I have three datasets that I want to compute the errors between them using
linear regression.for this, I want to iterate to reach certain criteria for
the calibration. if changes become smaller than eps the iteration is
successful, hence stop and write parameters into cal:eps=0.00001 if number
of iterations is > itermax the iteration failed, hence stop and fill cal
with missing value itermax=400
So I tried this code:
x= c(5,2,4,2,1)
y= c(5,3,4,6,9)
z= c(5,8,4,7,3)
itermax=400
get initial calibration parameters, here we assume that:x is the reference
dataset offset x_a=0, slope x_b=1 the other two datasets y, z are
"calibrated" to x using a simple linear regression
res=lm(x~y)
y_a=coef(res)[1] ; y_b=coef(res)[2]
res1=lm(x~z)
z_a=coef(res1)[1] ; z_b=coef(res1)[2]
y_t = y/y_b - y_a/y_b # "calibrate" y
z_t = z/z_b - z_a/z_b #"calibrate" z
x_e = sqrt(mean((x-y_t)*(x-z_t)))#calculate error of x
iter <- 0
while(((x_e-x) > 0.00001)&& (iter < itermax)) {
iter <- 0 ##start iteration
x = x_e
res=lm(x~y)
y_a=coef(res)[1] ; y_b=coef(res)[2]
res1=lm(x~z)
z_a=coef(res1)[1] ; z_b=coef(res1)[2]
y_t = y/y_b - y_a/y_b # "calibrate" y
z_t = z/z_b - z_a/z_b #"calibrate" z
x_e = sqrt(mean((x-y_t)*(x-z_t)))
iter <- iter + 1 # increase iteration counter
}
But I got the same result for X_e before and after the loop:
> x_e
[1] 6.454089
--
View this message in context:
http://r.789695.n4.nabble.com/problem-in-while-loop-tp4674962.html
Sent from the R help mailing list archive at Nabble.com.
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
