Model Analysis

Introduction

The erecord web services can be used for models analysis, that can be managed for example from a R program to which the POST vpz/output resource can provide some simulations.

Example of Sensitivity Analysis in R language

For the simulator ‘wwdm.vpz’ whose Id is 266 (more) :

• download the R file and call it,

• or enter in a R interpreter the following R code/instructions :

  #!/usr/bin/env Rscript
  
  # ****************************************************************************
  #                  WWDM model - Sensitivity Analysis
  #
  # Sensitivity of LAI and U to A, B, Eb, Eimax, K, Lmax, TI parameters
  #
  # Parameters features :
  #
  # A     :  0.0065   [ 0.0035, 0.01   ]
  # B     :  0.00205  [ 0.0011, 0.0025 ]
  # Eb    :  1.85     [ 0.9   , 2.8    ]
  # Eimax :  0.94     [ 0.9   , 0.99   ]
  # K     :  0.7      [ 0.6   , 0.8    ]
  # Lmax  :  7.5      [ 3     , 12     ]
  # TI    :  900      [ 700   , 1100   ]
  #
  # ****************************************************************************
  
  library('RCurl')
  library('rjson')
  
  sim = function(inputsmatrix)
  {
      header = c('Content-Type'='application/json', Accept='application/json')
      options(RCurlOptions=list(followlocation=TRUE))
  
      postfields = toJSON(list(
          vpz=266, # id=266 of wwdm.vpz
          duration=120, begin=2453982.0,
          cond_wwdm.A     = inputsmatrix[,1],
          cond_wwdm.B     = inputsmatrix[,2],
          cond_wwdm.Eb    = inputsmatrix[,3],
          cond_wwdm.Eimax = inputsmatrix[,4],
          cond_wwdm.K     = inputsmatrix[,5],
          cond_wwdm.Lmax  = inputsmatrix[,6],
          cond_wwdm.TI    = inputsmatrix[,7],
          mode=c("tree", "linear", "dataframe"),
          outselect="all", format="json"))
  
      res = postForm(uri="http://erecord.toulouse.inra.fr:8000/vpz/output/",
          .opts=list(postfields=postfields, httpheader=header))
      
      responsedata = fromJSON(res)
      res = fromJSON(responsedata$res)
      return(res)
  }
  
  # returns LAI value for each (A, B, Eb, Eimax, K, Lmax, TI) of inputsmatrix
  simLAI = function(inputsmatrix)
  {
      res = sim(inputsmatrix)
      lai = unlist(lapply( 1:nrow(inputsmatrix),
                           function(i){mean(res[[i]]$view$`top:wwdm.LAI`)}))
      return(lai)
  }
  
  # returns U value for each (A, B, Eb, Eimax, K, Lmax, TI) of inputsmatrix
  simU = function(inputsmatrix)
  {
      res = sim(inputsmatrix)
      U = unlist(lapply( 1:nrow(inputsmatrix),
                         function(i){mean(res[[i]]$view$`top:wwdm.U`)}))
      return(U)
  }
  
  library('sensitivity')
  
  LAI_sensitivity = function()
  {
      sensitivity = fast99(
         model = simLAI,
         factors = c("A", "B", "Eb", "Eimax", "K", "Lmax", "TI"),
         n = 70,
         q = c("qunif", "qunif", "qunif", "qunif", "qunif", "qunif", "qunif"),
         q.arg = list(list(min = 0.0035, max = 0.01),
                      list(min = 0.0011, max = 0.0025),
                      list(min = 0.9   , max = 2.8),
                      list(min = 0.9   , max = 0.99),
                      list(min = 0.6   , max = 0.8 ),
                      list(min = 3.0   , max = 12.0),
                      list(min = 700.0 , max = 1100.0)))
      return(sensitivity)
  }
  
  U_sensitivity = function()
  {
      sensitivity = fast99(
         model = simU,
         factors = c("A", "B", "Eb", "Eimax", "K", "Lmax", "TI"),
         n = 70,
         q = c("qunif", "qunif", "qunif", "qunif", "qunif", "qunif", "qunif"),
         q.arg = list(list(min = 0.0035, max = 0.01),
                      list(min = 0.0011, max = 0.0025),
                      list(min = 0.9   , max = 2.8),
                      list(min = 0.9   , max = 0.99),
                      list(min = 0.6   , max = 0.8 ),
                      list(min = 3.0   , max = 12.0),
                      list(min = 700.0 , max = 1100.0)))
      return(sensitivity)
  }
  
  #
  # source( "wwdm.R" )
  #
  # s = LAI_sensitivity()
  # print(s)
  # plot(s)
  #
  # s = U_sensitivity()
  # print(s)
  # plot(s)
  #
  
  

  s = LAI_sensitivity()
  print(s)
  plot(s)
  
  s = U_sensitivity()
  print(s)
  plot(s)