max.ts = 15
delta = 1
startproxel=c(1,0,0,1,0)
RG=matrix(c(1,1,0,1,
            0,1,1,1,
            0,0,1,1,
            0,0,0,1),ncol = 4,byrow = T)

Proxel_algo=function(RG,startproxel,delta,max.ts,Print=T){
  
counter_prob=0

unihrf <- function(age, a, b) {
  if (a <= age && age < b) {
    return (1.0 / (b - age))
  } else {
    return (0)
  }
}

exphrf <- function(age, l) {
  return (l)
}
  
proxel_list=list()
proxel_list[[max.ts+1]]=list()
proxel_list[[1]]=list(STEP=c(0),proxel=data.frame(state=startproxel[1],tau=startproxel[2],tau_k=startproxel[3],prob=startproxel[4],prev=startproxel[5]))
if(Print) print("initial proxel:")
if(Print) print(proxel_list[[1]])

for (timestep in 1:max.ts) {
  proxel_list[[timestep+1]]=list(timestep=c(timestep),proxel=data.frame(state=numeric(),tau=numeric(),tau_k=numeric(),prob=numeric(),prev=numeric()))     # erstelle schonmal eine neue leere liste für den nächsten timestep
  
  for (l in 1:nrow(proxel_list[[timestep]]$proxel)) {
    currprox = proxel_list[[timestep]]$proxel[l,]                                     # aktueller proxel zu dem folgeproxels erzeugt werden sollen
    folger = RG[as.numeric(currprox[1]),]                                             # folgestates von aktuellem proxel
    
    for (i in 1:4) {
      if (folger[i]==1) {                                                             # erstelle folgeproxel falls pfeil dahin gibt
        proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel)+1,1] = i                       # state vom nächsten proxel
        
        if(currprox$state==i) proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),2] = currprox$tau + delta
          else proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),2] = 0                    # tau vom nächsten proxel
        
        if(currprox$state %in% c(1,2,3) & i!=4) proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),3] = currprox$tau_k + delta
          else if(currprox$state==4 & i==4) proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),3] = currprox$tau_k - delta
            else proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),3] = 0                   # tau_k vom nächsten proxel
          
        if(currprox$state==1 & i==2) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),4] = (unihrf(currprox$tau_k,2,4) * delta) * currprox$prob
          counter_prob = counter_prob + unihrf(currprox$tau_k,2,4) * delta}                 # transition von S1 zu S2
        if(currprox$state==2 & i==3) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),4] = (exphrf(currprox$tau_k,0.1) * delta) * currprox$prob
          counter_prob = counter_prob + exphrf(currprox$tau_k,0.1) * delta}                 # transition von S2 zu S3
        if(currprox$state!=4 & i==4) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),4] = (unihrf(currprox$tau_k,7,14)* delta) * currprox$prob
          counter_prob = counter_prob + unihrf(currprox$tau_k,7,14)* delta}                 # transtion zu heal von S1,S2 oder S3
        if(currprox$state==1 & i==1) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),4] = (1 - (unihrf(currprox$tau_k,2,4) * delta) - (unihrf(currprox$tau_k,7,14)* delta)) * currprox$prob}
        if(currprox$state==2 & i==2) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),4] = (1 - (exphrf(currprox$tau_k,0.1) * delta) - (unihrf(currprox$tau_k,7,14)* delta)) * currprox$prob}
        if(currprox$state==3 & i==3) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),4] = (1 - (unihrf(currprox$tau_k,7,14)* delta)) * currprox$prob}
        if(currprox$state==4 & i==4) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),4] = 1 }
            
         proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),5] = l   
            
      }
    } 
    
    #proxel_list[[timestep+1]]$proxel[which(is.na(proxel_list[[timestep+1]]$proxel[,4])),4] = 1-counter_prob #* proxel_list[[timestep]]$proxel$prob[l] # rest wkt im state zu bleiben
    counter_prob=0
  }
  if(length(which(proxel_list[[timestep+1]]$proxel[,4]<=0))!=0) proxel_list[[timestep+1]]$proxel=proxel_list[[timestep+1]]$proxel[-which(proxel_list[[timestep+1]]$proxel[,4]<=0),]
  if(length(which(proxel_list[[timestep+1]]$proxel[,3]< 0))!=0) proxel_list[[timestep+1]]$proxel=proxel_list[[timestep+1]]$proxel[-which(proxel_list[[timestep+1]]$proxel[,3]< 0),]
  
  if(Print) print(paste("Time step:",timestep))
  if(Print) print(proxel_list[[timestep+1]])
}

return(proxel_list)
}
proxel_list=Proxel_algo(RG,startproxel,delta,max.ts,T)
## [1] "initial proxel:"
## $STEP
## [1] 0
## 
## $proxel
##   state tau tau_k prob prev
## 1     1   0     0    1    0
## 
## [1] "Time step: 1"
## $timestep
## [1] 1
## 
## $proxel
##   state tau tau_k prob prev
## 1     1   1     1    1    1
## 
## [1] "Time step: 2"
## $timestep
## [1] 2
## 
## $proxel
##   state tau tau_k prob prev
## 1     1   2     2    1    1
## 
## [1] "Time step: 3"
## $timestep
## [1] 3
## 
## $proxel
##   state tau tau_k prob prev
## 1     1   3     3  0.5    1
## 2     2   0     3  0.5    1
## 
## [1] "Time step: 4"
## $timestep
## [1] 4
## 
## $proxel
##   state tau tau_k prob prev
## 2     2   0     4 0.50    1
## 4     2   1     4 0.45    2
## 5     3   0     4 0.05    2
## 
## [1] "Time step: 5"
## $timestep
## [1] 5
## 
## $proxel
##   state tau tau_k  prob prev
## 1     2   1     5 0.450    1
## 2     3   0     5 0.050    1
## 4     2   2     5 0.405    2
## 5     3   0     5 0.045    2
## 7     3   1     5 0.050    3
## 
## [1] "Time step: 6"
## $timestep
## [1] 6
## 
## $proxel
##    state tau tau_k   prob prev
## 1      2   2     6 0.4050    1
## 2      3   0     6 0.0450    1
## 4      3   1     6 0.0500    2
## 6      2   3     6 0.3645    3
## 7      3   0     6 0.0405    3
## 9      3   1     6 0.0450    4
## 11     3   2     6 0.0500    5
## 
## [1] "Time step: 7"
## $timestep
## [1] 7
## 
## $proxel
##    state tau tau_k    prob prev
## 1      2   3     7 0.36450    1
## 2      3   0     7 0.04050    1
## 4      3   1     7 0.04500    2
## 6      3   2     7 0.05000    3
## 8      2   4     7 0.32805    4
## 9      3   0     7 0.03645    4
## 11     3   1     7 0.04050    5
## 13     3   2     7 0.04500    6
## 15     3   3     7 0.05000    7
## 
## [1] "Time step: 8"
## $timestep
## [1] 8
## 
## $proxel
##    state tau tau_k        prob prev
## 1      2   4     8 0.275978571    1
## 2      3   0     8 0.036450000    1
## 3      4   0     0 0.052071429    1
## 4      3   1     8 0.034714286    2
## 5      4   0     0 0.005785714    2
## 6      3   2     8 0.038571429    3
## 7      4   0     0 0.006428571    3
## 8      3   3     8 0.042857143    4
## 9      4   0     0 0.007142857    4
## 10     2   5     8 0.248380714    5
## 11     3   0     8 0.032805000    5
## 12     4   0     0 0.046864286    5
## 13     3   1     8 0.031242857    6
## 14     4   0     0 0.005207143    6
## 15     3   2     8 0.034714286    7
## 16     4   0     0 0.005785714    7
## 17     3   3     8 0.038571429    8
## 18     4   0     0 0.006428571    8
## 19     3   4     8 0.042857143    9
## 20     4   0     0 0.007142857    9
## 
## [1] "Time step: 9"
## $timestep
## [1] 9
## 
## $proxel
##    state tau tau_k        prob prev
## 1      2   5     9 0.202384286    1
## 2      3   0     9 0.027597857    1
## 3      4   0     0 0.045996429    1
## 4      3   1     9 0.030375000    2
## 5      4   0     0 0.006075000    2
## 7      3   2     9 0.028928571    4
## 8      4   0     0 0.005785714    4
## 10     3   3     9 0.032142857    6
## 11     4   0     0 0.006428571    6
## 13     3   4     9 0.035714286    8
## 14     4   0     0 0.007142857    8
## 16     2   6     9 0.182145857   10
## 17     3   0     9 0.024838071   10
## 18     4   0     0 0.041396786   10
## 19     3   1     9 0.027337500   11
## 20     4   0     0 0.005467500   11
## 22     3   2     9 0.026035714   13
## 23     4   0     0 0.005207143   13
## 25     3   3     9 0.028928571   15
## 26     4   0     0 0.005785714   15
## 28     3   4     9 0.032142857   17
## 29     4   0     0 0.006428571   17
## 31     3   5     9 0.035714286   19
## 32     4   0     0 0.007142857   19
## 
## [1] "Time step: 10"
## $timestep
## [1] 10
## 
## $proxel
##    state tau tau_k        prob prev
## 1      2   6    10 0.141669000    1
## 2      3   0    10 0.020238429    1
## 3      4   0     0 0.040476857    1
## 4      3   1    10 0.022078286    2
## 5      4   0     0 0.005519571    2
## 7      3   2    10 0.024300000    4
## 8      4   0     0 0.006075000    4
## 10     3   3    10 0.023142857    6
## 11     4   0     0 0.005785714    6
## 13     3   4    10 0.025714286    8
## 14     4   0     0 0.006428571    8
## 16     3   5    10 0.028571429   10
## 17     4   0     0 0.007142857   10
## 19     2   7    10 0.127502100   12
## 20     3   0    10 0.018214586   12
## 21     4   0     0 0.036429171   12
## 22     3   1    10 0.019870457   13
## 23     4   0     0 0.004967614   13
## 25     3   2    10 0.021870000   15
## 26     4   0     0 0.005467500   15
## 28     3   3    10 0.020828571   17
## 29     4   0     0 0.005207143   17
## 31     3   4    10 0.023142857   19
## 32     4   0     0 0.005785714   19
## 34     3   5    10 0.025714286   21
## 35     4   0     0 0.006428571   21
## 37     3   6    10 0.028571429   23
## 38     4   0     0 0.007142857   23
## 
## [1] "Time step: 11"
## $timestep
## [1] 11
## 
## $proxel
##    state tau tau_k        prob prev
## 1      2   7    11 0.092084850    1
## 2      3   0    11 0.014166900    1
## 3      4   0     0 0.035417250    1
## 4      3   1    11 0.015178821    2
## 5      4   0     0 0.005059607    2
## 7      3   2    11 0.016558714    4
## 8      4   0     0 0.005519571    4
## 10     3   3    11 0.018225000    6
## 11     4   0     0 0.006075000    6
## 13     3   4    11 0.017357143    8
## 14     4   0     0 0.005785714    8
## 16     3   5    11 0.019285714   10
## 17     4   0     0 0.006428571   10
## 19     3   6    11 0.021428571   12
## 20     4   0     0 0.007142857   12
## 22     2   8    11 0.082876365   14
## 23     3   0    11 0.012750210   14
## 24     4   0     0 0.031875525   14
## 25     3   1    11 0.013660939   15
## 26     4   0     0 0.004553646   15
## 28     3   2    11 0.014902843   17
## 29     4   0     0 0.004967614   17
## 31     3   3    11 0.016402500   19
## 32     4   0     0 0.005467500   19
## 34     3   4    11 0.015621429   21
## 35     4   0     0 0.005207143   21
## 37     3   5    11 0.017357143   23
## 38     4   0     0 0.005785714   23
## 40     3   6    11 0.019285714   25
## 41     4   0     0 0.006428571   25
## 43     3   7    11 0.021428571   27
## 44     4   0     0 0.007142857   27
## 
## [1] "Time step: 12"
## $timestep
## [1] 12
## 
## $proxel
##    state tau tau_k        prob prev
## 1      2   8    12 0.052181415    1
## 2      3   0    12 0.009208485    1
## 3      4   0     0 0.030694950    1
## 4      3   1    12 0.009444600    2
## 5      4   0     0 0.004722300    2
## 7      3   2    12 0.010119214    4
## 8      4   0     0 0.005059607    4
## 10     3   3    12 0.011039143    6
## 11     4   0     0 0.005519571    6
## 13     3   4    12 0.012150000    8
## 14     4   0     0 0.006075000    8
## 16     3   5    12 0.011571429   10
## 17     4   0     0 0.005785714   10
## 19     3   6    12 0.012857143   12
## 20     4   0     0 0.006428571   12
## 22     3   7    12 0.014285714   14
## 23     4   0     0 0.007142857   14
## 25     2   9    12 0.046963274   16
## 26     3   0    12 0.008287637   16
## 27     4   0     0 0.027625455   16
## 28     3   1    12 0.008500140   17
## 29     4   0     0 0.004250070   17
## 31     3   2    12 0.009107293   19
## 32     4   0     0 0.004553646   19
## 34     3   3    12 0.009935229   21
## 35     4   0     0 0.004967614   21
## 37     3   4    12 0.010935000   23
## 38     4   0     0 0.005467500   23
## 40     3   5    12 0.010414286   25
## 41     4   0     0 0.005207143   25
## 43     3   6    12 0.011571429   27
## 44     4   0     0 0.005785714   27
## 46     3   7    12 0.012857143   29
## 47     4   0     0 0.006428571   29
## 49     3   8    12 0.014285714   31
## 50     4   0     0 0.007142857   31
## 
## [1] "Time step: 13"
## $timestep
## [1] 13
## 
## $proxel
##    state tau tau_k        prob prev
## 1      2   9    13 0.020872566    1
## 2      3   0    13 0.005218142    1
## 3      4   0     0 0.026090708    1
## 4      3   1    13 0.004604243    2
## 5      4   0     0 0.004604243    2
## 7      3   2    13 0.004722300    4
## 8      4   0     0 0.004722300    4
## 10     3   3    13 0.005059607    6
## 11     4   0     0 0.005059607    6
## 13     3   4    13 0.005519571    8
## 14     4   0     0 0.005519571    8
## 16     3   5    13 0.006075000   10
## 17     4   0     0 0.006075000   10
## 19     3   6    13 0.005785714   12
## 20     4   0     0 0.005785714   12
## 22     3   7    13 0.006428571   14
## 23     4   0     0 0.006428571   14
## 25     3   8    13 0.007142857   16
## 26     4   0     0 0.007142857   16
## 28     2  10    13 0.018785309   18
## 29     3   0    13 0.004696327   18
## 30     4   0     0 0.023481637   18
## 31     3   1    13 0.004143818   19
## 32     4   0     0 0.004143818   19
## 34     3   2    13 0.004250070   21
## 35     4   0     0 0.004250070   21
## 37     3   3    13 0.004553646   23
## 38     4   0     0 0.004553646   23
## 40     3   4    13 0.004967614   25
## 41     4   0     0 0.004967614   25
## 43     3   5    13 0.005467500   27
## 44     4   0     0 0.005467500   27
## 46     3   6    13 0.005207143   29
## 47     4   0     0 0.005207143   29
## 49     3   7    13 0.005785714   31
## 50     4   0     0 0.005785714   31
## 52     3   8    13 0.006428571   33
## 53     4   0     0 0.006428571   33
## 55     3   9    13 0.007142857   35
## 56     4   0     0 0.007142857   35
## 
## [1] "Time step: 14"
## $timestep
## [1] 14
## 
## $proxel
##    state tau tau_k        prob prev
## 2      3   0    14 0.002087257    1
## 3      4   0     0 0.020872566    1
## 5      4   0     0 0.005218142    2
## 8      4   0     0 0.004604243    4
## 11     4   0     0 0.004722300    6
## 14     4   0     0 0.005059607    8
## 17     4   0     0 0.005519571   10
## 20     4   0     0 0.006075000   12
## 23     4   0     0 0.005785714   14
## 26     4   0     0 0.006428571   16
## 29     4   0     0 0.007142857   18
## 32     3   0    14 0.001878531   20
## 33     4   0     0 0.018785309   20
## 35     4   0     0 0.004696327   21
## 38     4   0     0 0.004143818   23
## 41     4   0     0 0.004250070   25
## 44     4   0     0 0.004553646   27
## 47     4   0     0 0.004967614   29
## 50     4   0     0 0.005467500   31
## 53     4   0     0 0.005207143   33
## 56     4   0     0 0.005785714   35
## 59     4   0     0 0.006428571   37
## 62     4   0     0 0.007142857   39
## 
## [1] "Time step: 15"
## $timestep
## [1] 15
## 
## $proxel
##    state tau tau_k        prob prev
## 1      3   1    15 0.002087257    1
## 13     3   1    15 0.001878531   12
proxel_list
## [[1]]
## [[1]]$STEP
## [1] 0
## 
## [[1]]$proxel
##   state tau tau_k prob prev
## 1     1   0     0    1    0
## 
## 
## [[2]]
## [[2]]$timestep
## [1] 1
## 
## [[2]]$proxel
##   state tau tau_k prob prev
## 1     1   1     1    1    1
## 
## 
## [[3]]
## [[3]]$timestep
## [1] 2
## 
## [[3]]$proxel
##   state tau tau_k prob prev
## 1     1   2     2    1    1
## 
## 
## [[4]]
## [[4]]$timestep
## [1] 3
## 
## [[4]]$proxel
##   state tau tau_k prob prev
## 1     1   3     3  0.5    1
## 2     2   0     3  0.5    1
## 
## 
## [[5]]
## [[5]]$timestep
## [1] 4
## 
## [[5]]$proxel
##   state tau tau_k prob prev
## 2     2   0     4 0.50    1
## 4     2   1     4 0.45    2
## 5     3   0     4 0.05    2
## 
## 
## [[6]]
## [[6]]$timestep
## [1] 5
## 
## [[6]]$proxel
##   state tau tau_k  prob prev
## 1     2   1     5 0.450    1
## 2     3   0     5 0.050    1
## 4     2   2     5 0.405    2
## 5     3   0     5 0.045    2
## 7     3   1     5 0.050    3
## 
## 
## [[7]]
## [[7]]$timestep
## [1] 6
## 
## [[7]]$proxel
##    state tau tau_k   prob prev
## 1      2   2     6 0.4050    1
## 2      3   0     6 0.0450    1
## 4      3   1     6 0.0500    2
## 6      2   3     6 0.3645    3
## 7      3   0     6 0.0405    3
## 9      3   1     6 0.0450    4
## 11     3   2     6 0.0500    5
## 
## 
## [[8]]
## [[8]]$timestep
## [1] 7
## 
## [[8]]$proxel
##    state tau tau_k    prob prev
## 1      2   3     7 0.36450    1
## 2      3   0     7 0.04050    1
## 4      3   1     7 0.04500    2
## 6      3   2     7 0.05000    3
## 8      2   4     7 0.32805    4
## 9      3   0     7 0.03645    4
## 11     3   1     7 0.04050    5
## 13     3   2     7 0.04500    6
## 15     3   3     7 0.05000    7
## 
## 
## [[9]]
## [[9]]$timestep
## [1] 8
## 
## [[9]]$proxel
##    state tau tau_k        prob prev
## 1      2   4     8 0.275978571    1
## 2      3   0     8 0.036450000    1
## 3      4   0     0 0.052071429    1
## 4      3   1     8 0.034714286    2
## 5      4   0     0 0.005785714    2
## 6      3   2     8 0.038571429    3
## 7      4   0     0 0.006428571    3
## 8      3   3     8 0.042857143    4
## 9      4   0     0 0.007142857    4
## 10     2   5     8 0.248380714    5
## 11     3   0     8 0.032805000    5
## 12     4   0     0 0.046864286    5
## 13     3   1     8 0.031242857    6
## 14     4   0     0 0.005207143    6
## 15     3   2     8 0.034714286    7
## 16     4   0     0 0.005785714    7
## 17     3   3     8 0.038571429    8
## 18     4   0     0 0.006428571    8
## 19     3   4     8 0.042857143    9
## 20     4   0     0 0.007142857    9
## 
## 
## [[10]]
## [[10]]$timestep
## [1] 9
## 
## [[10]]$proxel
##    state tau tau_k        prob prev
## 1      2   5     9 0.202384286    1
## 2      3   0     9 0.027597857    1
## 3      4   0     0 0.045996429    1
## 4      3   1     9 0.030375000    2
## 5      4   0     0 0.006075000    2
## 7      3   2     9 0.028928571    4
## 8      4   0     0 0.005785714    4
## 10     3   3     9 0.032142857    6
## 11     4   0     0 0.006428571    6
## 13     3   4     9 0.035714286    8
## 14     4   0     0 0.007142857    8
## 16     2   6     9 0.182145857   10
## 17     3   0     9 0.024838071   10
## 18     4   0     0 0.041396786   10
## 19     3   1     9 0.027337500   11
## 20     4   0     0 0.005467500   11
## 22     3   2     9 0.026035714   13
## 23     4   0     0 0.005207143   13
## 25     3   3     9 0.028928571   15
## 26     4   0     0 0.005785714   15
## 28     3   4     9 0.032142857   17
## 29     4   0     0 0.006428571   17
## 31     3   5     9 0.035714286   19
## 32     4   0     0 0.007142857   19
## 
## 
## [[11]]
## [[11]]$timestep
## [1] 10
## 
## [[11]]$proxel
##    state tau tau_k        prob prev
## 1      2   6    10 0.141669000    1
## 2      3   0    10 0.020238429    1
## 3      4   0     0 0.040476857    1
## 4      3   1    10 0.022078286    2
## 5      4   0     0 0.005519571    2
## 7      3   2    10 0.024300000    4
## 8      4   0     0 0.006075000    4
## 10     3   3    10 0.023142857    6
## 11     4   0     0 0.005785714    6
## 13     3   4    10 0.025714286    8
## 14     4   0     0 0.006428571    8
## 16     3   5    10 0.028571429   10
## 17     4   0     0 0.007142857   10
## 19     2   7    10 0.127502100   12
## 20     3   0    10 0.018214586   12
## 21     4   0     0 0.036429171   12
## 22     3   1    10 0.019870457   13
## 23     4   0     0 0.004967614   13
## 25     3   2    10 0.021870000   15
## 26     4   0     0 0.005467500   15
## 28     3   3    10 0.020828571   17
## 29     4   0     0 0.005207143   17
## 31     3   4    10 0.023142857   19
## 32     4   0     0 0.005785714   19
## 34     3   5    10 0.025714286   21
## 35     4   0     0 0.006428571   21
## 37     3   6    10 0.028571429   23
## 38     4   0     0 0.007142857   23
## 
## 
## [[12]]
## [[12]]$timestep
## [1] 11
## 
## [[12]]$proxel
##    state tau tau_k        prob prev
## 1      2   7    11 0.092084850    1
## 2      3   0    11 0.014166900    1
## 3      4   0     0 0.035417250    1
## 4      3   1    11 0.015178821    2
## 5      4   0     0 0.005059607    2
## 7      3   2    11 0.016558714    4
## 8      4   0     0 0.005519571    4
## 10     3   3    11 0.018225000    6
## 11     4   0     0 0.006075000    6
## 13     3   4    11 0.017357143    8
## 14     4   0     0 0.005785714    8
## 16     3   5    11 0.019285714   10
## 17     4   0     0 0.006428571   10
## 19     3   6    11 0.021428571   12
## 20     4   0     0 0.007142857   12
## 22     2   8    11 0.082876365   14
## 23     3   0    11 0.012750210   14
## 24     4   0     0 0.031875525   14
## 25     3   1    11 0.013660939   15
## 26     4   0     0 0.004553646   15
## 28     3   2    11 0.014902843   17
## 29     4   0     0 0.004967614   17
## 31     3   3    11 0.016402500   19
## 32     4   0     0 0.005467500   19
## 34     3   4    11 0.015621429   21
## 35     4   0     0 0.005207143   21
## 37     3   5    11 0.017357143   23
## 38     4   0     0 0.005785714   23
## 40     3   6    11 0.019285714   25
## 41     4   0     0 0.006428571   25
## 43     3   7    11 0.021428571   27
## 44     4   0     0 0.007142857   27
## 
## 
## [[13]]
## [[13]]$timestep
## [1] 12
## 
## [[13]]$proxel
##    state tau tau_k        prob prev
## 1      2   8    12 0.052181415    1
## 2      3   0    12 0.009208485    1
## 3      4   0     0 0.030694950    1
## 4      3   1    12 0.009444600    2
## 5      4   0     0 0.004722300    2
## 7      3   2    12 0.010119214    4
## 8      4   0     0 0.005059607    4
## 10     3   3    12 0.011039143    6
## 11     4   0     0 0.005519571    6
## 13     3   4    12 0.012150000    8
## 14     4   0     0 0.006075000    8
## 16     3   5    12 0.011571429   10
## 17     4   0     0 0.005785714   10
## 19     3   6    12 0.012857143   12
## 20     4   0     0 0.006428571   12
## 22     3   7    12 0.014285714   14
## 23     4   0     0 0.007142857   14
## 25     2   9    12 0.046963274   16
## 26     3   0    12 0.008287637   16
## 27     4   0     0 0.027625455   16
## 28     3   1    12 0.008500140   17
## 29     4   0     0 0.004250070   17
## 31     3   2    12 0.009107293   19
## 32     4   0     0 0.004553646   19
## 34     3   3    12 0.009935229   21
## 35     4   0     0 0.004967614   21
## 37     3   4    12 0.010935000   23
## 38     4   0     0 0.005467500   23
## 40     3   5    12 0.010414286   25
## 41     4   0     0 0.005207143   25
## 43     3   6    12 0.011571429   27
## 44     4   0     0 0.005785714   27
## 46     3   7    12 0.012857143   29
## 47     4   0     0 0.006428571   29
## 49     3   8    12 0.014285714   31
## 50     4   0     0 0.007142857   31
## 
## 
## [[14]]
## [[14]]$timestep
## [1] 13
## 
## [[14]]$proxel
##    state tau tau_k        prob prev
## 1      2   9    13 0.020872566    1
## 2      3   0    13 0.005218142    1
## 3      4   0     0 0.026090708    1
## 4      3   1    13 0.004604243    2
## 5      4   0     0 0.004604243    2
## 7      3   2    13 0.004722300    4
## 8      4   0     0 0.004722300    4
## 10     3   3    13 0.005059607    6
## 11     4   0     0 0.005059607    6
## 13     3   4    13 0.005519571    8
## 14     4   0     0 0.005519571    8
## 16     3   5    13 0.006075000   10
## 17     4   0     0 0.006075000   10
## 19     3   6    13 0.005785714   12
## 20     4   0     0 0.005785714   12
## 22     3   7    13 0.006428571   14
## 23     4   0     0 0.006428571   14
## 25     3   8    13 0.007142857   16
## 26     4   0     0 0.007142857   16
## 28     2  10    13 0.018785309   18
## 29     3   0    13 0.004696327   18
## 30     4   0     0 0.023481637   18
## 31     3   1    13 0.004143818   19
## 32     4   0     0 0.004143818   19
## 34     3   2    13 0.004250070   21
## 35     4   0     0 0.004250070   21
## 37     3   3    13 0.004553646   23
## 38     4   0     0 0.004553646   23
## 40     3   4    13 0.004967614   25
## 41     4   0     0 0.004967614   25
## 43     3   5    13 0.005467500   27
## 44     4   0     0 0.005467500   27
## 46     3   6    13 0.005207143   29
## 47     4   0     0 0.005207143   29
## 49     3   7    13 0.005785714   31
## 50     4   0     0 0.005785714   31
## 52     3   8    13 0.006428571   33
## 53     4   0     0 0.006428571   33
## 55     3   9    13 0.007142857   35
## 56     4   0     0 0.007142857   35
## 
## 
## [[15]]
## [[15]]$timestep
## [1] 14
## 
## [[15]]$proxel
##    state tau tau_k        prob prev
## 2      3   0    14 0.002087257    1
## 3      4   0     0 0.020872566    1
## 5      4   0     0 0.005218142    2
## 8      4   0     0 0.004604243    4
## 11     4   0     0 0.004722300    6
## 14     4   0     0 0.005059607    8
## 17     4   0     0 0.005519571   10
## 20     4   0     0 0.006075000   12
## 23     4   0     0 0.005785714   14
## 26     4   0     0 0.006428571   16
## 29     4   0     0 0.007142857   18
## 32     3   0    14 0.001878531   20
## 33     4   0     0 0.018785309   20
## 35     4   0     0 0.004696327   21
## 38     4   0     0 0.004143818   23
## 41     4   0     0 0.004250070   25
## 44     4   0     0 0.004553646   27
## 47     4   0     0 0.004967614   29
## 50     4   0     0 0.005467500   31
## 53     4   0     0 0.005207143   33
## 56     4   0     0 0.005785714   35
## 59     4   0     0 0.006428571   37
## 62     4   0     0 0.007142857   39
## 
## 
## [[16]]
## [[16]]$timestep
## [1] 15
## 
## [[16]]$proxel
##    state tau tau_k        prob prev
## 1      3   1    15 0.002087257    1
## 13     3   1    15 0.001878531   12

What is the probability that the patient is still sick after 8 days for different discrete time steps (e.g. 2, 1, 0.5, 0.25, 0.1)?

What is the probability of measuring fever on the 9th day for different discrete time steps (e.g. 2, 1, 0.5, 0.25, 0.1)?

## [1] "Wkt = 0.608"
## [1] "Wkt = 0.5284065"
## [1] "Wkt = 0.52882555229399"
## [1] "Wkt = 0.52913153254786"
## [1] "Wkt = 0.536988812479435"

What is the expected duration until healing with probability 99% for different discrete time steps (e.g. 2, 1, 0.5, 0.25, 0.1)?

##   state tau tau_k    prob prev
## 2     3   0    14 0.01792    1
## [1] "Für 2 An Tag: 12"
##    state tau tau_k        prob prev
## 2      3   0    14 0.002087257    1
## 32     3   0    14 0.001878531   20
## [1] "Für 1 An Tag: 14"
##     state tau tau_k         prob prev
## 2       3   0    14 0.0002843604    1
## 62      3   0    14 0.0002701424   40
## 125     3   0    14 0.0002566352   81
## 191     3   0    14 0.0002438035  124
## [1] "Für .5 An Tag: 14"
##     state tau tau_k         prob prev
## 2       3   0    14 3.761015e-05    1
## 122     3   0    14 3.666990e-05   80
## 245     3   0    14 3.575315e-05  161
## 371     3   0    14 3.485932e-05  244
## 500     3   0    14 3.398784e-05  329
## 632     3   0    14 3.313814e-05  416
## 767     3   0    14 3.230969e-05  505
## 905     3   0    14 3.150194e-05  596
## [1] "Für .25 An Tag: 14"
---
title: "ADM Task 4"
author: "Dominik Diedrich"
date: "`r Sys.Date()`"
output:
  html_document:
    code_download: yes
---

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(petrinetR)
library(markovchain)
```

```{r eval=FALSE, include=FALSE}
# Special Purpose Proxel-Based Solver

MINPROB <- 1.0e-12
S1 <- 0
S2 <- 2
S3 <- 4
H <-  6
DELTA <- 1
ENDTIME <- 50
PI <- 3.1415926

# Definition of a proxel structure
proxel <- function(id, s, tau1k, tau2k, val, left = NULL, right = NULL) {
  list(id = id, s = s, tau1k = tau1k, tau2k = tau2k, val = val, left = left, right = right)
}

y <- vector("list", 4)
tmax <- ENDTIME
TAUMAX <- tmax / DELTA
totcnt <- 0
maxccp <- 0
ccpcnt <- 0
root <- vector("list", 2)
firstfree <- NULL
eerror <- 0
sw <- 1
len <- 0
dt <- DELTA

#################################### distributions ###############
# uniform IRF
unihrf <- function(x, a, b) {
  if (x >= a && x < b) {
    y <- 1.0 / (b - x)
  } else {
    y <- 0.0
  }

  return(y)
}

# exponential IRF
exphrf <- function(x, l) {
  return(l)
}

#################################### output functions ##############
# Print all proxels in tree
printtree <- function(p) {
  if (is.null(p))
    return
  cat("s", p$s, "t1", p$tau1k, "t2", p$tau2k, "val", p$val, "\n")
  printtree(p$left)
  printtree(p$right)
}

# Print out complete solution
plotsolution <- function(kmax) {
  cat("\n\n")
  for (k in 1:(kmax + 1))
    cat(format(k * dt, digits = 7), "\t", format(y[[1]][k], scientific = TRUE, digits = 7), "\t", format(y[[3]][k], scientific = TRUE, digits = 7), "\n")
}

# Print out a proxel
printproxel <- function(c) {
  cat("processing", c$s, c$tau1k, c$val, "\n")
}

###################################### proxel manipulation function #########
# Compute unique id from proxel state
state2id <- function(s, t1k, t2k) {
  TAUMAX * (TAUMAX * s + t1k) + t2k
}

# Compute size of tree
size <- function(p) {
  if (is.null(p))
    return(0)
  sl <- size(p$left)
  sr <- size(p$right)
  sl + sr + 1
}

# Get a proxel from the tree
getproxel <- function() {
  temp <- root[[1 - sw]]
  old <- temp

  # Move down the tree to a leaf
  while (!is.null(temp)) {
    # Go right
    if (!is.null(temp$right) && is.null(temp$left)) {
      old <- temp
      temp <- temp$right
    }
    # Go left
    else if (is.null(temp$right) && !is.null(temp$left)) {
      old <- temp
      temp <- temp$left
    }
    # Choose right/left at random
    else if (!is.null(temp$right) && !is.null(temp$left)) {
      if (runif(1) > 0.5) {
        old <- temp
        temp <- temp$left
      } else {
        old <- temp
        temp <- temp$right
      }
    } else
      break
  }

  if (identical(temp, root[[1 - sw]]))
    root[[1 - sw]] <- NULL
  else {
    if (!is.null(temp$right))
      old$right <- NULL
    else
      old$left <- NULL
  }

  old <- firstfree
  firstfree <<- temp
  temp$right <- old
  ccpcnt <<- ccpcnt - 1

  return(temp)
}

# Get a fresh proxel and copy data into it
insertproxel <- function(s, tau1k, tau2k, val) {
  temp <- proxel(state2id(s, tau1k, tau2k), s, tau1k, tau2k, val)
  ccpcnt <<- ccpcnt + 1

  if (maxccp < ccpcnt)
    maxccp <<- ccpcnt

  return(temp)
}

# Adds a new proxel to the tree
addproxel <- function(s, tau1k, tau2k, val) {
  cont <- 1

  # Alarm! TAUMAX overstepped!
  if (tau1k >= TAUMAX) {
    tau1k <<- TAUMAX - 1
  }

  # New tree, add root
  if (is.null(root[[sw]])) {
    root[[sw]] <<- insertproxel(s, tau1k, tau2k, val)
    root[[sw]]$left <<- NULL
    root[[sw]]$right <<- NULL
    return()
  }

  # Compute id of new proxel
  id <- state2id(s, tau1k, tau2k)

  # Locate insertion point in tree
  temp <- root[[sw]]
  while (cont == 1) {
    if (!is.null(temp$left) && id < temp$id)
      temp <- temp$left
    else if (!is.null(temp$right) && id > temp$id)
      temp <- temp$right
    else
      cont <- 0
  }

  # Insert left leaf into tree
  if (is.null(temp$left) && id < temp$id) {
    temp2 <- insertproxel(s, tau1k, tau2k, val)
    temp$left <<- temp2
    temp2$left <<- NULL
    temp2$right <<- NULL
    return()
  }

  # Insert right leaf into tree
  if (is.null(temp$right) && id > temp$id) {
    temp2 <- insertproxel(s, tau1k, tau2k, val)
    temp$right <<- temp2
    temp2$left <<- NULL
    temp2$right <<- NULL
    return()
  }

  # Proxels have the same id, just add their vals
  if (id == temp$id) {
    temp$val <<- temp$val + val
    return()
  }

  cat("\n\n\n!!!!!! addproxel failed !!!!!\n\n\n")
}

##################################### model specific dists ####################
# Instantaneous rate function 1
S1_to_S2 <- function(age) {
  return(unihrf(age, 2, 4))
}

# Instantaneous rate function 2
S2_to_S3 <- function(age) {
  return(exphrf(age, 0.1))
}

# Instantaneous rate function 3
heal <- function(age) {
  return(unihrf(age, 7, 14))
}

################################### main loop ########################
main <- function() {
  kmax <- as.integer(ENDTIME / DELTA) + 1

  # Initialize the simulation
  root[[1]] <<- NULL
  root[[2]] <<- NULL
  eerror <<- 0.0
  totcnt <<- 0
  maxccp <<- 0
  ccpcnt <<- 0

  for (k in 1:4) {
    y[[k]] <- rep(0.0, kmax + 2)
  }

  TAUMAX <<- as.integer(ENDTIME / DELTA) + 1

  # Set initial proxel
  addproxel(S1, 0, 0, 1.0)

  # First loop: iteration over all time steps
  for (k in 2:(kmax + 1)) {

    # Print progress information
    if (k %% 100 == 0) {
      cat("\nSTEP", k, "\n")
      cat("Size of tree", size(root[[sw]]), "\n")
    }

    sw <<- 1 - sw

    # Second loop: iterating over all proxels of a time step
    while (!is.null(root[[1 - sw]])) {
      totcnt <<- totcnt + 1
      currproxel <- getproxel()
      print(y)
      while (currproxel$val < MINPROB && !is.null(root[[1 - sw]])) {
        val <- currproxel$val
        eerror <<- eerror + val
        currproxel <- getproxel()
      }

      val <- currproxel$val
      tau1k <- currproxel$tau1k
      tau2k <- currproxel$tau2k
      s <- currproxel$s
      y[[s]][k - 1] <<- y[[s]][k - 1] + val

      # Create child proxels
      switch(s,
             S1 = {
               z <- dt * S1_to_S2(tau1k * dt)
               z2 <- dt * heal(tau2k * dt)
               if (z < 1.0 & z2 < 1.0) {
                 addproxel(S2, 0, tau2k + 1, val * z)
                 addproxel(H , 0, tau2k + 1, val * z2)
                 addproxel(S1, tau1k + 1, tau2k + 1, val * (1 - z - z2))
               } 
               if (z==1.0) {
                 addproxel(S2, 0, tau2k + 1, val)
                 } else
                   addproxel(H, 0, 0, val)
             },
             S2 = {
               z <- dt * S2_to_S3(tau1k * dt)
               z2 <- dt * heal(tau2k * dt)
               if (z < 1.0 & z2 < 1.0) {
                 addproxel(S3, 0, tau2k + 1, val * z)
                 addproxel(H , 0, tau2k + 1, val * z2)
                 addproxel(S2, tau1k + 1, tau2k + 1, val * (1 - z - z2))
               } 
               if (z==1.0) {
                 addproxel(S3, 0, tau2k + 1, val)
                 } else
                   addproxel(H, 0, 0, val)
             },
             S3 = {
               z <- dt * heal(tau2k * dt)
               if (z < 1.0) {
                 addproxel(H, 0, tau2k + 1, val * z)
                 addproxel(S3, tau1k + 1, tau2k + 1, val * (1 - z))
               } else
                 addproxel(H, 0, 0, val)
             },
             H = {
               addproxel(H, tau1k + 1, 0, 1)
             })
    }
  }

  cat("error =", eerror, "\n")
  cat("ccpx =", maxccp, "\n")
  cat("count =", totcnt, "\n")
  plotsolution(kmax)
}
main()
```


```{r eval=FALSE, include=FALSE}
MINPROB <- 1.0e-12
STAGE1 <- 1
STAGE2 <- 2
STAGE3 <- 3
HEALTHY <- 4
DELTA <- c(2, 1, 0.5, 0.25, 0.1)
ENDTIME <- 13
PI <- 3.1415926

setClass("Proxel",representation = list(id = "numeric", s = "numeric", tau1k = "numeric", tau2k = "numeric", val = "numeric", left = "Proxel", right = "Proxel"
  ))
Proxel <- function() {
  list(
    id = 0,
    s = 0,
    tau1k = 0,
    tau2k = 0,
    val = 0.0,
    left = NULL,
    right = NULL
  )
}

y <- list(NULL, NULL, NULL, NULL)
tmax <- 0.0
TAUMAX <- 0
totcnt <- 0
maxccp <- 0
ccpcnt <- 0
root <- list(NULL, NULL)
firstfree <- NULL
eerror <- 0
sw <- 0
len <- 0
dt <- 0

unihrf <- function(x, a, b) {
  if (a <= x && x < b) {
    return (1.0 / (b - x))
  } else {
    return (0)
  }
}

exphrf <- function(x, l) {
  return (l)
}

printtree <- function(p) {
  if (is.null(p)) {
    return
  }
  cat("s", p$s, "t1", p$tau1k, "t2", p$tau2k, "val", p$val, "\n")
  printtree(p$left)
  printtree(p$right)
}

plotsolution <- function(kmax) {
  cat("\n\n")
  for (k in 1:(kmax + 1)) {
    cat(sprintf("%.5f\t%.5e\t%.5e\t%.5e\t%.5e\n", k * dt, y[[1]][k], y[[2]][k], y[[3]][k], y[[4]][k]))
  }
}

printproxel <- function(c) {
  cat("processing", c$s, c$tau1k, c$val, "\n")
}

state2id <- function(s, t1k, t2k) {
  return (TAUMAX * (TAUMAX * s + t1k) + t2k)
}

size <- function(p) {
  if (is.null(p)) {
    return (0)
  }
  sl <- size(p$left)
  sr <- size(p$right)
  return (sl + sr + 1)
}

getproxel <- function() {
  LEFT <- 0
  RIGHT <- 1
  dir <- 1
  cont <- 1

  if (is.null(root[[1 - sw+1]])) {
    return(NULL)
  }

  temp <- root[[1 - sw+1]]
  old <- temp

  while (cont == 1) {
    if (!is.null(temp$right) && is.null(temp$left)) {
      old <- temp
      temp <- temp$right
      dir <- RIGHT
    } else if (is.null(temp$right) && !is.null(temp$left)) {
      old <- temp
      temp <- temp$left
      dir <- LEFT
    } else if (!is.null(temp$right) && !is.null(temp$left)) {
      if (runif(1) > 0.5) {
        old <- temp
        temp <- temp$left
        dir <- LEFT
      } else {
        old <- temp
        temp <- temp$right
        dir <- RIGHT
      }
    } else {
      cont <- 0
    }
  }

  if (identical(temp, root[[1 - sw+1]])) {
    root[[1 - sw+1]] <- NULL
  } else {
    if (dir == RIGHT) {
      old$right <- NULL
    } else {
      old$left <- NULL
    }
  }

  old <- firstfree
  firstfree <- temp
  temp$right <- old
  ccpcnt <- ccpcnt - 1
  return(temp)
}

insertproxel <- function(s, tau1k, tau2k, val) {
  if (is.null(firstfree)) {
    temp <- Proxel()
  } else {
    temp <- firstfree
    firstfree <- firstfree$right
  }

  temp$id <- state2id(s, tau1k, tau2k)
  temp$s <- s
  temp$tau1k <- tau1k
  temp$tau2k <- tau2k
  temp$val <- val
  ccpcnt <- ccpcnt + 1

  if (maxccp < ccpcnt) {
    maxccp <- ccpcnt
  }

  return(temp)
}

addproxel <- function(s, tau1k, tau2k, val) {
  temp <- NULL
  temp2 <- NULL
  cont <- 1
  id <- state2id(s, tau1k, tau2k)

  if (tau1k >= TAUMAX) {
    tau1k <- TAUMAX - 1
  }
  if (is.null(root[[sw+1]])) {
    root[[sw+1]] <- insertproxel(s, tau1k, tau2k, val)
    root[[sw+1]]$left <- NULL
    root[[sw+1]]$right <- NULL
    return(NULL)
  }

  temp <- root[[sw+1]]

  while (cont == 1) {
    if (!is.null(temp$left) && id < temp$id) {
      temp <- temp$left
    } else if (!is.null(temp$right) && id > temp$id) {
      temp <- temp$right
    } else {
      cont <- 0
    }
  }

  if (is.null(temp$left) && id < temp$id) {
    temp2 <- insertproxel(s, tau1k, tau2k, val)
    temp$left <- temp2
    temp2$left <- NULL
    temp2$right <- NULL
  } else if (is.null(temp$right) && id > temp$id) {
    temp2 <- insertproxel(s, tau1k, tau2k, val)
    temp$right <- temp2
    temp2$left <- NULL
    temp2$right <- NULL
  } else if (id == temp$id) {
    temp$val <- temp$val + val
  } else {
    cat("\n\n\n!!!!!! addproxel failed !!!!!\n\n\n")
  }
}

incubation <- function(age) {
  return (unihrf(age, 2, 4))
}

get_worse <- function(age) {
  return (exphrf(age, 1/10))
}

heal <- function(age) {
  return (unihrf(age, 7, 14))
}

main <- function() {
  root <- list(NULL, NULL)
  eerror <- 0.0
  totcnt <- 0
  maxccp <- 0
  tmax <- ENDTIME
  dt <- DELTA[2]
  kmax <- as.integer(tmax / dt) + 1

  for (k in 1:4) {
    y[[k]] <- rep(0.0, kmax + 2)
  }

  TAUMAX <- as.integer(tmax / dt) + 1

  addproxel(STAGE1, 0, 0, 1.0)

  for (k in 2:(kmax + 2)) {
    if (k %% 100 == 0) {
      cat("\nSTEP", k, "\n")
      cat("Size of tree", size(root[[sw+1]]), "\n")
    }

    while (!is.null(root[[1 - sw+1]])) {
      totcnt <- totcnt + 1
      currproxel <- getproxel()

      while (currproxel$val < MINPROB && !is.null(root[[1 - sw+1]])) {
        val <- currproxel$val
        eerror <- eerror + val
        currproxel <- getproxel()
      }

      val <- currproxel$val
      tau1k <- currproxel$tau1k
      tau2k <- currproxel$tau2k
      s <- currproxel$s
      y[[s]][k - 1] <- y[[s]][k - 1] + val

      if (s == STAGE1) {
        z <- dt * incubation(tau1k * dt)
        z2 <- dt * heal(tau2k * dt)
        if (z2 < 1.0) {
          if (z < 1.0) {
            addproxel(STAGE2, 0, tau2k + 1, val * z)
            addproxel(STAGE1, tau1k + 1, tau2k + 1, val * (1 - z - z2))
            addproxel(HEALTHY, 0, 0, val * (z2))
          } else {
            addproxel(STAGE2, 0, tau2k + 1, val)
          }
        } else {
          addproxel(HEALTHY, 0, 0, val)
        }
      } else if (s == STAGE2) {
        z <- dt * get_worse(tau1k * dt)
        z2 <- dt * heal(tau2k * dt)
        if (z2 < 1.0) {
          if (z < 1.0) {
            addproxel(STAGE3, 0, tau2k + 1, val * z)
            addproxel(STAGE2, tau1k + 1, tau2k + 1, val * (1 - z - z2))
            addproxel(HEALTHY, 0, 0, val * (z2))
          } else {
            addproxel(STAGE3, 0, tau2k + 1, val)
          }
        } else {
          addproxel(HEALTHY, 0, 0, val)
        }
      } else if (s == STAGE3) {
        z <- dt * heal(tau2k * dt)
        if (z < 1.0) {
          addproxel(HEALTHY, 0, 0, val * z)
          addproxel(STAGE3, tau1k + 1, tau2k + 1, val * (1 - z))
        } else {
          addproxel(HEALTHY, 0, 0, val)
        }
      } else if (s == HEALTHY) {
        addproxel(HEALTHY, tau1k + 1, tau2k + 1, 1)
      }
    }
    if (sw==0) sw=1
    if (sw==1) sw=0
  }

  cat("\n\n")
  cat("error =", sprintf("%.5e", eerror), "\n")
  cat("ccpx =", maxccp, "\n")
  cat("count =", totcnt, "\n")
  plotsolution(kmax)
}

main()
```


```{r eval=FALSE, include=FALSE}

max.ts = 14
delta = 1
startproxel=c(1,0,1)
RG=matrix(c(1,1,0,1,
            0,1,1,1,
            0,0,1,1,
            0,0,0,1),ncol = 4,byrow = T)

Proxel_algo=function(RG,startproxel,delta,max.ts,Print=T){
  
counter_prob=0

unihrf <- function(age, a, b) {
  if (a <= age && age < b) {
    return (1.0 / (b - age))
  } else {
    return (0)
  }
}

exphrf <- function(age, l) {
  return (l)
}
  
proxel_list=list()
proxel_list[[max.ts+1]]=list()
proxel_list[[1]]=list(STEP=c(0),proxel=data.frame(state=startproxel[1],tau=startproxel[2],prob=startproxel[3]))
if(Print) print("initial proxel:")
if(Print) print(proxel_list[[1]])

for (timestep in 1:max.ts) {
  proxel_list[[timestep+1]]=list(timestep=c(timestep),proxel=data.frame(state=numeric(),tau=numeric(),prob=numeric()))     # erstelle schonmal eine neue leere liste für den nächsten timestep
  
  for (l in 1:nrow(proxel_list[[timestep]]$proxel)) {
    currprox = proxel_list[[timestep]]$proxel[l,]                                     # aktueller proxel zu dem folgeproxels erzeugt werden sollen
    folger = RG[as.numeric(currprox[1]),]                                             # folgestates von aktuellem proxel
    for (i in 1:4) {
      if (folger[i]==1) {                                                             # erstelle folgeproxel falls pfeil dahin gibt
        proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel)+1,1] = i                       # state vom nächsten proxel
        
        if(currprox$state==i) proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),2] = currprox$tau + delta
          else proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),2] = 0                    # tau vom nächsten proxel
          
        if(currprox$state==1 & i==2) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),3] = unihrf(currprox$tau,2,4) * delta
          counter_prob = counter_prob + unihrf(currprox$tau,2,4) * delta}                 # transition von S1 zu S2
        if(currprox$state==2 & i==3) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),3] = exphrf(currprox$tau,0.1) * delta
          counter_prob = counter_prob + exphrf(currprox$tau,0.1) * delta}                 # transition von S2 zu S3
        if(currprox$state!=4 & i==4) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),3] = unihrf(currprox$tau,7,14)* delta 
          counter_prob = counter_prob + unihrf(currprox$tau,7,14)* delta}                 # transtion zu heal von S1,S2 oder S3
      }
    } 
    proxel_list[[timestep+1]]$proxel[which(is.na(proxel_list[[timestep+1]]$proxel[,3])),3] = 1-counter_prob  # rest wkt im state zu bleiben
    counter_prob=0
  }
  
  proxel_list[[timestep+1]]$proxel=proxel_list[[timestep+1]]$proxel[-which(proxel_list[[timestep+1]]$proxel[,3]==0),]
  
  if(Print) print(paste("Time step:",timestep))
  if(Print) print(proxel_list[[timestep+1]])
}

return(proxel_list)
}
Proxel_algo(RG,startproxel,delta,max.ts,T)
```

```{r}
max.ts = 15
delta = 1
startproxel=c(1,0,0,1,0)
RG=matrix(c(1,1,0,1,
            0,1,1,1,
            0,0,1,1,
            0,0,0,1),ncol = 4,byrow = T)

Proxel_algo=function(RG,startproxel,delta,max.ts,Print=T){
  
counter_prob=0

unihrf <- function(age, a, b) {
  if (a <= age && age < b) {
    return (1.0 / (b - age))
  } else {
    return (0)
  }
}

exphrf <- function(age, l) {
  return (l)
}
  
proxel_list=list()
proxel_list[[max.ts+1]]=list()
proxel_list[[1]]=list(STEP=c(0),proxel=data.frame(state=startproxel[1],tau=startproxel[2],tau_k=startproxel[3],prob=startproxel[4],prev=startproxel[5]))
if(Print) print("initial proxel:")
if(Print) print(proxel_list[[1]])

for (timestep in 1:max.ts) {
  proxel_list[[timestep+1]]=list(timestep=c(timestep),proxel=data.frame(state=numeric(),tau=numeric(),tau_k=numeric(),prob=numeric(),prev=numeric()))     # erstelle schonmal eine neue leere liste für den nächsten timestep
  
  for (l in 1:nrow(proxel_list[[timestep]]$proxel)) {
    currprox = proxel_list[[timestep]]$proxel[l,]                                     # aktueller proxel zu dem folgeproxels erzeugt werden sollen
    folger = RG[as.numeric(currprox[1]),]                                             # folgestates von aktuellem proxel
    
    for (i in 1:4) {
      if (folger[i]==1) {                                                             # erstelle folgeproxel falls pfeil dahin gibt
        proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel)+1,1] = i                       # state vom nächsten proxel
        
        if(currprox$state==i) proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),2] = currprox$tau + delta
          else proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),2] = 0                    # tau vom nächsten proxel
        
        if(currprox$state %in% c(1,2,3) & i!=4) proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),3] = currprox$tau_k + delta
          else if(currprox$state==4 & i==4) proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),3] = currprox$tau_k - delta
            else proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),3] = 0                   # tau_k vom nächsten proxel
          
        if(currprox$state==1 & i==2) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),4] = (unihrf(currprox$tau_k,2,4) * delta) * currprox$prob
          counter_prob = counter_prob + unihrf(currprox$tau_k,2,4) * delta}                 # transition von S1 zu S2
        if(currprox$state==2 & i==3) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),4] = (exphrf(currprox$tau_k,0.1) * delta) * currprox$prob
          counter_prob = counter_prob + exphrf(currprox$tau_k,0.1) * delta}                 # transition von S2 zu S3
        if(currprox$state!=4 & i==4) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),4] = (unihrf(currprox$tau_k,7,14)* delta) * currprox$prob
          counter_prob = counter_prob + unihrf(currprox$tau_k,7,14)* delta}                 # transtion zu heal von S1,S2 oder S3
        if(currprox$state==1 & i==1) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),4] = (1 - (unihrf(currprox$tau_k,2,4) * delta) - (unihrf(currprox$tau_k,7,14)* delta)) * currprox$prob}
        if(currprox$state==2 & i==2) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),4] = (1 - (exphrf(currprox$tau_k,0.1) * delta) - (unihrf(currprox$tau_k,7,14)* delta)) * currprox$prob}
        if(currprox$state==3 & i==3) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),4] = (1 - (unihrf(currprox$tau_k,7,14)* delta)) * currprox$prob}
        if(currprox$state==4 & i==4) {
          proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),4] = 1 }
            
         proxel_list[[timestep+1]]$proxel[nrow(proxel_list[[timestep+1]]$proxel),5] = l   
            
      }
    } 
    
    #proxel_list[[timestep+1]]$proxel[which(is.na(proxel_list[[timestep+1]]$proxel[,4])),4] = 1-counter_prob #* proxel_list[[timestep]]$proxel$prob[l] # rest wkt im state zu bleiben
    counter_prob=0
  }
  if(length(which(proxel_list[[timestep+1]]$proxel[,4]<=0))!=0) proxel_list[[timestep+1]]$proxel=proxel_list[[timestep+1]]$proxel[-which(proxel_list[[timestep+1]]$proxel[,4]<=0),]
  if(length(which(proxel_list[[timestep+1]]$proxel[,3]< 0))!=0) proxel_list[[timestep+1]]$proxel=proxel_list[[timestep+1]]$proxel[-which(proxel_list[[timestep+1]]$proxel[,3]< 0),]
  
  if(Print) print(paste("Time step:",timestep))
  if(Print) print(proxel_list[[timestep+1]])
}

return(proxel_list)
}
proxel_list=Proxel_algo(RG,startproxel,delta,max.ts,T)
proxel_list

```


What is the probability that the patient is still sick after 8 days for different discrete time steps (e.g. 2, 1, 0.5, 0.25, 0.1)?

```{r include=FALSE}
P2=Proxel_algo(RG,startproxel,2,8,T)
P1=Proxel_algo(RG,startproxel,1,15,T)
P05=Proxel_algo(RG,startproxel,0.5,29,T)
P025=Proxel_algo(RG,startproxel,0.25,57,T)
P01=Proxel_algo(RG,startproxel,0.1,82,T)
```


```{r include=FALSE}
P2[[5]]   
P1[[9]]   
P05[[17]] 
P025[[33]]
P01[[81]] 

sum(P2[[5]]$proxel$prob[P2[[5]]$proxel$state != 4])
sum(P1[[9]]$proxel$prob[P1[[9]]$proxel$state != 4])
sum(P05[[17]]$proxel$prob[P05[[17]]$proxel$state != 4])
sum(P025[[33]]$proxel$prob[P025[[33]]$proxel$state != 4])
sum(P01[[81]]$proxel$prob[P01[[81]]$proxel$state != 4])
```


What is the probability of measuring fever on the 9th day for different discrete time steps (e.g. 2, 1, 0.5, 0.25, 0.1)? 


```{r echo=FALSE}

paste("Wkt =",sum(aggregate.data.frame(P2[[5]]$proxel$prob,by=list(P2[[5]]$proxel$state),FUN = sum)$x *c(0.5,0.8)))
paste("Wkt =",sum(aggregate.data.frame(P1[[9]]$proxel$prob,by=list(P1[[9]]$proxel$state),FUN = sum)$x *c(0.5,0.8,0)))
paste("Wkt =",sum(aggregate.data.frame(P05[[17]]$proxel$prob,by=list(P05[[17]]$proxel$state),FUN = sum)$x *c(0.5,0.8,0)))
paste("Wkt =",sum(aggregate.data.frame(P025[[33]]$proxel$prob,by=list(P025[[33]]$proxel$state),FUN = sum)$x *c(0.5,0.8,0)))
paste("Wkt =",sum(aggregate.data.frame(P01[[81]]$proxel$prob,by=list(P01[[81]]$proxel$state),FUN = sum)$x *c(0.5,0.8,0)))
```



What is the expected duration until healing with probability 99% for different discrete time steps (e.g. 2, 1, 0.5, 0.25, 0.1)?


```{r echo=FALSE}
i=7
#while (sum(P2[[i]]$proxel$prob[P2[[i]]$proxel$state != 4]) > 0.01 ) i=i+1
P2[[8]]$proxel[P2[[8]]$proxel$state != 4,]
print(paste("Für 2 An Tag:",(i-1)*2)) ## nach Tag 14

i=1
while (sum(P1[[i]]$proxel$prob[P1[[i]]$proxel$state != 4]) > 0.01 ) i=i+1
P1[[i]]$proxel[P1[[i]]$proxel$state != 4,]
print(paste("Für 1 An Tag:",(i-1)*1))

i=1
while (sum(P05[[i]]$proxel$prob[P05[[i]]$proxel$state != 4]) > 0.01 ) i=i+1
P05[[i]]$proxel[P05[[i]]$proxel$state != 4,]
print(paste("Für .5 An Tag:",(i-1)*0.5))

i=1
while (sum(P025[[i]]$proxel$prob[P025[[i]]$proxel$state != 4]) > 0.01 ) i=i+1
P025[[i]]$proxel[P025[[i]]$proxel$state != 4,]
print(paste("Für .25 An Tag:",(i-1)*0.25))
```

























