Nonlinear (weighted) least-squares example in R

nls.R

df <- read.csv("data.csv")

# Column as 'array'
x <- df$col1
y <- df$col2

# Random seed
# set.seed(20170227)

a_start <- 5000
b_start <- 0.5

m1 <- nls(y ~ a*(x - 0.5)^8, start=list(a=a_start), control=list(maxiter=5000))

# m2 <- nls(y ~ a*(2*(x - 0.5))^20, start=list(a=a_start), control=list(maxiter=5000))
m2 <- nls(y ~ a*(2*(x - b))^14, start=list(a=a_start, b=b_start), control=list(maxiter=5000))

y_test = 15000*(2*(x - 0.5))^20

# Fit params
m1
m2

# Get some estimation of fits
cor(y, predict(m1))
cor(y, y_test)
cor(y, predict(m2))

# Plot
plot(x, y)

# Params: lty - dash size, lwd - line width
lines(x, predict(m1), lty=2, lwd=3, col="red")
lines(x, predict(m2), lty=2, lwd=3, col="blue")
lines(x, y_test, lty=2, lwd=3, col="gray")