167: internal constructor for Model objects

In SoilR: Models of Soil Organic Matter Decomposition

Description

Note that we encourage the use of more convienient constructors for the creation of model objects. Since this method is tightly coupled to the internal implementation of the class it is much more likely to change in the future than the other constructors, which can be kept stable much more easily in the future and are therefor encouraged for user code. This method implements R's initialize generic for objects of class Model It is called whenever a new object of this class is created by a call to new with the first argument Model. It performs some sanity checks of its arguments and in case those tests pass returns an object of class Model. The checks can be turned off.( see arguments)

Arguments

.Object
times
mat	A decomposition Operator of some kind
initialValues
inputFluxes
solverfunc
pass

Details

Due to the mechanism of S4 object initialization (package "methods") new always calls initialize. (see the help pages for initialize and initialize-methods for details)

Value

an Object of class Model

Author(s)

Carlos A. Sierra, Markus Mueller

Examples

#
# vim:set ff=unix expandtab ts=2 sw=2:
require(RUnit)
# We present three possible scenarios:
# 1.) create an object from valid input
# 2.) try to build an Model object with unsound parameters and 
#     show the savety net in action.
# 3.) force an unsound model to be created that would be rejected by default
# 4.) show some other insensible models being rejected 
#     
#1.) we first create a sensible model
  t_start=0 
  t_end=10 
  tn=50
  timestep=(t_end-t_start)/tn 
  t=seq(t_start,t_end,timestep) 
  A=BoundLinDecompOp(
    function(times){
      matrix(nrow=3,ncol=3,byrow=TRUE,
    	     c(-1,    0,    0, 
    	      0.5,   -2,    0,   
    	        0,    1, -0.5)
      )    
    },
    t_start,
    t_end
  )  
  I=BoundInFlux(
     function(times){
       matrix(nrow=3,ncol=1,byrow=TRUE,
           c(-1,    0,    0)
       )
     },
     t_start,
     t_end
  )
  res=Model(t,A,c(0,0,0),I)
#2.)
# Now we present some examples where the constructor protests
# test.correctnessOfModel.impossibleCoefficients
   t_start=0 
   t_end=10 
   tn=50
   timestep=(t_end-t_start)/tn 
   t=seq(t_start,t_end,timestep) 

   A=BoundLinDecompOp(
      function(times){
        matrix(nrow=3,ncol=3,byrow=TRUE,
            c(-1,    0,    0, 
            1, -0.7,    0,   
            0,    1, -0.5)
        )
      },
      t_start,
      t_end
   )   
   I=BoundInFlux(
      function(times){
        matrix(nrow=3,ncol=1,byrow=TRUE,
            c(-1,    0,    0)
        )
      },
      t_start,
      t_end
    )
  checkException(
	Model(t,A,c(0,0,0),I), 
	"correctnessOfModel should have returned FALSE 
	because the matrix values indicate unbiological 
	behavior (ruwsum should be smaller than zero), 
	but has not",silent=TRUE)
#3.) 
# force it nevertheless 
	Model(t,A,c(0,0,0),I,pass=TRUE) 

#4.) further examples	
# test.correctnessOfModel.impossibleTimeRanges
   mess="correctnessOfModel should have returned FALSE, but has not"
   t_start=0 
   t_end=10 
   tdiff=t_end-t_start
   tn=50
   timestep=(tdiff)/tn 
   t=seq(t_start,t_end,timestep) 

   #we create an A(t) with sensible coeficients 
   #but where the time range begins to late 

   A=BoundLinDecompOp(
      function(t){
        matrix(nrow=3,ncol=3,byrow=TRUE,
            c(-1,    0,    0, 
            1, -0.7,    0,   
            0,    0.5, -0.5)
        )
      },
      t_start+1/4*tdiff,
      t_end
   )   
   I=BoundInFlux(
      function(times){
        matrix(nrow=3,ncol=1,byrow=TRUE,
            c(-1,    0,    0)
        )
      },
      t_start,
      t_end
    )
   
   checkException(Model(t,A,c(0,0,0),I),mess,silent=TRUE)
   #now we do the same to the InFluxes(t) while A(t) is correct 
   A=BoundLinDecompOp(
      function(times){
        matrix(nrow=3,ncol=3,byrow=TRUE,
            c(-1,    0,    0, 
            1, -0.7,    0,   
            0,  0.5, -0.5)
        )
      },
      t_start,
      t_end
   )   
   I=BoundInFlux(
      function(times){
        matrix(nrow=3,ncol=1,byrow=TRUE,
            c(-1,    0,    0)
        )
      },
      t_start+1/4*tdiff,
      t_end
    )
   checkException(Model(t,A,c(0,0,0),I),mess,silent=TRUE)

   #we create an A(t) with sensible coeficients 
   #but where the time range ends to early 

   A=BoundLinDecompOp(
      function(times){
        matrix(nrow=3,ncol=3,byrow=TRUE,
            c(-1,    0,    0, 
            1, -0.7,    0,   
            0,    0.5, -0.5)
        )
      },
      t_start,
      t_end-1/4*tdiff
   )   
   I=BoundInFlux(
      function(times){
        matrix(nrow=3,ncol=1,byrow=TRUE,
            c(-1,    0,    0)
        )
      },
      t_start,
      t_end
    )
   checkException(Model(t,A,c(0,0,0),I),mess,silent=TRUE)
   #now we do the same to the InFluxes(t) while A(t) is correct 
   A=BoundLinDecompOp(
      function(times){
        matrix(nrow=3,ncol=3,byrow=TRUE,
            c(-1,    0,    0, 
            1, -0.7,    0,   
            0,  0.5, -0.5)
        )
      },
      t_start,
      t_end
   )   
   I=BoundInFlux(
      function(times){
        matrix(nrow=3,ncol=1,byrow=TRUE,
            c(-1,    0,    0)
        )
      },
      t_start,
      t_end-1/4*tdiff
    )
   checkException(Model(t,A,c(0,0,0),I),mess,silent=TRUE)

SoilR documentation built on May 4, 2017, 9:08 p.m.