mlgp function

Impute missing data.

This function performs maximum likelihood estimation to estimate the variance parameters in a Gaussian process with a squared exponential covariance function. These parameters could then be used in the Gaussian process used for imputation.

mlgp(y, x, tol = 1e-06)

Arguments

• y: Numeric y vector of response values.
• x: Numeric x vector of locations used for the covariance function.
• tol: Tolerance level for the maximum likelihood procedure to fit the Gaussian process.

Returns

Standard optim output. The first optimized parameter value is the standard deviation the second is the length scale.

Examples

# Fake data
sim_groove <- function(beta = c(-0.28,0.28), a = 125)
{
    x <- seq(from = 0, to = 2158, by = 20)
    med <- median(x)
    y <- 1*(x <= a)*(beta[1]*(x - med) - beta[1]*(a - med)) +
    1*(x >= 2158 - a)*(beta[2]*(x - med) - beta[2]*(2158 - a - med))
    return(data.frame("x" = x, "y" = y))
}

fake_groove <- sim_groove()
fake_groove <- fake_groove[sample.int(n = nrow(fake_groove),
    size = round(0.8 * nrow(fake_groove)),
    replace = FALSE),]
plot(fake_groove$x, fake_groove$y)

# estimate the MLE's
mles <- mlgp(y = fake_groove$y, x = fake_groove$x)