My question is how to use loop(while) to get the final \"Tb\" and \"Theta_T\"?
Tmax = c(35.0,30.0,28.0,25.0,25.5,26.0)
Tmin = c(15.4,15.8,13.2,9.8,9.5,9.4)
X = c
as put in comments :
First run your code:
X = c(0.233,0.233,0.220,0.065,0.138,0.083)
D = c(12,25,46,41,45,62)
Ts = 40
To = 30
Tbo = 0
## calculate Theta_g by Tbo ##
if (Tbo<Tmin){
Theta_g = (Tmax+Tmin)/2
} else if (Tbo<Tmax & Tbo>Tmin){
Theta_g = (Tmax+Tmin)/2+(Tbo-Tmin)^2/2*(Tmax-Tmin)
} else
Theta_g = Tbo
##calculate Theta_a by Tbo ##
if (To>Tmin & Tmax<Ts){
Theta_a=(Ts-Tbo)*(Tmax-To)^2/(2*(Ts-To)*(Tmax-Tmin))
} else if (To>Tmin & Tmax>=Ts){
Theta_a=(Tmax*(Tmax-2*Tbo)-To*(Ts-Tbo)+Ts*Tbo)/(2*(Ts-To))
} else if (Tmin>=To & Tmin<Ts & Tmax<Ts){
Theta_a=(Ts-Tbo)*(Tmax+Tmin-2*To)/(2*(Tmax-Tmin))
} else if (Tmin>=To & Tmin<Ts & Tmax>Ts){
Theta_a=(Tmax+Tmin)/2-Tbo-(To-Tbo)*(Ts-Tmin)^2/(2*(Ts-To)*(Tmax-Tmin))
} else
Theta_a=(Tmax+Tmin)/2-Tbo
## Theta_s
Theta_s = (Theta_g-Theta_a)*D
## Linear formular: Theta_s/X=Theta_T+Tb(D/X)
y = Theta_s/X
x = D/X
##Least square linear regress simulation
Tb = sum((x-mean(x))*(y-mean(y)))/sum((x-mean(x))^2)
Theta_T = mean(y)-Tb*mean(x)
Then add the while loop to your code:
while ((Tb-Tbo)>=0.01) {
Tbo=Tb
## calculate Theta_g by Tbo ##
if (Tbo<Tmin){
Theta_g = (Tmax+Tmin)/2
} else if (Tbo<Tmax & Tbo>Tmin){
Theta_g = (Tmax+Tmin)/2+(Tbo-Tmin)^2/2*(Tmax-Tmin)
} else
Theta_g = Tbo
##calculate Theta_a by Tbo ##
if (To>Tmin & Tmax<Ts){
Theta_a=(Ts-Tbo)*(Tmax-To)^2/(2*(Ts-To)*(Tmax-Tmin))
} else if (To>Tmin & Tmax>=Ts){
Theta_a=(Tmax*(Tmax-2*Tbo)-To*(Ts-Tbo)+Ts*Tbo)/(2*(Ts-To))
} else if (Tmin>=To & Tmin<Ts & Tmax<Ts){
Theta_a=(Ts-Tbo)*(Tmax+Tmin-2*To)/(2*(Tmax-Tmin))
} else if (Tmin>=To & Tmin<Ts & Tmax>Ts){
Theta_a=(Tmax+Tmin)/2-Tbo-(To-Tbo)*(Ts-Tmin)^2/(2*(Ts-To)*(Tmax-Tmin))
} else
Theta_a=(Tmax+Tmin)/2-Tbo
## Theta_s
Theta_s = (Theta_g-Theta_a)*D
## Linear formular: Theta_s/X=Theta_T+Tb(D/X)
y = Theta_s/X
x = D/X
##Least square linear regress simulation
Tb = sum((x-mean(x))*(y-mean(y)))/sum((x-mean(x))^2)
Theta_T = mean(y)-Tb*mean(x)
}