R/minimal_model_MRMC.R

In BayesianFROC: FROC Analysis by Bayesian Approaches

minimal_model_MRMC  <- function() {

  # dataList <- dd
  # m <-dataList$m
  # q<-dataList$q
  # c<-dataList$c
  # h<-dataList$h
  # f<-dataList$f
  # NI<-dataList$NI
  # NL<-dataList$NL
  # C<-dataList$C
  # M<-dataList$M
  # Q<-dataList$Q


  m <-c(1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,1,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,2,3,3,3,3
  ,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,3,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,4,5,5,5,5,5,5,5,5
  ,5,5,5,5,5,5,5,5,5,5,5,5)

  q <-c(1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,1,1,1,1
        ,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,1,1,1,1,1,2,2,2,2,2,3,3,3,3,3,4,4,4,4,4,1,1,1,1,1,2,2,2
        ,2,2,3,3,3,3,3,4,4,4,4,4)


  c<-c(5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2
       ,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3,2,1,5,4,3
       ,2,1,5,4,3,2,1,5,4,3,2,1)

  f<-c(
     0,4,20,29,21,0,0,6,15,22,1,15,18,31,19,1,2,4,16,17,1,1,21,24,23,1,1,5,30
    ,40,2,19,31,56,42,2,0,2,30,32,1,7,13,28,19,0,1,7,7,31,7,15,28,41,9,0,2,5
    ,24,31,1,4,18,21,23,1,1,0,11,35,6,14,37,36,18,0,2,4,18,25,0,2,19,23,18,0,2
    ,6,10,30,2,25,40,29,24,1,1,4,24,32
  )


 h<-c(
    50,30,11,5,1,15,29,29,1,0,39,31,8,10,3,10,8,25,45,14,52,25,13,4,1,27,28,29,1
    ,0,53,29,13,2,4,9,16,22,43,14,43,29,11,6,0,18,29,21,0,0,43,29,6,7,1,10,14,19
    ,32,23,61,19,12,9,3,16,29,34,1,0,52,29,10,4,3,10,16,23,43,15,35,29,18,9,0,17,27
     ,24,0,0,34,33,7,13,2,12,16,21,35,15
  )

C<-5
M<-5
Q<-4
NI<-199
NL<-142




N <-C*M*Q

ff <- numeric(N) #Initialization of Cumulative False alarm
harray<-array(0,dim=c(C,M,Q));

for(md in 1:M) {
  for(cd in 1:C) {
    for(qd in 1 : Q){
      for(n  in 1:cd){
        ff[cd+(md-1)*C*Q+(qd-1)*C]<-ff[cd+(md-1)*C*Q+(qd-1)*C]+f[n+(md-1)*C*Q+(qd-1)*C]
      }
      harray[cd,md,qd] <- h[cd+(md-1)*C*Q+(qd-1)*C]
    }}}


data <- list(N=N,Q=Q, M=M,m=m  ,C=C  , NL=NL,NI=NI
             ,c=c,q=q,
             h=h, f=f,
             ff=ff,
             harray=harray,ModifiedPoisson=FALSE
             )




Stan.model <- rstan::stan_model(

  model_code="
data{
  int <lower=0>N;
  int <lower=0>M;
  int <lower=0>C;
  int <lower=0>Q;
  int <lower=0>h[N];
  int <lower=0>f[N];
  int <lower=0>q[N];
  int <lower=0>c[N];
  int <lower=0>m[N];
  int <lower=0>NL;
  int <lower=0>NI;

  int <lower=0>ff[N];
  int <lower=0>harray[C,M,Q];

  int ModifiedPoisson;//////Logical



}
transformed data {
  int <lower=0> NX;
if(ModifiedPoisson==0) NX = NI;
if(ModifiedPoisson==1) NX =NL;



print(\" 2019 Dec 25 Non hierarchical MRMC model           \")
print(\" 2019 Dec 25 Non hierarchical MRMC model           \")
print(\" 2019 Dec 25 Non hierarchical MRMC model           \")
print(\" 2019 Dec 25 Non hierarchical MRMC model           \")
print(\" 2019 Dec 25 Non hierarchical MRMC model           \")
print(\" 2019 Dec 25 Non hierarchical MRMC model           \")
print(\" 2019 Dec 25 Non hierarchical MRMC model           \")
print(\" 2019 Dec 25 Non hierarchical MRMC model           \")
print(\" 2019 Dec 25 Non hierarchical MRMC model           \")
print(\" 2019 Dec 25 Non hierarchical MRMC model           \")
print(\" 2019 Dec 25 Non hierarchical MRMC model           \")
print(\" 2019 Dec 25 Non hierarchical MRMC model           \")




}

parameters{
  real    w;
  real <lower =0  >  dz[C-1];
  real               mu[M,Q];
  real <lower=0>      v[M,Q];

}

transformed parameters {
  real <lower =0>       dl[C];
  real <lower=0,upper=1> ppp[C,M,Q];
  real <lower =0>      l[C];
  real    z[C];
  real                      aa[M,Q];
  real <lower =0>           bb[M,Q];
  real <lower=0,upper=1>    AA[M,Q];
  real deno[C-1,M,Q];
  real hit_rate[C,M,Q];
  real <lower=0,upper=1>A[M];

  z[1]=w;

  for(md in 1 : M) {
    for(qd in 1 : Q) {
      aa[md,qd]=mu[md,qd]/v[md,qd];
      bb[md,qd]=1/v[md,qd];

      for(cd in 1 : C-1) z[cd+1] = z[cd] + dz[cd];
      ppp[C,md,qd] = 1- Phi((z[C] -mu[md,qd])/v[md,qd]);

      for(cd in 1 : C-1) ppp[cd,md,qd] = Phi((z[cd+1] -mu[md,qd])/v[md,qd])  - Phi((z[cd ] -mu[md,qd])/v[md,qd]);



      for(cd in 1 : C) l[cd] = (-1)*log(Phi(z[cd]));
      dl[C] = fabs(l[C]-0);
      for(cd in 1:C-1) dl[cd]= fabs(l[cd]-l[cd+1]);




    }
  }

  for(md in 1 : M) {
    for(qd in 1 : Q) {
      AA[md,qd]=Phi(  (mu[md,qd]/v[md,qd])/sqrt((1/v[md,qd])^2+1)  );//////Measures of modality performance
    }}

  for(md in 1 : M) {
   A[md] = 0;
    for(qd in 1 : Q) {
     A[md] =  A[md] +  AA[md,qd];
    }
   A[md]=   A[md]/Q;
    }


  for(md in 1 : M) {
    for(qd in 1 : Q) {
      deno[C-1,md,qd]=1-ppp[C,md,qd];
      for(cd in 3:C){  deno[c[cd],md,qd]=deno[c[cd-1],md,qd]-ppp[c[cd-1],md,qd];  }
    }}


  for(md in 1 : M) {
    for(qd in 1 : Q) {
      for(cd in 1:C-1){
        hit_rate[cd,md,qd]=ppp[cd,md,qd]/deno[cd,md,qd];
      }
      hit_rate[C,md,qd]=ppp[C,md,qd];

    }}



}






model{
    int s=0;

    for(n in 1:N) {
      target +=   poisson_lpmf(ff[n]|l[c[n]]*NX);
    }




    for(qd in 1 : Q) {
      for(md in 1 : M) {
        s=0;
        for(cd in 1 : C){
           target += binomial_lpmf(harray[cd,md,qd]  |  NL-s, hit_rate[c[cd],md,qd]  );
          s = s + harray[cd,md,qd]; }
        }}








      w ~  uniform(-3,3);
      for(cd in 1:C-1) dz[cd] ~  uniform(0.001,7);
      for(md in 1 : M) { for(qd in 1 : Q) {
        mu[md,qd] ~ uniform(-11,11);
        v[md,qd] ~ uniform(0.01,11);

      }}





  }
")


# fit  <-  rstan::sampling(
#   object= Stan.model, data=metadata_to_fit_MRMC(data_2modaities_2readers_3confidence),  verbose = FALSE,
#   seed=123, chains=1, warmup=111, iter=32222
#   , control = list(adapt_delta = 0.9999999,
#                    max_treedepth = 15)
#   # ,init = initial
# )

# rstan::traceplot(fit,pars=c("w"))
# rstan::check_hmc_diagnostics(fit)



# fit  <-  rstan::sampling(
#   object= Stan.model, data=metadata_to_fit_MRMC(dd),  verbose = FALSE,
#   seed=1234567, chains=1, warmup=111, iter=12222
#   , control = list(adapt_delta = 0.9999999,
#                    max_treedepth = 15)
#   # ,init = initial
# )
#
# rstan::traceplot(fit,pars=c("w"))
# rstan::check_hmc_diagnostics(fit)



#
#
#
# fit  <-  rstan::sampling(
#   object= Stan.model, data=data,  verbose = FALSE,
#   seed=1234567, chains=1, warmup=111, iter=1111
#   , control = list(adapt_delta = 0.9999999,
#                    max_treedepth = 15)
#   # ,init = initial
# )
#
# rstan::traceplot(fit,pars=c("w"))
# rstan::check_hmc_diagnostics(fit)
#
#


#
# fit  <-  rstan::sampling(
#   object= Stan.model, data=data,  verbose = FALSE,
#   seed=123, chains=1, warmup=111, iter=122
#   , control = list(adapt_delta = 0.9999999,
#                    max_treedepth = 15)
#   # ,init = initial
# )
#
# rstan::traceplot(fit,pars=c("w"))
# rstan::check_hmc_diagnostics(fit)

#
#
#
#
#
fit  <-  rstan::sampling(
  object= Stan.model, data=data,  verbose = FALSE,
  seed=1, chains=1, warmup=11, iter=111,
  sample_file =paste0(file.path(Sys.getenv("USERPROFILE"),"Desktop"),"\\samples"),
   control = list(adapt_delta = 0.9999999,
                   max_treedepth = 15)
  # ,init = initial
)

rstan::traceplot(fit,pars=c("w"))
rstan::check_hmc_diagnostics(fit)

#
#
#
# fit  <-  rstan::sampling(
#   object= Stan.model, data=data,  verbose = FALSE,
#   seed=1, chains=1, warmup=111, iter=122
#   , control = list(adapt_delta = 0.9999999,
#                    max_treedepth = 15)
#   # ,init = initial
# )
#
# rstan::traceplot(fit,pars=c("w"))
# rstan::check_hmc_diagnostics(fit)
#



}#fun