Commit 6e2998d9 authored by pfreon's avatar pfreon
Browse files

Moderate changes the decision tree related to the additive model & use of th...

Moderate changes the decision tree related to the additive model & use of th Previous button & in messages

Continued improvement of the elimination of the question related to large fluctuation in abundance when overexploitation if there is no corresponding (additive) model available.
Improvement of plots titles.
Addition of a message about the use of the Previous button.
Addition of a message related to MSY & MSE graphs display.
Transformation from integer to boolean of the variables Flag_Modele_Lineaire_deja_recherche, Flag_Modele_Exponentiel_deja_recherche
Improvement of message contents related to the stop of the procedure according to R2 values.
parent b002150e
......@@ -359,7 +359,12 @@ public class Cadre_Question extends JFrame
RechercheModele.select();
//System.out.println("retour equation " +RechercheModele.getEquation());
if (RechercheModele.getType() == -1) {
new MsgDialogBox(0, "There is no available model fully appropriate to your case. \nIn order to found an appropriate model, you can revise \nyour previous answers. \nIt is not recommended to modify your answers too much upward \ndue to re-initialization issues.", 1, this.parent);
new MsgDialogBox(0, "There is no available model fully appropriate to your case, which is:\n"
+ "an environmental influence on: " + Global.environmental_influence
+ "\na relationship between CPUE and E: "
+ CadreMain.cpu_relation_E[Global.relationCPU_E]
+ "\nand a relationship between CPUE and V: " + CadreMain.cpu_relation_V[Global.relationCPU_V]
+ ".\nIn order to found an appropriate model, you can revise \nyour previous answers. \nIt is not recommended to modify your answers too much upward \ndue to re-initialization issues.", 1, this.parent);
return;
}
......@@ -403,10 +408,10 @@ public class Cadre_Question extends JFrame
String[] item;
String message;
modele = new DefaultListModel<String>();
numQ = QuestionReponse.getNum(); // Numéro de règle appliquée (4ème colonne de fichier Arbre_decisions.csv).
numR = QuestionReponse.getNumRegle();
numQ = QuestionReponse.getNum(); // Numéro de question vers laquelle la règle renvoi (2ème et 3ème colonne de fichier Arbre_decisions.csv)
numR = QuestionReponse.getNumRegle(); // Numéro de règle appliquée (4ème colonne de fichier Arbre_decisions.csv).
//System.out.println("Dans Cadre_Question ligne 406: n° de question numQ = " + numQ + " n° de règle numR = " + numR);
if (numQ == -1)
if (numQ == -1) // End of the model selection procedure due to basic assumptions not fulfilled.
{
String m$ = "Your data set is not appropriate.";
if (numR > 6) // Modif. 2020
......@@ -420,9 +425,9 @@ public class Cadre_Question extends JFrame
return;
}
else
if (numQ == -2)
{
new MsgDialogBox(0, "The fit of the selected model is not good enough.\nI stop here the procedure.", 0, this.parent);
if (numQ == -2) // End of the model selection procedure due to R2 < .40 for models CPUE=f(E) or CPUE=f(V). Comment 2021
{
new MsgDialogBox(0, "The fit of the simple selected model CPUE = f(E) or CPUE = f(V) \nis not good enough (R2 < 0.40) to be retained in a final model \nor in a mixed model where the considerd variable is most influent.\nI stop here the procedure.", 0, this.parent);
//System.out.println("Texte_erreur_jackknife$1 = "); // + Texte_erreur_jackknife$[1]
QuestionReponse.reset();
this.dispose();
......@@ -434,8 +439,23 @@ public class Cadre_Question extends JFrame
}
return;
}
else
if (numQ == -3)
else
if (numQ == -3) // End of the validation procedure due to R2 < .70 for all mixed models CPUE=f(E,V) {
{
new MsgDialogBox(0, "The selected model is not validated \ndue to R2 < .70 for a mixed model CPUE=f(E,V).\nI stop here the procedure.", 0, this.parent);
QuestionReponse.reset();
this.dispose();
Global.CadreQuestion = null;
if (dlgSp != null)
{
dlgSp.setVisible(false);
dlgSp = null;
}
return;
}
else
if (numQ == -4) // End of the validation procedure due to the last answer related to plots at the end of the validation procedure
// Addition 2021.
{
new MsgDialogBox(0, "The selected model is not validated \ndue to your last answer.\nI stop here the procedure.", 0, this.parent);
QuestionReponse.reset();
......@@ -449,14 +469,16 @@ public class Cadre_Question extends JFrame
return;
}
else
if (numQ == -4)
if (numQ == -5) // Happy end of the validation procedure
{
if (dlgSp != null)
{
dlgSp.setVisible(false);
dlgSp = null;
}
new MsgDialogBox(0, "The model is validated. \nYou can use this model for prediction, \ndisplay result tables through the 'Modelization' menu \nand built a folder containing .html and .jpg files of the history \nof all results (graphs & tables) and answers to questions \nthrough the 'File' menu.", 1, this.parent);
if ((Global.numero_modele < 6 && Global.numero_modele > 1) || Global.numero_modele == 20 || Global.numero_modele == 33) // Modèles CPUE=f(V) et modèle exponentiel additif
new MsgDialogBox(0, "MSY and MS-E graphs are not justified or available for this model.\nPlease continue.", 0, this);
new MsgDialogBox(0, "The model is validated. \nYou can use it for prediction, display result tables through \nthe 'Modelization' menu and built a folder containing .html and \n.jpg files of the history of all results (graphs & tables) and \nanswers to questions through the 'File' menu.", 1, this.parent);
QuestionReponse.reset();
this.dispose();
......@@ -543,11 +565,20 @@ public class Cadre_Question extends JFrame
case 16:
dlgSp = new Cadre_SplitPlot(Global.indePlot);
break;
case 18:
Modele.Flag_additive_model_fitted = false;
TexteRegles.Flag_Modele_Exponentiel_deja_recherche = false;
TexteRegles.Flag_Modele_Lineaire_deja_recherche = false;
break;
case 19:
case 20:
//dlgSp=new Cadre_SplitPlot(Global.scatterPlot[2]);
dlgSp = new Cadre_SplitPlot(Global.lagPlot[0]);
break;
case 46:
Modele.Flag_additive_model_fitted = false;
Global.relationCPU_V=0;
break;
case 48:
case 49:
case 50:
......@@ -558,6 +589,9 @@ public class Cadre_Question extends JFrame
//dlgSp=new Cadre_SplitPlot(Global.scatterPlot[3]);
dlgSp = new Cadre_SplitPlot(Global.lagPlot[1]);
break;
case 66:
Global.relationCPU_E=0;
break;
case 68:
dlgSp = new Cadre_SplitPlot(Global.residualsPlot[0]);
break;
......
......@@ -24,6 +24,7 @@ public class Modele {
static final private double eps = 1.E-8;
static final private double mul = 1.E-3;
static public int nim;
static public boolean Flag_additive_model_fitted;
static private int nimTabFish;
static private int nbYears; // Addition 2020.
static private double lambda;
......@@ -69,11 +70,11 @@ public class Modele {
calcul_val_init();
double PosInfinity = Double.POSITIVE_INFINITY;
double NegInfinity = Double.NEGATIVE_INFINITY;
Global.message$[12] = "";
Global.message$[13] = "";
for (int i = 0; i < nbre_par; i++) {
par_init[i] = par_alors[i]; // Valeur initiale paramètre pour une estimation des valeurs initiales du modèle simplifié initial (sans constante) avant ajustement du modèle final incluant une constante. Comment. 2020
if (par_alors[i] == PosInfinity || par_alors[i] == NegInfinity)
Global.message$[12] = "The estimate of the initial value of at least one of the parameter is equal to infinity.\nThis is likely due to a poor fitting of the model. As a result, the plots related to\nmodelling are not shown.\n\n";
Global.message$[13] = "The estimate of the initial value of at least one of the parameter is equal to infinity.\nThis is likely due to a poor fitting of the model. As a result, the plots related to\nmodelling are not shown.\n\n";
}
//System.out.println( "Avant marquardt");
//for(int i=0;i<nbre_par;i++)System.out.print( par_alors[i]+" ");
......@@ -507,6 +508,7 @@ public class Modele {
par_alors[0] = Math.exp(par_alors[0]);
break;
case 20: // CPUE=a.exp(b.E)+c.V+d MODELE ABONDANCE ADDITIF (impossible reformuler en a+bV+c exp(d E) du fait que les paramètres de l'initialisation doivent être les premiers).
Flag_additive_model_fitted = true;
nbre_par = 2;
for (k = 0; k < nim; k++) {
ptmp[0] = 1;
......@@ -1072,8 +1074,8 @@ public class Modele {
PlotSerie[] pvs = new PlotSerie[8];
Global.variatePlot[0] = new Plot();
Global.variatePlot[1] = new Plot();
pvs[0] = new PlotSerie("Weighted effort (E)", Data.getF(), "Observed CPUE", Data.getPue()); // Graphique CPUE observée vs E
pvs[1] = new PlotSerie("Weighted effort (E)", Data.getF(), "Observed Catch (Y)", Data.getYexp()); // Graphique Catche observée vs E
pvs[0] = new PlotSerie("Weighted effort (E)", Data.getF(), "CPUE", Data.getPue()); // Graphique CPUE observée vs E
pvs[1] = new PlotSerie("Weighted effort (E)", Data.getF(), "Catch (Y)", Data.getYexp()); // Graphique Y observée vs E
pvs[0].setCouleur(Color.black);
pvs[1].setCouleur(Color.black);
Global.variatePlot[0].setValeurs(pvs[0]);
......@@ -1179,7 +1181,7 @@ public class Modele {
/************ Traitement des modèles CPUE = f(V) ****************/
else
{
double[] ext = Stat1.Extremas(Data.getV());
double[] ext = Stat1.Extremas(Data.getVbar());
miniy = ext[0]; // Minimum de V (axe des X)
maxiy = ext[1];
pasy = (maxiy - miniy) / nim;
......@@ -1189,7 +1191,7 @@ public class Modele {
PlotSerie[] pvs = new PlotSerie[8];
Global.variatePlot[0] = new Plot();
Global.variatePlot[1] = null; // Elimine graphique Y = f(V) au cas où il reste en mémoire d'un ajustement antérieur de Y = f(E,V) ou Y = f(E)
pvs[0] = new PlotSerie("Weighted environment (V)", Data.getV(), "Observed CPUE", Data.getPue());
pvs[0] = new PlotSerie("Weighted environment (V)", Data.getVbar(), "CPUE", Data.getPue()); // Données observées
pvs[0].setCouleur(Color.black);
Global.variatePlot[0].setValeurs(pvs[0]);
Global.variatePlot[0].setTitreGraphique("Function CPUE=f(V) (V lagged if relevant)");
......@@ -1202,7 +1204,7 @@ public class Modele {
tabxx[i] = yy;
yy = yy + pasy;
}
pvs[4] = new PlotSerie("Weighted environment (V)", tabxx, "Predicted CPUE", estim1);
pvs[4] = new PlotSerie("Weighted environment (V)", tabxx, "CPUE", estim1); // Predicted CPUE (fitted curve).
Global.variatePlot[0].setValeurs(pvs[4]);
pvs[4].setFigure(2);
yy = yy + pasy;
......
This diff is collapsed.
......@@ -266,6 +266,7 @@ public static void valide_modele()
//char *nom_jk[] = {"R2(%) time plot","a(%) time plot","b(%) time plot","c(%) time plot","d(%) time plot"};
//char val[25];
/**************** initialisations ********************/
Global.MSY_MSE_OK=false;
nbre_par=Global.nbre_param;
for(i=0;i<nbre_par;i++) par_init[i] = Global.val_param[i];
nim=Data.getNbDataRetenue();
......@@ -426,16 +427,17 @@ public static void valide_modele()
// System.out.println();
}
/*******Calcul des valeurs théoriques en fonction****/
/*******de valeurs remarquables de V observées*******/
/******** Modification du 20/10/96*******************/
/*******Calcul des valeurs théoriques en fonction****
********de valeurs remarquables de V observées*******
******** et warnings de valeurs anormales**********
********* Modification du 20/10/96*******************/
Global.Min_95_Noteworthy_MSE = 0;
Global.Min_95_Noteworthy_MSY = 0;
Global.Max_95_Noteworthy_MSE = 0;
Global.Max_95_Noteworthy_MSY = 0;
for (int n=1; n<5;n++) Global.message$[n] = "";
for (int n=6; n<12;n++) Global.message$[n] = "";
for (int n=6; n<14;n++) Global.message$[n] = "";
for(iv=0;iv<4;iv++){
vv = val_v[iv];
f_ms_m2[iv] /= nim; // Moyenne des valeurs MSE résultant du jackknife
......@@ -474,38 +476,44 @@ public static void valide_modele()
if(y_ms_m2[iv] < 0.0)
Global.message$[7] = "Please note that some MSY central values for at least one \nof the noteworthy V values are unexpectidly negative.\n\n";
if((((vec_y_max2[iv] - vec_y_min2[iv])) / y_ms_m2[iv]) > 1.0)
Global.message$[8] = "Please note that the width of the 95% confidence interval of MSY central values\nfor at least one of the noteworthy V values is larger than the corresponding\nMSY value. Details available in the result table (tab 'Validation').\n\n";
Global.message$[8] = "Please note that the width of the 95% confidence interval of MSY central \nvalues for at least one of the noteworthy V values is larger than\nthe corresponding MSY value. \nDetails available in the result table (tab 'Validation').\n\n";
if((((vec_f_max2[iv] - vec_f_min2[iv])) / f_ms_m2[iv]) > 1.0)
Global.message$[9] = "Please note that the width of the 95% confidence interval of MSE central values for \nat least one of the noteworthy V values is larger than the corresponding MSE value. \nDetails available in the result table (tab 'Validation').\n\n";
Global.message$[9] = "Please note that the width of the 95% confidence interval of MSE \ncentral values for at least one of the noteworthy V values is larger \nthan the corresponding MSE value. \nDetails available in the result table (tab 'Validation').\n\n";
//System.out.println("iv = " + iv + " Val MSY interv. 95% = " + (vec_y_max2[iv] - y_ms_m2[iv]) + " Val central MSY = " + y_ms_m2[iv]);
if(y_ms_m2[iv] > (Data.stat[7][0] * 10))
Global.message$[11] = "Please note that some MSY central values for at least\none of the noteworthy V values are 10 times larger\nthan the maximum observed catch value, which is unexpected\nfor a stock that underwent optimal and/or overexploitation.\n\n";
if(f_ms_m2[iv] > (Data.stat[7][2] * 5))
Global.message$[12] = "Please note that some MSE central values for at least\none of the noteworthy V values are 5 times larger \nthan the maximum observed fishing effort value, which is \nunexpected for a stock that underwent optimal and/or \noverexploitation.\n\n";
}
if(y_ms_m2[0] > Data.stat[7][0])
Global.message$[11] = "Please note that the MSY central value for the mean V value is 10 times \nlarger than the maximum observed catch value.\n\n";
//System.out.println("y_ms_m2[0] = " + y_ms_m2[0] + " f_ms_m2[1] = " + f_ms_m2[1] + " f_ms_m2[2] = " + f_ms_m2[2] + " f_ms_m2[3] = " + f_ms_m2[3]);
//saveResult();
//System.out.println("Global.Max_95_Noteworthy_MSE = " + Global.Max_95_Noteworthy_MSE + " Global.Min_95_Noteworthy_MSE = " + Global.Min_95_Noteworthy_MSE);
//System.out.println("Global.Max_95_Noteworthy_MSY = " + Global.Max_95_Noteworthy_MSY + " Global.Min_95_Noteworthy_MSY = " + Global.Min_95_Noteworthy_MSY);
//System.out.println("Flag makePlotJack() Validation.java ligne 484");
makePlotJack(); // Make jackknife parameters (and not anymore MSY = f(V) + MSE = f(V) plots)
Global.validationOk=true;
//System.out.println("y_ms_m2[0] = " + y_ms_m2[0] + " f_ms_m2[1] = " + f_ms_m2[1] + " f_ms_m2[2] = " + f_ms_m2[2] + " f_ms_m2[3] = " + f_ms_m2[3]);
//saveResult();
//System.out.println("Global.Max_95_Noteworthy_MSE = " + Global.Max_95_Noteworthy_MSE + " Global.Min_95_Noteworthy_MSE = " + Global.Min_95_Noteworthy_MSE);
//System.out.println("Global.Max_95_Noteworthy_MSY = " + Global.Max_95_Noteworthy_MSY + " Global.Min_95_Noteworthy_MSY = " + Global.Min_95_Noteworthy_MSY);
//System.out.println("Flag makePlotJack() Validation.java ligne 484");
makePlotJack(); // Make jackknife parameters (and not anymore MSY = f(V) + MSE = f(V) plots)
Global.validationOk=true;
if ((Global.Max_95_Noteworthy_MSE == 4 || Global.Max_95_Noteworthy_MSY == 4) && Global.numero_modele != 20 && (Global.numero_modele > 5 || Global.numero_modele < 2) && Global.numero_modele != 33) {
Global.message$[3] = "WARNING: \nAll noteworthy values of MSY and/or MSE upper limits at 95% \nare null or negative which suggest a poor fit or an unrealistic shape \nof MSY vs V and/or MSE vs V. \nFor more details please open the menu 'Modelization' and click \non 'Display result tables' and on the tab 'Validation'.\nThe graphs MSY vs V and MSY vs V are not shown.\n\n" ;
Global.MSY_MSE_OK=false;
}
if ((Global.Min_95_Noteworthy_MSE>=4 && Global.Min_95_Noteworthy_MSY>=4) && Global.numero_modele != 20 && (Global.numero_modele > 5 || Global.numero_modele < 2) && Global.numero_modele != 33) {
Global.message$[4] = "WARNING: \nAll noteworthy values of MSY and MSE lower limits at 95% are \nnull or negative which suggest a poor fit or an unrealistic shape \nMSY vs V and MSE vs V. \nFor more details please open the menu 'Modelization' and click \non 'Display result tables' and on the tab 'Validation'.\nThe line of the lower limit at 95% is not visible on\nthe graphs MSY = f(V) and MSE = f(V), and it might be also\nthe case for the central lines of MSY and MSE.\n\n" ;
Global.message$[4] = "WARNING: \nAll noteworthy values of MSY and MSE lower limits at 95% are \nnull or negative which suggest a poor fit or an unrealistic shape \nMSY vs V and MSE vs V. \nFor more details please open the menu 'Modelization' and click \non 'Display result tables' and on the tab 'Validation'.\n\n" ;
}
if (Global.Min_95_Noteworthy_MSE>=4 && Global.numero_modele != 20 && (Global.numero_modele > 5 || Global.numero_modele < 2) && Global.numero_modele != 33) {
Global.message$[1] = "WARNING: \nAll noteworthy values of MSE lower limit at 95% are \nnull or negative which suggest a poor fit or an unrealistic shape \nMSE vs V. \nFor more details please open the menu 'Modelization' and click \non 'Display result tables' and on the tab 'Validation'.\nThe line of the lower limit at 95% is not visible on \nthe graph MSE vs V.\n\n" ;
Global.message$[1] = "WARNING: \nAll noteworthy values of MSE lower limit at 95% are \nnull or negative which suggest a poor fit or an unrealistic shape \nMSE vs V. \nFor more details please open the menu 'Modelization' and click \non 'Display result tables' and on the tab 'Validation'.\n\n" ;
}
if (Global.Min_95_Noteworthy_MSY>=4 && Global.numero_modele != 20 && (Global.numero_modele > 5 || Global.numero_modele < 2) && Global.numero_modele != 33) {
Global.message$[2] = "WARNING: \nAll noteworthy values of MSY lower limit at 95% are \nnull or negative which suggest a poor fit or an unrealistic shape \nMSY vs V. \nFor more details please open the menu 'Modelization' and click \non 'Display result tables' and on the tab 'Validation'.\nThe line of the lower limit at 95% is not visible on \nthe graph MSY vs V.\n\n" ;
Global.message$[2] = "WARNING: \nAll noteworthy values of MSY lower limit at 95% are \nnull or negative which suggest a poor fit or an unrealistic shape \nMSY vs V. \nFor more details please open the menu 'Modelization' and click \non 'Display result tables' and on the tab 'Validation'.\n\n" ;
}
if (Global.message$[6] == "" && Global.message$[10] == "" && Global.message$[3] == "") {
Global.MSY_MSE_OK=true;
makePlotMSE_MSY();
}
else {
Global.msyPlot[0]=new Plot(); // [0] MS-E versus V; & [1] MSY versus V. 2020.
Global.msyPlot[1]=new Plot();
}
}
public static Object[][] getParamResult(){
......@@ -580,7 +588,8 @@ public static Object[][] getParamResult(){
}
/****************************************************
* Jacknife plot
* Jacknife plot *
* and warning messages *
*****************************************************/
static private void makePlotJack(){
......@@ -626,7 +635,7 @@ static private void makePlotJack(){
if (trjk[jk] >= 100) Nb_years_greater100[i] = Nb_years_greater100[i] +1; // Compte le nb d'occurence >100%
if (trjk[jk] > 200 || trjk[jk] < 1) ++Global.Flag_pb_jackknife_Tot;
}
if (Nb_years_greater100[i] == nim || Nb_years_lower100[i] == nim || Global.Flag_pb_jackknife_Tot >1)
if (Nb_years_greater100[i] == nim || Nb_years_lower100[i] == nim || Global.Flag_pb_jackknife_Tot > 0)
{
++Global.Flag_pb_jackknife_Tot;
//System.out.println("Problem in Validation.java Nb years = " + nim + " i = " + i + " Flag_pb_jackknife_Tot = " + Global.Flag_pb_jackknife_Tot + " Nb_years_greater100[i] = " + Nb_years_greater100[i] + " Nb_years_lower100[i] " + Nb_years_lower100[i]);
......
......@@ -12,13 +12,13 @@
9;11;10;35;1;under_and_over_exploited;Do you think that the data-set covers periods of both overexploitation and of underexploitation?; undover.hlp ;;;;;;;;;;;
10;11;-1;49;1;under_and_optimaly;Do you think that the data-set cover periods of both underexploitation and optimal exploitation?; undopt.hlp ;;;;;;;;;;;
11;12;12;37;1;statistiques_anormales;Do you see any abnormal statistics in the statistical data table? (See the second table in the Climprod frame); abnormal.hlp ;;;;;;;;;;;
12;13;13;36;1;unstability;Is interannual variability too large?; unstabil.hlp ;;;;;;;;;;;
12;13;13;36;1;unstability;Is the interannual variability too large?; unstabil.hlp ;;;;;;;;;;;
13;14;14;38;1;abnormal_points_dist;Do you see abnormal distribution in the histograms?; abnormal distrib.hlp ;;;;;;;;;;;
14;15;15;39;1;abnormal_points_scatt;Do you see outlier points?; outlier.hlp ;;;;;;;;;;;
15;16;16;50;1;effort_increasing;Constantly increasing effort?; increasf.hlp ;;;;;;;;;;;
16;17;17;40;1;independance;Are the two variables dependent?; independ.hlp ;;;;;;;;;;;
17;18;18;-1;1;nb_classes_exploitees;Number of significantly exploited year-classes; nbclass.hlp ;1;2;3;4;5;6;7;8;>8;;
18;56;19;-1;1;effort_preponderant;Is the influence of the environment on CPUE more important than the influence of fishing effort?; fishmore.hlp ;;;;;;;;;;;
18;56;19;-1;1;envir_preponderant;Is the influence of the environment on CPUE more important than the influence of fishing effort?; fishmore.hlp ;;;;;;;;;;;
19;20;-1;48;1;decreasing_relationship;Does this plot appear to be decreasing?; decreas.hlp ;;;;;;;;;;;
20;39;21;17;1;obviously;Does this plot look obviously linear?; obvious.hlp ;;;;;;;;;;;
21;39;22;18;1;pessimiste;Do you have any (additional) reason to expect a highly unstable behaviour or a collapse of the stock?; pessimis.hlp ;;;;;;;;;;;
......@@ -46,30 +46,28 @@
43;101;44;51;2;;;;;;;;;;;;;;
44;101;101;52;2;;;;;;;;;;;;;;
45;95;46;6;2;;;;;;;;;;;;;;
46;102;48;-1;1;environmental_influence;Does the environment influence:; Influenc.hlp ;abundance;catchability;both;;;;;;;;
102;47;48;56;1;;;;;;;;;;;;;;
47;50;48;-1;1;cpue_sous_sur_production;May the stock present large fluctuations in abundance due to the environment when overexploited?; additif.hlp ;;;;;;;;;;;
46;48;48;-1;1;environmental_influence;Does the environment influence:; Influenc.hlp ;abundance;catchability;both;;;;;;;;
48;104;49;17;1;linear_relationship;Does this plot look linear; linear.hlp ;;;;;;;;;;;
49;104;104;-1;1;monotonic_relationship;Does this plot look monotonic?; monotoni.hlp ;;;;;;;;;;;
50;104;-1;57;1;linear_relationship;Does this plot look linear?; linear.hlp ;;;;;;;;;;;
104;54;51;59;1;;;;;;;;;;;;;;
51;52;52;-1;1;recruitment_age;Age at recruitment; Agerec.hlp ;1;2;3;4;5;6;7;8;>8;;
52;53;53;-1;1;begin_influence_period;Age at the begining of environmental influence; begin.hlp ;0;1;2;3;4;5;6;7;8;>8;
53;54;54;-1;1;end_influence_period;Age at the end of environmental influence; end.hlp ;0;1;2;3;4;5;6;7;8;>8;
53;102;102;-1;1;end_influence_period;Age at the end of environmental influence; end.hlp ;0;1;2;3;4;5;6;7;8;>8;
102;47;54;56;1;;;;;;;;;;;;;;
47;54;54;57;1;cpue_sous_sur_production;May the stock present large fluctuations in abundance due to the environment when overexploited?; additif.hlp ;;;;;;;;;;;
54;95;55;4;2;;;;;;;;;;;;;;
55;95;-2;5;2;;;;;;;;;;;;;;
55;95;-3;5;2;;;;;;;;;;;;;;
56;57;57;-1;1;recruitment_age;Age at recruitment; Agerec.hlp ;1;2;3;4;5;6;7;8;9;10;>10
57;58;58;-1;1;begin_influence_period;Age at the begining of environmental influence; begin.hlp ;0;1;2;3;4;5;6;7;8;>8;
58;59;59;-1;1;end_influence_period;Age at the end of environmental influence; end.hlp ;0;1;2;3;4;5;6;7;8;>8;
59;62;60;-1;1;linear_relationship;Does_this_plot_look_linear?; linear.hlp ;;;;;;;;;;;
59;62;60;27;1;linear_relationship;Does_this_plot_look_linear?; linear.hlp ;;;;;;;;;;;
60;62;63;-1;1;monotonic_relationship;Does this plot look monotonic?; monotoni.hlp ;;;;;;;;;;;
61;64;62;26;1;;;;;;;;;;;;;;
62;64;63;28;1;;;;;;;;;;;;;;
63;64;64;29;1;;;;;;;;;;;;;;
64;95;65;9;2;;;;;;;;;;;;;;
65;-2;66;53;2;;;;;;;;;;;;;;
66;67;68;-1;1;environmental_influence;Does the environment influence:; Influenc.hlp ;abundance;catchability;both;;;;;;;;
67;68;68;-1;1;cpue_sous_sur_production;May the stock present large fluctuations in abundance when overexploited?; additif.hlp;;;;;;;;;;;
66;68;68;-1;1;environmental_influence;Does the environment influence:; Influenc.hlp ;abundance;catchability;both;;;;;;;;
68;87;69;17;1;obviously;Does this plot look obviously linear?; obvious.hlp ;;;;;;;;;;;
69;87;70;18;1;pessimiste;Do you have any (additional) reason to expect highly unstable behaviour or collapse of the stock?; pessimis.hlp ;;;;;;;;;;;
70;87;71;19;1;stock_deja_effondre;Did the stock already collapse or exhibit drastic decrease(s) in catches?; collapse.hlp ;;;;;;;;;;;
......@@ -78,7 +76,7 @@
73;74;74;-1;1;reserves_naturelles;Are there natural protected areas for the stock or constantly inacessible adult biomass?; protect.hlp ;;;;;;;;;;;
74;75;75;-1;1;premiere_reproduction_avant_recrutement;Are there one or several non negligible spawnings before recruitment?; firstspa.hlp ;;;;;;;;;;;
75;76;76;-1;1;fecondite_faible;Is the fecundity of the species very low (sharks mammals etc.)?; fecundit.hlp ;;;;;;;;;;;
76;77;77;-1;1;cpue_unstable;Is there a strong instability in the cpue time series?; unstabil.hlp ;;;;;;;;;;;
76;77;77;-1;1;cpue_unstable;Is there a strong instability in the cpue time series?; unstabilCPUE.hlp ;;;;;;;;;;;
77;89;78;10;1;;;;;;;;;;;;;;
78;89;79;11;1;;;;;;;;;;;;;;
79;89;80;12;1;;;;;;;;;;;;;;
......@@ -91,16 +89,20 @@
86;91;91;23;1;;;;;;;;;;;;;;
87;88;93;24;2;;;;;;;;;;;;;;
88;90;90;51;2;;;;;;;;;;;;;;
89;90;93;25;2;;;;;;;;;;;;;;
89;67;105;56;1;;;;;;;;;;;;;;
67;105;105;-1;1;cpue_sous_sur_production;May the stock present large fluctuations in abundance due to the environment when overexploited?; additif.hlp;;;;;;;;;;;
105;90;93;25;2;;;;;;;;;;;;;;
90;93;93;52;2;;;;;;;;;;;;;;
91;95;92;51;2;;;;;;;;;;;;;;
92;93;93;52;2;;;;;;;;;;;;;;
93;95;94;7;2;;;;;;;;;;;;;;
94;95;-2;8;2;;;;;;;;;;;;;;
95;96;-3;0;2;good_results;Is this an acceptable model?; acceptab.hlp ;;;;;;;;;;;
96;97;-3;15;3;trend_residuals;Are there a good fit and no trend or strong autocorrelation in residuals?; fitresid.hlp ;;;;;;;;;;;
97;98;-3;1;3;coeff_determination_instable;Do you validate the model from the graphical and statistical results that appear in the Jackknife plots window (p values of t-ratios of the parameters, R2 values, p value of F)? (See help file for suggestions); valide.hlp ;;;;;;;;;;;
98;99;100;58;2;;;;;;;;;;;;;;
99;100;-3;55;3;acceptable_graphs;Are the shapes, maximum values and confidence intervals of the MSY and MSE graphs acceptable?; MSY_MSE_graphs.hlp;;;;;;;;;;;
100;-4;-4;3;3;;;;;;;;;;;;;;
93;106;107;60;1;;;;;;;;;;;;;;
106;107;107;-1;1;cpue_sous_sur_production;May the stock present large fluctuations in abundance due to the environment when overexploited?; additif.hlp;;;;;;;;;;;
107;95;94;7;2;;;;;;;;;;;;;;
94;95;-3;8;2;;;;;;;;;;;;;;
95;96;-4;0;2;good_results;Is this an acceptable model?; acceptab.hlp ;;;;;;;;;;;
96;97;-4;15;3;trend_residuals;Are there a good fit and no trend or strong autocorrelation in residuals?; fitresid.hlp ;;;;;;;;;;;
97;98;-4;1;3;coeff_determination_instable;Do you validate the model from the graphical and statistical results that appear in the Jackknife plots window (p values of t-ratios of the parameters, R2 values, p value of F)? (See help file for suggestions); valide.hlp ;;;;;;;;;;;
98;99;100;58;3;;;;;;;;;;;;;;
99;100;-4;55;3;acceptable_graphs;Are the shapes, maximum values and confidence intervals of the MSY and MSE graphs acceptable?; MSY_MSE_GRAPHS.hlp;;;;;;;;;;;
100;-5;-5;3;3;;;;;;;;;;;;;;
101;-2;45;54;2;;;;;;;;;;;;;;
35;Surplus production models results are sensitive to the location of the higher level exploitation reached with respect of the Maximum Sustainable Effort (MSE).; According to your assessment of the maximum level of exploitation reached, modelization might be borderline or not recommended, as you will see after answering the next question.
36;When the time-series is unstable, especially when effort or environment is concerned, the models are unable to take into account the transitional situations because the past-effort-averaging and the past-environment-averaging approaches are used in this software.;Of course this comment does not apply in the case of short lived species where effort and environment only concern one year of a single year-class.
35;Surplus production models results sensitive to the location of the higher ;level of exploitation reached with respect to the Maximum Sustainable Effort (MSE). ;According to your assessment of the maximum level of exploitation reached, ;modelization might be borderline or not recommended, as you will see ;after answering the next question.
36;When the time-series is unstable, especially when effort or environment is concerned, ;the models are unable to take into account the transitional situations ;because the past-effort-averaging and the past-environment-averaging approaches ;are used in this software.;Of course this comment does not apply in the case of short lived species where effort and ;environment only concern one year of a single year-class.
37;Modelization will not be reliable owing to the data-series structure.
38;Your case is a borderline case for using these models because your data-set probably contains a few outlier points which may strongly force the structure of any model. The jackknife results will probably confirm that the fit of the model (if any selected by the software) is poor.
39;Check your data to confirm that the outlier point(s) is (are) not an error. If not this (these) point(s) may hinder the analysis, especially if the program has to use the concerned point(s) when applying the past-averaging approach to solve the problem of transitional situation. ;This comment does not apply in the case of short-lived species where the effort and environment only concern one year of a single year-class.
40;If the two explanatory variables, environment and fishing effort, are not independent (co-linearity or shaped relationship), it will be difficult to recognize the difference between the influence of these two variables on the CPUE, and therefore the model will be imprecise. If the mentioned relationship seems very important, it may be recommended to use a model with a single dependent variable.
41;Your data-set is too short for using these models. ;At least 12 years of observation are required in order to limit the problem of the low degrees of freedom when fitting a model (providing that after taking into account possible lags and the number of parameters of the retained model will not reduce it further down).; Sorry, I stop the model selection routine here.
42;Your data-set is not appropriate for using these models except if the used fishing effort takes into account these changes.;Sorry, I stop the model selection routine here.
43;Your data-set is not appropriate for using these models because you have first to standardize your fishing effort unit.;Sorry, I stop the model selection routine here.
44;Your data-set is not appropriate for using these models on account of the population dynamics of your stock (or of your insufficient knowledge of it).;Sorry, I stop the model selection routine here.
45;Your data-set is not appropriate for using these models on account of the stock structure (or of your insufficient knowledge of it).; Sorry, I stop the model selection routine here.
46;Your data-set is not appropriate for using these models on account of the stock structure (or of your insufficient knowledge of it).; Sorry, I stop the model selection routine here.
47;Your data-set is not appropriate for using surplus production models using fishing effort because the relative range of fishing effort variation, as it appears in the "Current known facts panel, is lower than 100% (factor x 2).;If you think that environment is the main variable driving CPUE variation and if you suppose that fishing effort will continue to stay at the same level, you may apply a simple regression using only environment as an independent variable. For doing this, please choose the 'fit_a_model_directly' menu or answer NO to the question: "Is the influence of effort on CPUE more important than the environmental influence?".;Please note that this regression is not a model and that any prediction will be poor since the fishing effort level may change without influence on the regression.
48;There is a contradiction between your answers, or your data-set is not appropriate for use with surplus production models because the relationship between CPUE (or CPUE residuals) and effort is expected to be obvioulsy decreasing. This means that fishing effort was probably not the key variable driving the stock during the period of observation.;If you think that environment is the main variable driving CPUE variations and if you suppose that fishing effort will continue to stay at the same level, you may apply a simple regression using only environment as independent variable (choose the "fit_a_model_directly" menu; or answer NO to the question: "Is the influence of effort on CPUE more important than the environmental influence?". ;Note that this regression is not a model and that any prediction will be poor since the fishing effort level may change without influence on the regression.;Sorry, I stop the model selection routine here.
49;Model results would be inappropriate for stock assessment owing to the low range of exploitation levels.;Sorry, I stop the model selection routine here.
50;Maximum sustainable yield(s) will be overestimated owing to the dynamics of exploitation: when the effort is constantly increasing, the equilibrium state is not respected, and owing to the past-effort-averaging method which was retained in transitional state cases MSY is overestimaded (see Appendix B of the manual for discussion).
57;There is no available model fully appropriate to your case. ;This is due to your positive answer to the question: 'May the stock present large fluctuations in CPUE when overexploited?' and ;to your negative answer regarding the linearity of the relationship between residual CPUE and environment.
\ No newline at end of file
38;Your case is a borderline case for using these models because your data-set ;probably contains a few outlier points which may strongly ;force the structure of any model. The jackknife results will probably ;confirm that the fit of the model (if any selected by the software) is poor.
39;Check your data to confirm that the outlier point(s) is (are) not an error. ;If not this (these) point(s) may hinder the analysis, ;especially if the program has to use the concerned point(s) when applying the ;past-averaging approach to solve the problem of transitional situation. ;This comment does not apply in the case of short-lived species where the effort and environment only concern one year of a single year-class.
40;If the two explanatory variables, environment and fishing effort, are not independent ;(co-linearity or shaped relationship), it will be difficult to recognize ;the difference between the influence of these two variables on the CPUE, ;and therefore the model will be imprecise. ;If the mentioned relationship seems very important, it may be recommended to use a model with a single dependent variable.
41;Your data-set is too short for using these models. ;At least 12 years of observation are required in order to limit the problem ;of the low degrees of freedom when fitting a model (providing that after ;taking into account possible lags and the number of parameters ;of the retained model will not reduce it further down). ;Sorry, I stop the model selection routine here.
42;Your data-set is not appropriate for using these models except ;if the used fishing effort takes into account these changes.;Sorry, I stop the model selection routine here.
43;Your data-set is not appropriate for using these models because ;you have first to standardize your fishing effort unit.;Sorry, I stop the model selection routine here.
44;Your data-set is not appropriate for using these models on account of the ;population dynamics of your stock (or of your insufficient knowledge of it).;Sorry, I stop the model selection routine here.
45;Your data-set is not appropriate for using these models on account of the ;stock structure (or of your insufficient knowledge of it). ;Sorry, I stop the model selection routine here.
46;Your data-set is not appropriate for using these models on account of the ;stock structure (or of your insufficient knowledge of it). ;Sorry, I stop the model selection routine here.
47;Your data-set is not appropriate for using surplus production models using ;fishing effort because the relative range of fishing effort variation, as it ;appears in the "Current known facts" panel, is lower than 100% (factor x 2).;If you think that environment is the main variable driving CPUE variation ;and if you suppose that fishing effort will continue to stay at the same level, ;you may apply a simple regression using only the environment ;as an independent variable. ;For doing this, please choose the "fit_a_model_directly" menu or answer NO ;to the question: "Is the influence of effort on CPUE more important than ;the environmental influence?".;Please note that this regression is not a model and that any prediction ;will be poor since the fishing effort level may change without ;influence on the regression.
48;There is a contradiction between your answers, or your data-set is not appropriate ;for use with surplus production models because the relationship ;between CPUE (or CPUE residuals) and effort is expected to be obvioulsy decreasing. ;This means that fishing effort was probably not the key variable ;driving the stock during the period of observation.;If you think that environment is the main variable driving CPUE variations ;and if you suppose that fishing effort will continue to stay at the same level, ;you may apply a simple regression using only environment ;as independent variable (choose the "fit_a_model_directly" menu ;or answer NO to the question: "Is the influence of effort ;on CPUE more important than the environmental influence?". ;Note that this regression is not a model and that any prediction ;will be poor since the fishing effort level may change without influence on the regression.;Sorry, I stop the model selection routine here.
49;Model results would be inappropriate for stock assessment ;owing to the low range of exploitation levels.;Sorry, I stop the model selection routine here.
50;Maximum sustainable yield(s) will be overestimated owing to the dynamics of exploitation: ;when the effort is constantly increasing, the equilibrium state is not respected, ;and owing to the past-effort-averaging method which was retained in ;transitional state cases MSY is overestimaded (see Appendix B of the manual for discussion).
57;There is no available model fully appropriate to your case. ;This is due to your positive answer to the question: 'May the stock ;present large fluctuations in CPUE when overexploited?' and ;to your negative answer regarding the linearity of the relationship between residual CPUE ;and environment.
58;There is no possibility of drawing MSY and MS-E graphs for this particular model. Please continue.
\ No newline at end of file
......@@ -25,11 +25,12 @@
24;0;A simple model CPUE=f(E) or Residuals=f(E) of generalized type is deduced if:; -a linear model has been deduced ; -this model has a coefficient of determination R2 < 40 ;;;;;;;
25;0;A simple model CPUE=f(E) or Residuals=f(E) of generalized type is deduced if:; -an exponential model has been deduced ; -this model has a coefficient of determination R2 < 40 ;;;;;;;
26;0;A simple model CPUE=f(V) or Residuals=f(V) of linear type is deduced if:; -the graphic of this relationship appears linear.;;;;;;;;
27;0;A message on the use of the 'Previous (P)' is displayed if:; -The question regarding the type of influence of the environment was not asked before.;;;;;;;;
28;0;A simple model CPUE=f(V) or Residuals=f(V) of type power or general is deduced if:; -the graphic of this relationship appears non-linear ; -the graphic of this relationship appears monotonic ;;;;;;;
29;0;A simple model CPUE=f(v) or Residuals=f(V) of quadratic type is deduced if:; -the graphic of this relationship appears non-linear ; -the graphic of this relationship does not appear monotonic ;;;;;;;
30;0;The message about the double click use is shown if:;-you declared that you are aware of this use.;;;;;;;;
32;0;Effect is catchability if:; -you answer catchability to the environmental influence question.;;;;;;;;
33;0;Effect is abundance catchability if:; -you answer both or I do not know to the environmental influence question.;;;;;;;;
33;0;Effect of the environment is both abundance & catchability if:; -you answer both or I do not know to the environmental influence question.;;;;;;;;
35;0;Model results are uncertain (particularly maximum sustainable yield) if:; -the data-series does not cover periods of both underexploitation and overexploitation ;;;;;;;;
49;0;Model results are inappropriate for assessment and predicition if:; -the data-series does not cover periods of both under exploitation and overexploitation; -the data-series does not cover periods of both underexploitation and of optimal exploitation; ;;;;;;
36;0;Modelization is not reliable if:; -you graphically detect unstability in the time plots .;;;;;;;;
......@@ -51,7 +52,8 @@
53;1;A simple model CPUE=f(V) is not convenient if:; -this model has a coefficient of determination R2 < 40.;;;;;;;;
54;1;A simple model CPUE=f(E) is not convenient if:; -this model has a coefficient of determination R2 < 40.;;;;;;;;
55;0;Model X is not validated if:; -model X is convenient;-the MSY or MSE versus V graphs are not shaped as expected.;;;;;;;
56;0;A mixed additive model M CPUE=f(E,V) is not looked for if:; -the relationship CPUE residual = f(E) is not exponential.;;;;;;;;
56;0;The question 'May the stock present large fluctuations in CPUE when overexploited?' is asked only if:; -the environmental effect is on abundance; -a simple model CPUE=f(E) of exponential type was deduced; - the mixed model exponential-additive has not been fitted before.;-The relationship CPUE=f(V) is linear.;;;;;;;
57;1;A mixed additive model M CPUE= a exp(bE) + cV +d is deduced if:; -effort is preponderant ; -a simple model M1 such as CPUE=f(E) is deduced; -M1 is exponential; -Model M1 has a coefficient of determination R2 lying between 40 and 90; -the environmental effect is abundance; -the stock may present large fluctuations in CPUE when overexploited; -a simple linear model residuals f(V) M2 of is deduced.;;
58;0;Graphs MSY MSE vs V shown during validation if:; -the retained model is not exponential additive;" -the retained model is not a simple model CPUE=f(V).";;;;;;;
59;0;Questions relative to ages at recruitment and at the begining and end of the environmental influence are not asked (unnecessary) if:; -the environmental influence is only on catchabiliy.;;;;;;;;
60;0;The question 'May the stock present large fluctuations in CPUE when overexploited?' is asked only if:; -This question was not asked before; -the environmental effect is on abundance; -a simple model CPUE=f(E) of exponential type was deduced; - the mixed model exponential-additive has not been fitted before.;-The relationship CPUE=f(V) is linear.;;;;;;
\ No newline at end of file
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