Description Usage Arguments Examples
lomb_scargle
applies the Lomb Scargle method for performing weighted nonlinear least squares in a single band using magnitude measurements recorded at irregular intervals.
1 | lomb_scargle(t, m, sigma, omega, weights_flag = TRUE)
|
t |
times |
m |
magnitudes |
sigma |
errors |
omega |
frequency |
weights_flag |
boolean (TRUE to use inverse errors as weights; FALSE uses uniform weights) |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | n <- 1e3
set.seed(12345)
sigma <- runif(n)
t <- cumsum(runif(n))
A <- 2.3
rho <- 0.1
omega <- 0.2
beta0 <- 0.3
m <- beta0 + A*sin(omega*t + rho) + sigma*rnorm(n)
plot(t,m,xlab='time',ylab='magnitude',main='Simulated Light Curve',pch=16)
## Try several omega
nOmega <- 500
omega_seq <- seq(0.1,0.3,length=nOmega)
sol_ls <- vector(mode="list",length=nOmega)
RSS_seq <- double(nOmega)
for (i in 1:nOmega) {
sol_ls[[i]] <- lomb_scargle(t,m,sigma,omega_seq[i])
RSS_seq[i] <- sol_ls[[i]]$RSS
}
plot(omega_seq,RSS_seq,xlab=expression(omega),ylab='RSS',pch=16)
ix_min <- which(RSS_seq==min(RSS_seq))
sol_final <- sol_ls[[ix_min]]
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