Commit 8f80daad authored by pfreon's avatar pfreon
Browse files

Minor change (re-ordering and numbering of models)

Attribution of a number (#) to the different models and re-ordering. Please note that these numbers do not match the case numbers in Model.java.
parent 840b3505
......@@ -34,9 +34,6 @@ public class TexteRegles {
{
try
{
//Flag_Modele_Lineaire_deja_recherche=0;
//Flag_Modele_Exponentiel_deja_recherche=0;
//Flag_Modele_General_deja_recherche=0;
int nbData=0;
String[] dataLine;
......@@ -180,13 +177,13 @@ public static boolean isTrue(){
case 46:
result = (Global.stock_unique!=1 && Global.metapopulation!=1); // Changement de nom variable. Modif. 2020
if(!result){
commentaireEnCours="Metapopulations are special cases that difficult the application of any kind of population dynamics model. \nAccording to the level connectivity between sub-stocks, modelization might be borderline or not recommended \nas you will see after answering the next question.";
commentaireEnCours="Metapopulations are special cases that difficult the application of any kind of population dynamics model.\nAccording to the level connectivity between sub-stocks, modelization might be borderline\nor not recommended as you will see after answering the next question.";
}
break;
case 45:
result = (Global.stock_unique!=1 && Global.metapopulation==1 && Global.sous_stock_isole!=1); // Global.metapopulation==1
if(!result){
commentaireEnCours="Your case is a borderline one either because you deal only with a sub-stock or with a full metapopulation \n(and the lower is the connectivity between sub-stocks, the more borderline you are).\nYou must not extrapolate your results beyond the interval of observation of the different variables (effort and/orenvironment), ;when using the model for predictions.\nMoreover, any surplus production model using effort will implicitly make the assumption that \nno variation in recruitment due to other sub-stocks will occur.";
commentaireEnCours="Your case is a borderline one either because you deal only with a sub-stock or with a full metapopulation\n(and the lower is the connectivity between sub-stocks, the more borderline you are).\nYou must not extrapolate your results beyond the interval of observation of the\ndifferent variables (effort and/orenvironment) when using the model for predictions.\nMoreover, any surplus production model using effort will implicitly make the assumption that \nno variation in recruitment due to other sub-stocks will occur.";
}
break;
case 35:
......@@ -375,9 +372,9 @@ public static boolean isTrue(){
result=(Global.coeff_determination<0.4);
if(result)
{
commentaireEnCours="The coefficient of determination of the model " + RechercheModele.getEquation()+ " is " +nf.format(Global.coeff_determination)+"\n";
commentaireEnCours=commentaireEnCours+ "From the current rule this simple empirical model CPUE = f(V) is not convenient (R² < 0.40)";
commentaireEnCours=commentaireEnCours+"\n Please make sure that your answer to the question 'Is the influence of fishing \neffort on CPUE more important than the environmental influence?' \nwas the correct one";
commentaireEnCours="The coefficient of determination of the model " + RechercheModele.getEquation()+ " is " +nf.format(Global.coeff_determination)+".\n";
commentaireEnCours=commentaireEnCours+ "From the current rule this simple empirical model CPUE = f(V) is not convenient (R² < 0.40).";
commentaireEnCours=commentaireEnCours+"\nPlease make sure that your answer to the question 'Is the influence of fishing \neffort on CPUE more important than the environmental influence?' \nwas the correct one.";
}
break;
case 54:
......@@ -385,9 +382,9 @@ public static boolean isTrue(){
result=(Global.coeff_determination<0.4);
if(result)
{
commentaireEnCours="The coefficient of determination of the model " + RechercheModele.getEquation()+ " is " +nf.format(Global.coeff_determination)+"\n";
commentaireEnCours="The coefficient of determination of the model " + RechercheModele.getEquation()+ " is " +nf.format(Global.coeff_determination)+".\n";
commentaireEnCours=commentaireEnCours+ "From the current rule this simple empirical model CPUE = f(E) is not convenient (R² < 0.40).";
commentaireEnCours=commentaireEnCours+"\nPlease make sure that your answer to the question 'Is the influence of fishing \neffort on CPUE more important than the environmental influence?' \nwas the correct one";
commentaireEnCours=commentaireEnCours+"\nPlease make sure that your answer to the question 'Is the influence of fishing \neffort on CPUE more important than the environmental influence?' \nwas the correct one.";
}
//result=(Global.typeModele == RechercheModele.mixte) ;
break;
......@@ -407,24 +404,34 @@ public static boolean isTrue(){
commentaireEnCours="There is a contradiction between: \n1) your assumption that the data-set covers periods of both underexploitation and optimal exploitation\nand 2) the fact that the maximum observed catch does not overpass any of the MSY central values for noteworthy V values.\nPlease consider revising your answer.\n\n";
if(Global.under_and_over_exploited == 1 && Validation.f_ms_m2[0] > Extremaf[1] && Validation.f_ms_m2[1] > Extremaf[1] && Validation.f_ms_m2[2] > Extremaf[1] && Validation.f_ms_m2[3] > Extremaf[1])
commentaireEnCours=commentaireEnCours+"There is a contradiction between: \n1) your assumption that the data-set covers periods of both overexploitation and underexploitation and\n2) the fact that the maximum observed weighted effort does not overpass any of the MSE central values for noteworthy V values.\nPlease consider revising your answer.\n\n";
else
if(Global.under_and_optimaly == 1 && Validation.f_ms_m2[0] > Extremaf[1] && Validation.f_ms_m2[1] > Extremaf[1] && Validation.f_ms_m2[2] > Extremaf[1] && Validation.f_ms_m2[3] > Extremaf[1])
else if(Global.under_and_optimaly == 1 && Validation.f_ms_m2[0] > Extremaf[1] && Validation.f_ms_m2[1] > Extremaf[1] && Validation.f_ms_m2[2] > Extremaf[1] && Validation.f_ms_m2[3] > Extremaf[1])
commentaireEnCours=commentaireEnCours+"There is a contradiction between: \n1) your assumption that the data-set covers periods of both underexploitation and optimal exploitation and\n2) the fact that the maximum observed weighted effort does not overpass any of the MSE central values for noteworthy V values.\nPlease consider revising your answer.\n\n";
if(Validation.y_ms_m2[0] < 0.0 || Validation.y_ms_m2[1] < 0.0 || Validation.y_ms_m2[2] < 0.0 || Validation.y_ms_m2[3] < 0.0 )
commentaireEnCours=commentaireEnCours+"Please note that some MSY central values for at least one \nof the noteworthy V values are unexpectidly negative.\nPlease consider revising your answer.\n";
if(((Validation.vec_y_max2[0] - Validation.y_ms_m2[0])*2.0) > Validation.y_ms_m2[0] || ((Validation.vec_y_max2[1] - Validation.y_ms_m2[1])*2.0) > Validation.y_ms_m2[1] || ((Validation.vec_y_max2[2] - Validation.y_ms_m2[2])*2.0) > Validation.y_ms_m2[2] || ((Validation.vec_y_max2[3] - Validation.y_ms_m2[3])*2.0) > Validation.y_ms_m2[3])
commentaireEnCours=commentaireEnCours+"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').\nPlease consider revising your answer.\n\n";
if(((Validation.vec_f_max2[0] - Validation.f_ms_m2[0])*2.0) > Validation.f_ms_m2[0] || ((Validation.vec_f_max2[1] - Validation.f_ms_m2[1])*2.0) > Validation.f_ms_m2[1] || ((Validation.vec_f_max2[2] - Validation.f_ms_m2[2])*2.0) > Validation.f_ms_m2[2] || ((Validation.vec_f_max2[3] - Validation.f_ms_m2[3])*2.0) > Validation.f_ms_m2[3])
commentaireEnCours=commentaireEnCours+"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.\nPlease consider revising your answer.\n\n";
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)
commentaireEnCours=commentaireEnCours+ Global.message$[4] + "Please consider revising your answer.\n";
else {
if (Global.Min_95_Noteworthy_MSE>=4 && Global.Min_95_Noteworthy_MSY>=4 && Global.numero_modele != 20 && Global.numero_modele > 5 && Global.numero_modele < 2 && Global.numero_modele != 33)
commentaireEnCours=commentaireEnCours+Global.message$[5] + "Please consider revising your answer.\n";
if (Global.Min_95_Noteworthy_MSY>=4 && Global.numero_modele != 20 && Global.numero_modele > 5 && Global.numero_modele < 2 && Global.numero_modele != 33)
commentaireEnCours=commentaireEnCours+Global.message$[2] + "Please consider revising your answer.\n";
}
}
if (Global.message$[6] != "") // At least one of the central values of MSE is larger than 1.0E10 for noteworthy V values.
commentaireEnCours=commentaireEnCours + Global.message$[6] + "Please consider revising your answer.\n\n";
else if (Global.message$[10] != "") // At least one of the central values of MSE is negative for noteworthy V values.
commentaireEnCours=commentaireEnCours + Global.message$[10] + "Please consider revising your answer.\n\n";
else if (Global.message$[3] != "") // All noteworthy values of MSY and/or MSE upper limits at 95% are null or negative.
commentaireEnCours=commentaireEnCours + Global.message$[3] + "Please consider revising your answer.\n\n";
else // ici on ne rajoute pas Global.message$[5] != "" car déjà dans règle 0 \\ At least one set of predicted values of CPUE=f(E,V) and Y=f(E,V) corresponding to Vmin or Vmax presents only negative values.
{
if (Global.message$[4] != "") // All noteworthy values of MSY and MSE lower limits at 95% are null or negative.
commentaireEnCours=commentaireEnCours + Global.message$[4] + "Please consider revising your answer.\n\n";
else {
if (Global.message$[1] != "") // All noteworthy values of MSE lower limit at 95% are null or negative.
commentaireEnCours=commentaireEnCours + Global.message$[1] + "Please consider revising your answer.\n\n";
if (Global.message$[7] != "") // Please note that some MSY central values for at least one of the noteworthy V values are unexpectidly negative.
commentaireEnCours=commentaireEnCours + Global.message$[7] + "Please consider revising your answer.\n\n";
if (Global.message$[2] != "") // All noteworthy values of MSY lower limit at 95% are null or negative.
commentaireEnCours=commentaireEnCours + Global.message$[2] + "Please consider revising your answer.\n\n";
if (Global.message$[8] != "") //Please note that the width of the 95% confidence interval of MSY central values for at least one of the noteworthy V values is larger than the correspondingMSY value.
commentaireEnCours=commentaireEnCours + Global.message$[8] + "Please consider revising your answer.\n\n";
if (Global.message$[9] != "") // Please note that the width of the 95% confidence interval of MSE central values for at least one of the noteworthy V values is larger than the corresponding MSE value.
commentaireEnCours=commentaireEnCours + Global.message$[9] + "Please consider revising your answer.\n\n";
if (Global.message$[11] != "")// Please note that the MSY central value for the mean V value is 10 times larger than the maximum observed catch value.
commentaireEnCours=commentaireEnCours + Global.message$[11] + "Please consider revising your answer.\n\n";
}
}
}
break;
case 56:
result=(Flag_Modele_Exponentiel_deja_recherche==1);
......@@ -545,11 +552,13 @@ public static boolean isTrue(){
commentaireEnCours=commentaireEnCours+"Warning: You validated the model although the correcte R² value is\n";
commentaireEnCours=commentaireEnCours+"lower than 0.65, which is not recommended.\n\n";
}
if (Global.message$[0] != "") commentaireEnCours=commentaireEnCours+"Warning: "+Global.message$[0]+"\n\n";
}
break;
case 0:
result=(Global.good_results!=1);
if (Global.AllPredictedNegativeTot > 0) commentaireEnCours=commentaireEnCours + Global.message$[3] + "Please consider revising your answer.\n";
if (Global.message$[5] != "") // At least one set of predicted values of CPUE=f(E,V) and Y=f(E,V) corresponding to Vmin or Vmax presents only negative values.
commentaireEnCours=commentaireEnCours + Global.message$[5] + "Please consider revising your answer.\n\n";
if(result)
commentaireEnCours="From the current rule and according to your last answer, the model " + RechercheModele.getEquation()+ " is not validated.";
break;
......
This diff is collapsed.
1;-1;1;1;CPUE=a.exp(b.E);2;model2.txt;2;0;0
0;-2;1;0;CPUE=a+b.E;2;model1.txt;1;0;0
2;-3;1;6;CPUE=(a+b.E)^(1/(c-1));3;model7.txt;3;0;0
3;-4;2;2;CPUE=a+b.V;2;model3.txt;0;1;0
4;-5;2;3;CPUE=a.V^b;2;model4.txt;0;4;0
5;-6;2;4;CPUE=a+b.V^c;3;model5.txt;0;3;0
6;-7;2;5;CPUE=a+b.V+c.V^2;3;model6.txt;0;5;0
12;3;3;9;CPUE=a.V+b.E;2;model10.txt;1;1;1
11;2;3;8;CPUE=a+b.V+c.E;3;model9.txt;1;1;1
13;1;3;7;CPUE=a.V^b+c.E;3;model8.txt;1;3;1
14;4;3;10;CPUE=a+b.V+c.V^2+d.E;4;model11.txt;1;5;1
8;7;3;13;CPUE=a.V.exp(b.E);2;model14.txt;2;1;1
7;6;3;12;CPUE=(a+b.V).exp(c.E);3;model13.txt;2;1;1
9;14;3;20;CPUE=a.exp(b.E)+c.V+d;4;model21.txt;6;1;1
10;5;3;11;CPUE=a.V^b.exp(c.E);3;model12.txt;2;3;1
28;22;3;28;CPUE=a.V^b.exp(c.V^d.E);4;model29.txt;2;3;1
17;15;3;21;CPUE=(a.V+b.V^2).exp(c.E);3;model22.txt;2;5;1
15;18;3;22;CPUE=((a.V^b)+d.E)^(1/(c-1));4;model23.txt;3;3;1
16;19;3;23;CPUE=((a.V+b.V^2)^(c-1)+d.E)^(1/(c-1));4;model24.txt;3;5;1
19;10;3;16;CPUE=a.V+b.V^2.E;2;model17.txt;1;1;2
18;8;3;14;CPUE=a+b.V+c.(a+b.V)^2.E;3;model15.txt;1;1;2
20;9;3;15;CPUE=a.V^b+c.V^(2.b).E;3;model16.txt;1;3;2
22;13;3;19;CPUE=a.V.exp(b.V.E);2;model20.txt;2;1;2
23;12;3;18;CPUE=(a+b.V).exp(c.(a+b.V).E);3;model19.txt;2;1;2
24;11;3;17;CPUE=a.V^b.exp(c.V^b.E);3;model18.txt;2;3;2
21;16;3;24;CPUE=a.V.(b-c.V)+d.V^2.(b-c.V)^2.E;4;model25.txt;1;5;2
25;17;3;25;CPUE=a.V.(1+b.V).exp(c.V.(1+b.V).E);3;model26.txt;2;5;2
26;20;3;26;CPUE=a.V^(b+c)+d.V^(2.b).E;4;model27.txt;1;1;3
27;21;3;27;CPUE=a.V^(1+b)+c.V^(2+b)+d.V^(2.b).E;4;model28.txt;1;5;3
29;23;3;29;CPUE=a.V^b.exp(c.V^d.E);4;model30.txt;2;1;3
30;24;3;30;CPUE=(a.V^(1+b)+c.V^(2+b)).exp(d.V^b.E);4;model31.txt;2;5;3
31;20;3;26;CPUE=a.V^(b+c)+d.V^(2.b).E;4;model27.txt;1;3;3
32;23;3;29;CPUE=a.V^b.exp(E.c.V^d);4;model30.txt;2;3;3
33;25;2;33;CPUE=a.V.exp(b.V);2;model33.txt;0;5;0
34;26;3;34;CPUE=a.V.exp(b.V)+c.E;3;model34.txt;1;5;1
35;28;3;35;CPUE=a.V.exp(b.V).exp(c.E);3;model35.txt;2;5;1
36;30;3;36;CPUE=a.V.exp(b.V).exp(c.V.exp(b.V).E);3;model36.txt;2;5;2
37;31;3;37;CPUE=a.V.exp(b.V)+c.(a.V.exp(b.V))^2).E;3;model37.txt;1;5;2
\ No newline at end of file
0;1;0;#0: CPUE=a+b.E;2;model0.txt;1;0;0
1;1;1;#1: CPUE=a.exp(b.E);2;model1.txt;2;0;0
2;1;6;#2: CPUE=(a+b.E)^(1/(c-1));3;model2.txt;3;0;0
3;2;2;#3: CPUE=a+b.V;2;model3.txt;0;1;0
4;2;3;#4: CPUE=a.V^b;2;model4.txt;0;4;0
5;2;4;#5: CPUE=a+b.V^c;3;model5.txt;0;3;0
6;2;5;#6: CPUE=a+b.V+c.V^2;3;model6.txt;0;5;0
7;2;33;#7: CPUE=a.V.exp(b.V);2;model7.txt;0;1;0
8;3;8;#8: CPUE=a+b.V+c.E;3;model8.txt;1;1;1
9;3;9;#9: CPUE=a.V+b.E;2;model9.txt;1;1;1
10;3;7;#10: CPUE=a.V^b+c.E;3;model10.txt;1;3;1
11;3;10;#11: CPUE=a+b.V+c.V^2+d.E;4;model11.txt;1;5;1
12;3;34;#12: CPUE=a.V.exp(b.V)+c.E;3;model12.txt;1;5;1
13;3;13;#13: CPUE=a.V.exp(b.E);2;model13.txt;2;1;1
14;3;12;#14: CPUE=(a+b.V).exp(c.E);3;model14.txt;2;1;1
15;3;20;#15: CPUE=a.exp(b.E)+c.V+d ;4;model15.txt;6;1;1
16;3;11;#16: CPUE=a.V^b.exp(c.E);3;model16.txt;2;3;1
17;3;28;#17: CPUE=a.V^b.exp(c.V^d.E);4;model17.txt;2;3;1
18;3;21;#18: CPUE=(a.V+b.V^2).exp(c.E);3;model18.txt;2;5;1
19;3;22;#19: CPUE=((a.V^b)+d.E)^(1/(c-1));4;model19.txt;3;3;1
20;3;23;#20: CPUE=((a.V+b.V^2)^(c-1)+d.E)^(1/(c-1));4;model20.txt;3;5;1
21;3;35;#21: CPUE=a.V.exp(b.V).exp(c.E);3;model21.txt;2;5;1
22;3;14;#22: CPUE=a+b.V+c.(a+b.V)^2.E;3;model22.txt;1;1;2
23;3;16;#23: CPUE=a.V+b.V^2.E;2;model23.txt;1;1;2
24;3;15;#24: CPUE=a.V^b+c.V^(2.b).E;3;model24.txt;1;3;2
25;3;24;#25: CPUE=a.V.(b-c.V)+d.V^2.(b-c.V)^2.E;4;model25.txt;1;5;2
26;3;37;#26: CPUE=a.V.exp(b.V)+c.(a.V.exp(b.V))^2).E;3;model26.txt;1;5;2
27;3;19;#27: CPUE=a.V.exp(b.V.E);2;model27.txt;2;1;2
28;3;18;#28: CPUE=(a+b.V).exp(c.(a+b.V).E);3;model28.txt;2;1;2
29;3;17;#29: CPUE=a.V^b.exp(c.V^b.E);3;model29.txt;2;3;2
30;3;25;#30: CPUE=a.V.(1+b.V).exp(c.V.(1+b.V).E);3;model30.txt;2;5;2
31;3;36;#31: CPUE=a.V.exp(b.V).exp(c.V.exp(b.V).E);3;model31.txt;2;5;2
32;3;26;#32: CPUE=a.V^(b+c)+d.V^(2.b).E;4;model32.txt;1;1;3
33;3;26;#32: CPUE=a.V^(b+c)+d.V^(2.b).E;4;model32.txt;1;3;3
34;3;27;#33: CPUE=a.V^(1+b)+c.V^(2+b)+d.V^(2.b).E;4;model33.txt;1;5;3
35;3;29;#34: CPUE=a.V^b.exp(c.V^d.E);4;model34.txt;2;1;3
36;3;29;#34: CPUE=a.V^b.exp(c.V^d.E);4;model34.txt;2;3;3
37;3;30;#35: CPUE=(a.V^(1+b)+c.V^(2+b)).exp(d.V^b.E);4;model35.txt;2;5;3
0;-1;1;1;CPUE=a.exp(b.E);2;model2.txt;2;0;0
1;-2;1;0;CPUE=a+b.E;2;model1.txt;1;0;0
2;-3;1;6;CPUE=(a+b.E)^(1/(c-1));3;model7.txt;3;0;0
3;-4;2;2;CPUE=a+b.V;2;model3.txt;0;1;0
4;-5;2;3;CPUE=a.V^b;2;model4.txt;0;4;0
5;-6;2;4;CPUE=a+b.V^c;3;model5.txt;0;3;0
6;-7;2;5;CPUE=a+b.V+c.V^2;3;model6.txt;0;5;0
12;3;3;9;CPUE=a.V+b.E;2;model10.txt;1;1;1
11;2;3;8;CPUE=a+b.V+c.E;3;model9.txt;1;1;1
13;1;3;7;CPUE=a.V^b+c.E;3;model8.txt;1;3;1
14;4;3;10;CPUE=a+b.V+c.V^2+d.E;4;model11.txt;1;5;1
8;7;3;13;CPUE=a.V.exp(b.E);2;model14.txt;2;1;1
7;6;3;12;CPUE=(a+b.V).exp(c.E);3;model13.txt;2;1;1
9;14;3;20;CPUE=a.exp(b.E)+c.V+d;4;model21.txt;6;1;1
10;5;3;11;CPUE=a.V^b.exp(c.E);3;model12.txt;2;3;1
28;22;3;28;CPUE=a.V^b.exp(c.V^d.E);4;model29.txt;2;3;1
17;15;3;21;CPUE=(a.V+b.V^2).exp(c.E);3;model22.txt;2;5;1
15;18;3;22;CPUE=((a.V^b)+d.E)^(1/(c-1));4;model23.txt;3;3;1
16;19;3;23;CPUE=((a.V+b.V^2)^(c-1)+d.E)^(1/(c-1));4;model24.txt;3;5;1
19;10;3;16;CPUE=a.V+b.V^2.E;2;model17.txt;1;1;2
18;8;3;14;CPUE=a+b.V+c.(a+b.V)^2.E;3;model15.txt;1;1;2
20;9;3;15;CPUE=a.V^b+c.V^(2.b).E;3;model16.txt;1;3;2
22;13;3;19;CPUE=a.V.exp(b.V.E);2;model20.txt;2;1;2
23;12;3;18;CPUE=(a+b.V).exp(c.(a+b.V).E);3;model19.txt;2;1;2
24;11;3;17;CPUE=a.V^b.exp(c.V^b.E);3;model18.txt;2;3;2
21;16;3;24;CPUE=a.V.(b-c.V)+d.V^2.(b-c.V)^2.E;4;model25.txt;1;5;2
25;17;3;25;CPUE=a.V.(1+b.V).exp(c.V.(1+b.V).E);3;model26.txt;2;5;2
26;20;3;26;CPUE=a.V^(b+c)+d.V^(2.b).E;4;model27.txt;1;1;3
27;21;3;27;CPUE=a.V^(1+b)+c.V^(2+b)+d.V^(2.b).E;4;model28.txt;1;5;3
29;23;3;29;CPUE=a.V^b.exp(c.V^d.E);4;model30.txt;2;1;3
30;24;3;30;CPUE=(a.V^(1+b)+c.V^(2+b)).exp(d.V^b.E);4;model31.txt;2;5;3
31;20;3;26;CPUE=a.V^(b+c)+d.V^(2.b).E;4;model27.txt;1;3;3
32;23;3;29;CPUE=a.V^b.exp(E.c.V^d);4;model30.txt;2;3;3
33;25;2;33;CPUE=a.V.exp(b.V);2;model33.txt;0;5;0
34;26;3;34;CPUE=a.V.exp(b.V)+c.E;3;model34.txt;1;5;1
36;28;3;36;CPUE=a.V.exp(b.V).exp(c.E);3;model36.txt;2;5;1
38;30;3;38;CPUE=a.V.exp(b.V).exp(c.V.exp(b.V).E);3;model38.txt;2;5;2
39;31;3;39;CPUE=a.V.exp(b.V)+c.(a.V.exp(b.V))^2).E;3;model39.txt;1;5;2
32;23;3;29;CPUE=a.V^b.exp(E.c.V^d);4;model30.txt;2;3;3
0;-1;1;1;CPUE=a.exp(b.E);2;model2.txt;2;0;0
1;-2;1;0;CPUE=a+b.E;2;model1.txt;1;0;0
2;-3;1;6;CPUE=(a+b.E)^(1/(c-1));3;model7.txt;3;0;0
3;-4;2;2;CPUE=a+b.V;2;model3.txt;0;1;0
4;-5;2;3;CPUE=a.V^b;2;model4.txt;0;4;0
5;-6;2;4;CPUE=a+b.V^c;3;model5.txt;0;3;0
6;-7;2;5;CPUE=a+b.V+c.V^2;3;model6.txt;0;5;0
12;3;3;9;CPUE=a.V+b.E;2;model10.txt;1;1;1
11;2;3;8;CPUE=a+b.V+c.E;3;model9.txt;1;1;1
13;1;3;7;CPUE=a.V^b+c.E;3;model8.txt;1;3;1
14;4;3;10;CPUE=a+b.V+c.V^2+d.E;4;model11.txt;1;5;1
8;7;3;13;CPUE=a.V.exp(b.E);2;model14.txt;2;1;1
7;6;3;12;CPUE=(a+b.V).exp(c.E);3;model13.txt;2;1;1
9;14;3;20;CPUE=a.exp(b.E)+c.V+d;4;model21.txt;6;1;1
10;5;3;11;CPUE=a.V^b.exp(c.E);3;model12.txt;2;3;1
28;22;3;28;CPUE=a.V^b.exp(c.V^d.E);4;model29.txt;2;3;1
17;15;3;21;CPUE=(a.V+b.V^2).exp(c.E);3;model22.txt;2;5;1
15;18;3;22;CPUE=((a.V^b)+d.E)^(1/(c-1));4;model23.txt;3;3;1
16;19;3;23;CPUE=((a.V+b.V^2)^(c-1)+d.E)^(1/(c-1));4;model24.txt;3;5;1
19;10;3;16;CPUE=a.V+b.V^2.E;2;model17.txt;1;1;2
18;8;3;14;CPUE=a+b.V+c.(a+b.V)^2.E;3;model15.txt;1;1;2
20;9;3;15;CPUE=a.V^b+c.V^(2.b).E;3;model16.txt;1;3;2
22;13;3;19;CPUE=a.V.exp(b.V.E);2;model20.txt;2;1;2
23;12;3;18;CPUE=(a+b.V).exp(c.(a+b.V).E);3;model19.txt;2;1;2
24;11;3;17;CPUE=a.V^b.exp(c.V^b.E);3;model18.txt;2;3;2
21;16;3;24;CPUE=a.V.(b-c.V)+d.V^2.(b-c.V)^2.E;4;model25.txt;1;5;2
25;17;3;25;CPUE=a.V.(1+b.V).exp(c.V.(1+b.V).E);3;model26.txt;2;5;2
26;20;3;26;CPUE=a.V^(b+c)+d.V^(2.b).E;4;model27.txt;1;1;3
27;21;3;27;CPUE=a.V^(1+b)+c.V^(2+b)+d.V^(2.b).E;4;model28.txt;1;5;3
29;23;3;29;CPUE=a.V^b.exp(c.V^d.E);4;model30.txt;2;1;3
30;24;3;30;CPUE=(a.V^(1+b)+c.V^(2+b)).exp(d.V^b.E);4;model31.txt;2;5;3
31;20;3;26;CPUE=a.V^(b+c)+d.V^(2.b).E;4;model27.txt;1;3;3
32;23;3;29;CPUE=a.V^b.exp(E.c.V^d);4;model30.txt;2;3;3
33;25;2;33;CPUE=a.V.exp(b.V);2;model33.txt;0;5;0
34;26;3;34;CPUE=a.V.exp(b.V)+c.E;3;model34.txt;1;5;1
36;28;3;36;CPUE=a.V.exp(b.V).exp(c.E);3;model36.txt;2;5;1
37;29;3;37;CPUE=a.V.exp(b.V)+c.(V.exp(b.V))^2).E;3;model37.txt;1;5;2
38;30;3;38;CPUE=a.V.exp(b.V).exp(c.V.exp(b.V).E);3;model38.txt;2;5;2
39;31;3;39;CPUE=a.V.exp(b.V)+c.(a.V.exp(b.V))^2).E;3;model39.txt;1;5;2
MODEL #0 CPUE = a + b E
This is the conventional linear surplus production model, usually
named "Schaefer model" or "Graham-Schaefer model".
It does not take into account any environmental influence on the
production (white noise).

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MODEL: CPUE = a + b E
MODEL #1 CPUE = a exp(b E)
This is the conventional linear surplus production model, usually
named "Schaefer model" or "Graham-Schaefer model".
This is the conventional exponential surplus production model, usually
named "Fox model".
It does not take into account any environmental influence on the
production (white noise).
......
MODEL: CPUE = a V + b E
MODEL #10 CPUE = a V^b + c E
This is a surplus production model combining on the one hand a linear
relationship between the CPUE and the fishing effort E, and on the other
hand a linear relationship between the CPUE and the environmental variable V.
This latter variable is supposed to only influence the abundance of the
stock (surplus production) and not the catchability coefficient.
hand a power relationship between the CPUE and the environmental variable V.
This latter variable is supposed to only influence the abundance of the stock
(surplus production) and not the catchability coefficient.
The particularity of this model is the absence of an intercept parameter
(constant term a) in its equation. After fitting this model, if the fit is
not good it is suggested to try fitting and validating the following related
model which includes an intercept parameter but, as a result, has fewer degrees
of freedom:
After fitting this model, if the value of the parameter b is not
significantly different from one, it is suggested to try one of the
following models:
CPUE = a + b V + c E.
#8 CPUE = a + b V + c E or
For doing this, please use click on "Modelization" and then on
"Fit a model directly" in the main menu of CLIMPROD.
#9 CPUE = aV + bE
If you are presently using the mode "Select the appropriate model and fit it"
of CLIMPROD, you can do this shift of model by clicking on "Modelization" and
then on "Fit a model directly" in the main menu of CLIMPROD.
Then it is highly recommanded to assess the relevance of any of these shifts
of model by using the AIC and BIC results available in CLIMPROD. The model with
the lowest criterion value is the most suitable.

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MODEL: CPUE = a + bV + cV^2 + dE
MODEL #11 CPUE = a + bV + cV^2 + dE
This is a surplusproduction model combining on the one hand a linear
relationship between the CPUE and the fishing effort E, and on the other
......@@ -10,7 +10,11 @@ This kind of non-monotonic equation is useful when the relationship between
CPUE and V is shaped (optimal central value). The graph MSY versus V must
OBVIOUSLY be parabolic. If not, the data-set does not justify such a model
and it is recommanded to use a model with less parameters and more degree
of freedom (CPUE = a+bV+cE or CPUE = aV^b+cE for instance).
of freedom as for instance models:
#8 CPUE = a+bV+cE or
#10 CPUE = aV^b+cE.
Moreover, when using these formulae in a non-equilibrium condition
(transitional state) the user must carefully check the temporal stability
......@@ -18,8 +22,16 @@ of the V variable on the time-series graph. In particular, it would be
nonsense to allow the program to average two V values with one located
on the left side of the parabola and the other on the right.
If the fit is not good, it is suggested to fit a model where the
more flexible Ricker type equation is used to describe the relation-
ship between CPUE and V: CPUE = a V exp(bV) + c E. Unfortunately, these
equations are not available in CLIMPROD.

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If the fit is not good, it is suggested to fit a model where the more
flexible Ricker type equation is used to describe the relationship between
CPUE and V:
#12 CPUE = a V exp(bV) + c E.
If you are presently using the mode "Select the appropriate model and fit it"
of CLIMPROD, you can do these shifts of model by clicking on "Modelization" and
then on "Fit a model directly" in the main menu of CLIMPROD.
Then it is highly recommanded to assess the relevance of the shift to
model #12 by using the AIC and BIC results available in CLIMPROD. The
lower is the criterion value, the more suitable is the model.
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MODEL: CPUE = aV^b exp(cE)
MODEL #12 CPUE = aV exp(bV) + cE
This is a production model combining, in a multiplicative way, on the one
hand an exponential relationship between the CPUE and the fishing effort E,
and on the other a power relationship between the CPUE and the environmental
variable V. This latter variable is supposed to only influence the abundance
of the stock (surplus production) and not the catchability coefficient. In
this model, MSE constant and therefore independent of V.
This is a surplus production model combining on the one hand a linear
relationship between the CPUE and the fishing effort E (Graham-Schaefer model),
and on the other hand a non-linear relationship between the CPUE and the environmental
variable V. The shape of this non-linear relationship is quite flexible (it is in fact
the same equation as the one used by Ricker to describe stock-recruitment relationships).
After fitting this model, if the value of the parameter b is not
significantly different from one, it is suggested to try the following
model which has more degrees of freedom: CPUE = a V exp(b E), or another
one which has a constant term and the same degrees of freedom:
Variable V is supposed to only influence the abundance of the
stock (surplus production) and not the catchability coefficient.
CPUE = (a + bV) exp(cE).
This kind of non-monotonic equation is useful when the relationship between
CPUE and V is shaped (optimal central value). The graph MSY versus V must
OBVIOUSLY be parabolic. If not, the data-set does not justify such a model
and it is recommanded to use a model with less parameters and more degree
of freedom as for instance models:
Another option is to intent the fit of the following model in which MSE = f(V):
#8 CPUE = a+bV+cE or
CPUE = aV^b exp(cV^dE)
#10 CPUE = aV^b+cE.
Moreover, when using these models in a non-equilibrium condition for the variable
V (transitional state*) the user must carefully check the temporal stability
of the variable V on the time-series graph. In particular, it would be
nonsense to allow the program to average two V values with on located one
on the left-hand side of the parabola and the other on the right-hand side.
If the fit is not good, it is suggested to fit the following model where the
relationship between CPUE and V is quadratic (parabola):
#11 CPUE = a + bV + cV^2 + dE.
If you are presently using the mode "Select the appropriate model and fit it"
of CLIMPROD, you can do these shifts of model by clicking on "Modelization" and
then on "Fit a model directly" in the main menu of CLIMPROD.
Then it is highly recommanded to assess the relevance of this shift to model #11
by using the AIC and BIC results available in CLIMPROD. The model with the lowest
criterion value is the most suitable.
*That is when V is averaged, which is the case when the age at beginning
and/or the end of environmental influence is different from 1.

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MODEL: CPUE = (a + b V) exp(c E)
MODEL #13 CPUE = a V exp(b E)
This is a surplus production model combining, in a multiplicative way, on the
one hand an exponential relationship between the CPUE and the fishing effort
E, and on the other hand a linear relationship between the CPUE and the
environmental variable V. This latter variable is supposed to only influence
the abundance of the stock (surplus production) and not the catchability
coefficient.
This is a surplus production model combining, in a multiplicative way,
on the one hand an exponential relationship between the CPUE and the fishing
effort E, and on the other hand a linear relationship between the CPUE
and the environmental variable V. This last variable is supposed to
only influence the abundance of the stock (surplus production) and not
the catchability coefficient.
After fitting this model, if the value of the parameter a is not significantly
different from zero, it is suggested to try fitting and validating the following
model which has no intercept parameter (constant term a) but, as a result, has
more degrees of freedom:
The particularity of this model is the absence of intercept parameter
(constant term) in its equation. After fitting this model, if the fit is
not good it is suggested to try fitting and validating the following
model which includes an intercept term (a) but, as a result, has fewer
degrees of freedom:
CPUE = a V exp(b E).
#14 CPUE = (a + b V) exp(c E).
For doing this, please use click on "Modelization" and then on
"Fit a model directly" in the main menu of CLIMPROD.
If you are presently using the mode "Select the appropriate model and fit it"
of CLIMPROD, you can do this shift of model by clicking on "Modelization"
and then on "Fit a model directly" in the main menu of CLIMPROD.
Then it is highly recommanded to assess the relevance of this shift of
model by using the AIC and BIC results available in CLIMPROD. The model
with the lowest criterion value is the most suitable.

\ No newline at end of file
MODEL: CPUE = a V exp(b E)
MODEL #14 CPUE = (a + b V) exp(c E)
This is a surplus production model combining, in a multiplicative way,
on the one hand an exponential relationship between the CPUE and the fishing
effort E, and on the other hand a linear relationship between the CPUE
and the environmental variable V. This last variable is supposed to
only influence the abundance of the stock (surplus production) and not
the catchability coefficient.
This is a surplus production model combining, in a multiplicative way, on the
one hand an exponential relationship between the CPUE and the fishing effort
E, and on the other hand a linear relationship between the CPUE and the
environmental variable V. This latter variable is supposed to only influence
the abundance of the stock (surplus production) and not the catchability
coefficient.
The particularity of this model is the absence of intercept parameter
(constant term) in its equation. After fitting this model, if the fit is
not good it is suggested to try fitting and validating the following
model which includes an intercept term (a) but, as a result, has fewer
degrees of freedom:
After fitting this model, if the value of the parameter a is not significantly
different from zero, it is suggested to try fitting and validating the following
model which has no intercept parameter (constant term a) but, as a result, has
more degrees of freedom:
CPUE = (a + b V) exp(c E).
For doing this, please use click on "Modelization" and then on
"Fit a model directly" in the main menu of CLIMPROD.
#13 CPUE = a V exp(b E).
Then it is highly recommanded to assess the relevance of this shift of
model by using the AIC and BIC results available in CLIMPROD. The model
with the lowest criterion value is the most suitable.

\ No newline at end of file
MODEL: CPUE = a + bV - c(a+bV)^2 E
MODEL #15 CPUE = a exp(bE) + cV + d
This is a surplus production model combining on the one hand a linear
relationship between the CPUE and the fishing effort E, and on the other a
linear relationship between the CPUE and the environmental variable V.
This latter variable is supposed to only influence the catchability
coefficient and not the stock abundance (surplus production).
This is a surplus production model combining, in an additive way, on the one
hand an exponential relationship between the CPUE and the fishing effort
E, and on the other hand a linear relationship between the CPUE and the
environmental variable V. This latter variable is supposed to only influence
the stock abundance (surplus production) and not the catchability
coefficient.
After fitting this model, if the value of the parameter a is not
significantly different from zero, it is suggested to try the
following model which has no constant term but more degrees of freedom:
It is suggested to use it when the user suspects that at high levels of
exploitation the stock is still able to present a high variability in
the CPUE according to fluctuations of V.
CPUE = a V + b V^2 E.
Owing to the additive combination mentioned above, in some instances the
model having the best fit can show an abnormal behaviour: when the
fishing effort E increases, the resulting production (Y = CPUE * E) may
increase exponentialy and MSY & MSE values tend to infinity (plot not available).
In such a case, the solution is to use a regression under constraint on
parameter b (option not available in this CLIMPROD version) or to use
one of the following multiplicative models:
#13 CPUE = a V exp(b E)
#14 CPUE = (a + b V) exp(c E).
If you are presently using the mode "Select the appropriate model and fit it"
of CLIMPROD, you can do these shifts of model by clicking on "Modelization" and
then on "Fit a model directly" in the main menu of CLIMPROD.

\ No newline at end of file
MODEL: CPUE = a V^b + c V^(2 b) E
MODEL #16 CPUE = aV^b exp(cE)
This is a surplus production model combining on the one hand a linear
relationship between the CPUE and the fishing effort E, and on the other a
power relationship between the CPUE and the environmental variable V.
This latter variable is supposed to only influence the catchability
coefficient and not the stock abundance (surplus production).
This is a production model combining, in a multiplicative way, on the one
hand an exponential relationship between the CPUE and the fishing effort E,
and on the other a power relationship between the CPUE and the environmental
variable V. This latter variable is supposed to only influence the abundance
of the stock (surplus production) and not the catchability coefficient. In
this model, MSE constant and therefore independent of V.
After fitting this model, if the value of the parameter b is not
significantly different from one, it is suggested to try the following
model which has more degrees of freedom:
#13 CPUE = a V exp(b E),
or another one which has a constant term and the same degrees of freedom:
#14 CPUE = (a + bV) exp(cE).
Another option is to intent the fit of the following model in which MSE = f(V):
#17 CPUE = aV^b exp(cV^dE).
If you are presently using the mode "Select the appropriate model and fit it"
of CLIMPROD, you can do these shifts of model by clicking on "Modelization" and
then on "Fit a model directly" in the main menu of CLIMPROD.
Then it is highly recommanded to assess the relevance of any of these shifts
of model by using the AIC and BIC results available in CLIMPROD. The model
with the lowest criterion value is the most suitable. Nonetheless, model #17
might be retain on the sole base that MSE = f(V) seems more relevant than a
constant MSE.

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