added example of overload fn to track REPORT

TMB examples/Track report objects/Track_report.R

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+
+setwd( "C:/Users/James.Thorson/Desktop/Project_git/2018_FSH556/TMB examples/Track report objects/" )
+
+library(TMB)
+library(RandomFields)
+library(Matrix)
+
+###################
+# Equal distance 2D autoregressive
+###################
+
+Dim = c("n_x"=10, "n_y"=10)
+loc_xy = expand.grid("x"=1:Dim['n_x'], "y"=1:Dim['n_y'])
+Scale = 5
+Sigma2 = (0.5) ^ 2
+beta0 = 3
+prob_missing = 0.2
+
+# Simulate spatial process
+RMmodel = RMexp(var=Sigma2, scale=Scale)
+epsilon_xy = array(RFsimulate(model=RMmodel, x=loc_xy[,'x'], y=loc_xy[,'y'])@data[,1], dim=Dim)
+image( z=epsilon_xy )
+
+# SImulate counts
+c_xy = array(NA, dim=dim(epsilon_xy))
+for(x in 1:nrow(c_xy)){
+for(y in 1:ncol(c_xy)){
+  c_xy[x,y] = rpois(1, exp(beta0 + epsilon_xy[x,y]) )
+  if( rbinom(n=1, size=1, prob=prob_missing)==1) c_xy[x,y] = NA
+}}
+true_abundance =  sum( exp(beta0 + epsilon_xy) )
+
+# Generate sparse matrices for precision matrix of 2D AR1 process
+M0 = as( ifelse(as.matrix(dist(loc_xy,diag=TRUE,upper=TRUE))==0,1,0), "dgTMatrix" )
+M1 = as( ifelse(as.matrix(dist(loc_xy,diag=TRUE,upper=TRUE))==1,1,0), "dgTMatrix" )
+M2 = as( ifelse(as.matrix(dist(loc_xy,diag=TRUE,upper=TRUE))==sqrt(2),1,0), "dgTMatrix" )
+
+# Compile
+Params = list( "beta0"=0, "ln_sigma2"=0, "logit_rho"=0, "epsilon_xy"=array(rnorm(prod(dim(loc_xy)))-100,dim=dim(epsilon_xy)) )
+compile( "autoregressive_grid.cpp" )
+dyn.load( dynlib("autoregressive_grid") )
+
+# Build object
+Data = list("c_xy"=c_xy, "M0"=M0, "M1"=M1, "M2"=M2 )
+Obj = MakeADFun( data=Data, parameters=Params, random="epsilon_xy", DLL="autoregressive_grid" )
+
+# Overload marginal log-likelihood function
+Obj$fn_orig = Obj$fn
+Obj$fn = function( par ){
+  MargNLL = Obj$fn_orig( par )
+  Report = Obj$report()
+  Total_Abundance = rbind(Total_Abundance, c(nrow(Total_Abundance)+1, Report$Total_Abundance) )
+  colnames(Total_Abundance) = c("Function_call","Total_Abundance")
+  assign( "Total_Abundance", Total_Abundance, pos=.GlobalEnv )
+  return( MargNLL )
+}
+
+# Optimize
+Total_Abundance = data.frame()
+Opt = TMBhelper::Optimize( obj=Obj, fn=Obj$fn_trace, newtonsteps=1 )
+

TMB examples/Track report objects/autoregressive_grid.cpp

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+
+#include <TMB.hpp>
+// Function for detecting NAs
+template<class Type>
+bool isNA(Type x){
+  return R_IsNA(asDouble(x));
+}
+
+// Space time
+template<class Type>
+Type objective_function<Type>::operator() ()
+{
+  // Data
+  DATA_MATRIX( c_xy );
+
+  // Sparse matrices for forming the AR precision matrix (Version #4 below)
+  // Q = M0*(1+rho^2)^2 + M1*(1+rho^2)*(-rho) + M2*rho^2
+  DATA_SPARSE_MATRIX(M0);
+  DATA_SPARSE_MATRIX(M1);
+  DATA_SPARSE_MATRIX(M2);
+
+  // Parameters
+  PARAMETER( beta0 );
+  PARAMETER( ln_sigma2 );
+  PARAMETER( logit_rho );
+
+  // Random effects
+  PARAMETER_ARRAY( epsilon_xy );
+
+  // Objective funcction
+  int n_x = c_xy.col(0).size();
+  int n_y = c_xy.row(0).size();
+  vector<Type> jnll_comp(2);
+  jnll_comp.setZero();
+  Type sigma2 = exp(ln_sigma2);
+  Type rho = 1 / (1 + exp(-logit_rho));
+
+  //// Calculate using built-in TMB functions
+  using namespace density;
+  Eigen::SparseMatrix<Type> Q_zz = ( M0*pow(1+pow(rho,2),2) + M1*(1+pow(rho,2))*(-rho) + M2*pow(rho,2) ) / sigma2;
+  jnll_comp(1) += GMRF(Q_zz)( epsilon_xy );
+
+  // Probability of data conditional on random effects
+  Type Total_Abundance = 0;
+  for( int x=0; x<n_x; x++){
+  for( int y=0; y<n_y; y++){
+    if( !isNA(c_xy(x,y)) ) jnll_comp(0) -= dpois( c_xy(x,y), exp(beta0 + epsilon_xy(x,y)), true );
+    Total_Abundance += exp(beta0 + epsilon_xy(x,y));
+  }}
+
+  // Reporting
+  Type jnll = jnll_comp.sum();
+  REPORT( jnll_comp );
+  REPORT( jnll );
+  REPORT( sigma2 );
+  REPORT( rho );
+  REPORT( Total_Abundance );
+  ADREPORT( Total_Abundance );
+
+  return jnll;
+}