Dear R Helper Community!
Thank you for all your help and suggestions.
For your reference and any future person searching the archives, here is the
solution that did what I wanted it to do.
As background, my goal was to find the coefficients for the following
equation form:
y ~ c1 * x1 * x2 ^ -c2 *x3 ^ c3
where y, x1, x2, and x3 are observed data, and
c1, c2 and c3 are regression coefficients subject to the following
constraints:
0.0 <= c2 <= 1.0
0.0 <= c3 <= 1.0
I ran the following code as a script from the R Gui on a windows machine.
The command for this was: source("C:\\Users\\me\\R281\\work\\optimcall.R")
# **** Script file: optimcall.R ***********
# function definition
rss <- function(par) {
y <-
c(0.111111111111111,0.0921052631578947,0.0564516129032258,0.0608108108108108
,0.0128205128205128,0.0136078431372549)
x1 <-
c(0,0.978723404255319,0.087378640776699,0.549295774647887,0.0596026490066225
,0.61578947368421)
x2 <- c(1,3,4,5,6,7)
x3 <- c(3600,169200,185400,255600,271800,342000)
par1 <- par[1]
par2 <- par[2]
par3 <- par[3]
ressq <- (y - par1 * (x1 + 1) * x2^(-par2) * x3^par3)^2
sum(ressq)
}
#call to optimizer
opti <- optim(c(0.5, 0.5, 0.1), rss, gr = NULL, method = "L-BFGS-B", lower =
c(-Inf, 0, 0), upper = c(Inf, 1, 1) )
# ***** End script file *********
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