Comments and TODO

exploration/NEA_calibration_offline/maturation.Rmd

@@ -168,6 +168,7 @@ computeOgive = function(lengthTrajectories, lengthAtMaturity) {
 ```
 
 ```{r test}
+#Do not run 
 
 tic()
 computeMultipleLengthTrajectories(temperaturePattern, Nind = 1000, growParStich) %>% 
@@ -187,6 +188,7 @@ computeMultipleLengthTrajectories(temperaturePattern %>%  filter(age < 10) ,
 ```
 
 ```{r ogive variability}
+#Do not run 
 tic(msg = 'ogive variability')
 replicate(10, computeMultipleLengthTrajectories(temperaturePattern %>%  filter(age < 10) , 
                                                 Nind = 100000, growPar) %>% 
@@ -221,6 +223,7 @@ computeMultipleLengthTrajectories(temperaturePattern , Nind = 10000, growParStic
 ```
 
 ```{r optimisation}
+#obsolete (presque) au k ou!
 ogiveObs = data.frame(age = seq.int(9), p = c(0, 0, 0.01, 0.09, 0.33, 0.63, 0.92, 1, 1))
 
 lengthTrajectoriesInSpring = computeMultipleLengthTrajectories(temperaturePattern, Nind = 1000, growPar) %>% 
@@ -260,7 +263,7 @@ res_A <- optim(par = c(lengthAtMaturity = 35),
                upper = growPar$Linf)
 
 
-computeOgive(yearlyLengthTrajectories = lengthTrajectoriesInSpring,
+computeOgive(lengthTrajectories = lengthTrajectoriesInSpring,
              lengthAtMaturity = res_A$par) %>% 
   inner_join(ogiveObs, by = 'age', suffix = c(".pred", ".obs"))
 
@@ -329,15 +332,16 @@ computeMultipleLengthTrajectories(temperaturePattern,
   geom_abline(slope =0, intercept = 44)
 ```
 
-```{r calibration K and lengthAtMaturity by gender with ogive}
+```{r calibration K and lengthAtMaturity by gender with ogive, warning = "FALSE"}
  #par = res_D$par
 
 objFn_D = function(par, fixedPar, ogivesObs, parGrowRef,  temperaturePattern, 
                     Nind = 1000, seasonSelected = 'spring',
                     RNGseed =1) {
   
-  fullPar <- enframe(c(par, fixedPar)) %>%  pivot_wider() 
+  fullPar <- enframe(c(par, fixedPar)) %>%  pivot_wider() #full set of parameters
   
+  #extract parameters by sex
   parFemale = fullPar %>% select(tempMinGrow, tempOptGrow, tempMaxGrow, 
                                  lengthAtHatching, 
                                  Linf = linfVonBert, 
@@ -359,7 +363,7 @@ objFn_D = function(par, fixedPar, ogivesObs, parGrowRef,  temperaturePattern,
                                                                           RNGseed = RNGseed) %>%
     filter(season == seasonSelected) %>% 
     computeOgive(lengthAtMaturity = parFemale$lengthAtMaturity) %>% 
-        # add a penalty when p(max(age)) < 1
+        # add a penalty when p(max(age)) < 1 - avoid immature individuals at maximuma age
     mutate(p = if_else(age == max(age) & p < 1, 10*(p-1)^2, p))
   
   SSEfemale = ogivesObs %>% select(age, p = p_female) %>% 
@@ -387,18 +391,19 @@ objFn_D = function(par, fixedPar, ogivesObs, parGrowRef,  temperaturePattern,
     unlist(use.names = FALSE)
   
   ### SSE for growth curve
+  #growth curve for female by using temperature pattern with sigma = 0 (mean_curve)
     femaleCurve <- computeMultipleLengthTrajectoriesWithRandomSeed(temperaturePattern , 
                                                   Nind = 1, 
                                                   growPar = parFemale %>% mutate(sigmaDeltaLVonBert=0) , 
                                                   RNGseed = RNGseed) %>% 
     filter(season == seasonSelected) %>% 
-    select(age, Lfemale = L)
+    select(age, Lfemale = L)#rename L by Lfemale 
     
   maleCurve <- computeMultipleLengthTrajectoriesWithRandomSeed(temperaturePattern , 
                                                   Nind = 1, 
                                                   growPar = parMale %>% mutate(sigmaDeltaLVonBert=0) , 
                                                   RNGseed = RNGseed) %>% 
-    filter(season == seasonSelected) %>% 
+    filter(season == seasonSelected) %>% #1 value for a season 
     select(age, Lmale = L)
   
   SCE1growth <- 
@@ -407,19 +412,24 @@ objFn_D = function(par, fixedPar, ogivesObs, parGrowRef,  temperaturePattern,
     mutate(Lref = vonBertalanffyGrowth(age, parGrowRef$L0, parGrowRef$Linf, parGrowRef$K),
           L2genders = (Lfemale + Lmale)/2,
            SE = ((Lfemale + Lmale)/2 - Lref)^2 ) %>%
+    #SE = (((Lfemale + Lmale)/2 - Lref)/Lref)^2)#TODO TESTER 
     summarise(SSE = sum(SE)) %>%  unlist(use.names = FALSE)
   
 
-  return((SSEmale + SSEfemale)*10 + SCE1growth)
+  return((SSEmale + SSEfemale)*10 + SCE1growth)# 10 for weigthed the sumSquare compared to the sum squared for gives so that both sum squared varied in the same interval
 }
 
+#TODO: ajuster les bonnes valeurs par rapport aux ogives de la literature
 ogivesObs = data.frame(age = seq.int(9), p_female = c(0, 0, 0.01, 0.09, 0.33, 0.63, 0.92, 1, 1),
                        p_male =  c(0, 0, 0.01, 0.15, 0.45, 0.75, 0.92, 1, 1))
 
 
-#TODO verifier si ce sont bien les parametres de Stich
+#TODO verifier si ce sont bien les parametres de Stich ou les valeurs ajustés sur les paramètres de Stich expeté sur le Top ? A retravailler pour voir la manière dont on peu enchainer les processus. 
+#Quelle source de paramètre choisit-on ?
+
 parGrowRef = growParStich %>% select(L0 = lengthAtHatching, K, Linf)
 
+#TODO: modifer les paramètres fixés et les paramètres à optimiser à mon bon vouloir ahah
 par = c(tempOptGrow = 5, 
         kOptForFemale = 0.32, 
         kOptForMale = .21,