The mtkWWDMEvaluator class

Share:

Description

The mtkWWDMEvaluator class is a sub-class of the class mtkEvaluator used to manage the simulation of the model WWDM.

Class Hierarchy

Parent classes :

mtkEvaluator

Direct Known Subclasses :

Constructor

mtkWWDMEvaluator

signature(mtkParameters = NULL, listParameters = NULL)

Slots

name:

(character) always takes the string "evaluate".

protocol:

(character) a string to name the protocol used to run the process: http, system, R, etc. Here, it always takes the character "R".

site:

(character) a string to indicate where the service is located. Here, it always takes the string "mtk".

service:

(character) a string to name the service to invoke. Here, it always takes the string "WWDM".

parameters:

(vector) a vector of [mtkParameter] containing the parameters to pass while calling the service. The WWDM model does not need parameters.

ready:

(logical) a logical to tell if the process is ready to run.

state:

(logical) a logical to tell if the results produced by the process are available and ready to be consumed.

result:

(ANY) a data holder to hold the results produced by the process

Methods

setName

signature(this = "mtkWWDMEvaluator", name = "character"): Not used, method inherited from the parent class.

setParameters

signature(this = "mtkWWDMEvaluator", f = "vector"): Assigns new parameters to the process.

getParameters

signature(this = "mtkWWDMEvaluator"): Returns the parameters as a named list.

is.ready

signature( = "mtkWWDMEvaluator"): Tests if the process is ready to run.

setReady

signature(this = "mtkWWDMEvaluator", switch = "logical"): Makes the process ready to run.

is.ready

signature( = "mtkWWDMEvaluator"): Tests if the results produced by the process are available.

setReady

signature(this = "mtkWWDMEvaluator", switch = "logical"): Marks the process as already executed.

getResult

signature(this = "mtkWWDMEvaluator"): Returns the results produced by the process as a [mtkWWDMEvaluatorResult].

getData

signature(this = "mtkWWDMEvaluator"): Returns the results produced by the process as a data.frame.

serializeOn

signature(this = "mtkWWDMEvaluator"): Returns all data managed by the process as a named list.

run

signature(this = "mtkWWDMEvaluator", context= "mtkExpWorkflow"): runs the simulation.

summary

signature(object = "mtkWWDMEvaluator"): Provides a summary of the results produced by the process.

print

signature(x = "mtkWWDMEvaluator"): Prints a report of the results produced by the process.

plot

signature(x = "mtkWWDMEvaluator"): Plots the results produced by the process.

report

signature(this = "mtkWWDMEvaluator"): Reports the results produced by the process.

Author(s)

Juhui WANG, MIA-Jouy, Inra, Juhui.Wang@jouy.inra.fr

References

  1. J. Wang, H. Richard, R. Faivre, H. Monod (2013). Le package mtk, une bibliothèque R pour l'exploration numérique des modèles. In: Analyse de sensibilité et exploration de modèles : Application aux sciences de la nature et de l'environnement (R. Faivre, B. Iooss, S. Mahévas, D. Makowski, H. Monod, Eds). Editions Quae, Versailles.

  2. R. Faivre, D. Makowski, J. Wang, H. Richard, R. Monod (2013). Exploration numérique d'un modèle agronomique avec le package mtk. In: Analyse de sensibilité et exploration de modèles : Application aux sciences de la nature et de l'environnement (R. Faivre, B. Iooss, S. Mahévas, D. Makowski, H. Monod, Eds). Editions Quae, Versailles.

See Also

help(WWDM)

Examples

 1
 2
 3
 4
 5
 6
 7
 8
 9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
# Carry out a sensitivity analysis with the WWDM model

##	Input the factors
	data(WWDM.factors)

##	Specify the experiments designer
	designer <- mtkMorrisDesigner (
		listParameters = list(type="oat", levels=5, grid.jump=3, r=10)
		)

##	Specify the model simulator
	model <- mtkWWDMEvaluator(
		listParameters = list(year=3)
		)
	
##	Specify the sensiticity analyser
	analyser <- mtkMorrisAnalyser()

##	Specify the workflow
	exp <- new("mtkExpWorkflow", expFactors=WWDM.factors,
		   processesVector=c(
				              design=designer,
				              evaluate=model,
				              analyze=analyser)
			  				)
## Run and report the results
	run(exp)
	summary(exp)

Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.