGS version 4.3.0 found successfully using the command 'C:/Program Files/JAGS/JAGS-4.3.0/x64/bin/jags-terminal.exe'
The rjags package is installed

Otherwise, you can tell your system where JAGS executable is (somewhere in 'C:/Program Files/JAGS/JAGS-4.3.0/x64/bin/jags-terminal.exe' on windows machines):

testjags(jags=runjags.getOption('jagspath')) # replace jagspath by the PATH to JAGS
# FOR INSTANCE, on the current machine I do:
testjags(jags=runjags.getOption('C:/Program Files/JAGS/JAGS-4.3.0/x64/bin/jags-terminal.exe'))

C++

C++ is used for running simulations. In R, you should not have issue with C++ requirement since it is very well integrated (many R functions are simple interfaces to C++ functions).

For now, we do not have any trouble reported on this use. But feel free to report if there is one: New Issue for morse R package

Survival analysis

Fit

library(morse)
data("propiconazole")
survData_PRZ = survData(propiconazole)
fit_cstSD = survFit(survData_PRZ, model_type = "SD")

… with plot

library(morse)
data("propiconazole")
survData_PRZ = survData(propiconazole)
fit_cstSD = survFit(survData_PRZ, model_type = "SD")
plot(fit_cstSD)

LCx

library(morse)
data("propiconazole")
survData_PRZ = survData(propiconazole)
fit_cstSD = survFit(survData_PRZ, model_type = "SD")
LCx(fit_cstSD, X = 50)

… with plot

library(morse)
data("propiconazole")
survData_PRZ = survData(propiconazole)
fit_cstSD = survFit(survData_PRZ, model_type = "SD")
LCX_cstSD = LCx(fit_cstSD, X = 50)
plot(LCX_cstSD)

MFX

library(morse)
data("propiconazole")
survData_PRZ = survData(propiconazole)
fit_cstSD = survFit(survData_PRZ, model_type = "SD")
data_4MFx <- data.frame(time = 1:10,
                        conc = c(0,0.5,8,3,0,0,0.5,8,3.5,0))
MFx_PRZ_cstSD <- MFx(object = fit_cstSD, data_predict = data_4MFx, ode = TRUE)

… with plot

library(morse)
data("propiconazole")
survData_PRZ = survData(propiconazole)
fit_cstSD = survFit(survData_PRZ, model_type = "SD")
data_4MFx <- data.frame(time = 1:10,
                        conc = c(0,0.5,8,3,0,0,0.5,8,3.5,0))
MFx_PRZ_cstSD <- MFx(object = fit_cstSD, data_predict = data_4MFx, ode = TRUE)
plot(MFx_PRZ_cstSD)

Predict survival probability

library(morse)
data("propiconazole")
survData_PRZ = survData(propiconazole)
fit_cstSD = survFit(survData_PRZ, model_type = "SD")
data_example <- data.frame(
   time = c(1,1.9,2,15,15.1,20),
   conc = c(0,0,20,20,0,0),
   replicate = rep("example", 6)
)
predict_example_NULL = predict_ode(
  object = fit_cstSD,
  data_predict = data_example,
  mcmc_size = 10,
  interpolate_length = NULL)

… with plot

with few exposure profile time points:

plot(predict_example_NULL)

test option interpolate_length

predict_example_100 = predict_ode(
  object = fit_cstSD,
  data_predict = data_example,
  mcmc_size = 10,
  interpolate_length = 100)
plot(predict_example_100)

and with a very detailed exposure profile

data("FOCUSprofile")
predict_FOCUS = predict_ode(
  object = fit_cstSD,
  data_predict = FOCUSprofile,
  mcmc_size = 10,
  interpolate_length = NULL)
plot(predict_FOCUS)

Predict number of survival

library(morse)
data("propiconazole")
survData_PRZ = survData(propiconazole)
fit_cstSD = survFit(survData_PRZ, model_type = "SD")
data("propiconazole_pulse_exposure")
predict_Nsurv = predict_Nsurv_ode(
  object = fit_cstSD,
  data_predict = propiconazole_pulse_exposure
)

… with plot

library(morse)
data("propiconazole")
survData_PRZ = survData(propiconazole)
fit_cstSD = survFit(survData_PRZ, model_type = "SD")
data("propiconazole_pulse_exposure")
predict_Nsurv = predict_Nsurv_ode(
  object = fit_cstSD,
  data_predict = propiconazole_pulse_exposure
)
plot(predict_Nsurv)

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.