Modèles Nonlinéaires
- Modele Adbudg, lègèrement plus sensible à la publicité qu'à la force de vente [ 2.21]
- Modele Adbudg, lègèrement moins sensible à la force de vente qu'à la publicité[ 2.21_2]
1. a=10
2. b=0
3. c=2
4. d=2
5. x<-seq(0,10,1)
6. y<-b+(a-b)*((x)^c/(d^c+(x)^c))
7. df<-data.frame(Effort=x, Ventes.ForceV=y)
8. d=1.5
9. y<-b+(a-b)*((x)^c/(d^c+(x)^c))
10. df$Ventes.Pub=y
11. df
12. matplot(x, df[,2:3], pch = 1:2, type = "o", col = 1:2,xlab="Valeurs de x", ylab="Ventes et/ou Profits"
13. legend(min(x), max(df[,2:3]),names(df)[2:3], lwd=3, col=1:2, pch=1:2)
- Modèle à deux variables sans interactions [ 2.23]
1. a=10
2. b=0
3. c=2
4. d1=2
5. d2=1.5
6. x1<-seq(0,9,1) # varie
7. x2<-rep(0,10) # fix
8. g1<-b+(a-b)*((x1)^c/(d1^c+(x1)^c))
9. g2<-b+(a-b)*((x2)^c/(d2^c+(x2)^c))
10. y<-15+1*g1+1.6*g2
11. df<-data.frame(Effort=x1, Ventes.Pub=y)
12. x1<-rep(0,10) # fix
13. x2<-seq(0,9,1) # varie
14. g1<-b+(a-b)*((x1)^c/(d1^c+(x1)^c))
15. g2<-b+(a-b)*((x2)^c/(d2^c+(x2)^c))
16. y<-15+1*g1+1.6*g2
17. df$Ventes.ForceV=y
18. df
19. matplot(x2, df[,2:3], pch=1:2, type = "o", col = 1:2, xlab="Valeurs de x", ylab="Ventes et/ou Profits")
20. legend(min(x), max(df[,2:3]),names(df)[2:3], lwd=3, col=1:2, pch=1:2)