MLestimates: ML Estimate of Normal Parameters
In ROCit: Performance Assessment of Binary Classifier with Visualization

Description Usage Arguments Details Value Comment Examples

The function calculates the maximum likelihood (ML) estimates of the two parameters μ and σ, when a set of numbers are assumed to be normally distributed.

1	MLestimates(x)

`x`	A numeric vector.

If a set of observations are assumed to be normally distributed, two parameters, mean μ and the variance (the square of σ) are to be estimated. In theory, the ML estimate of μ is the mean of the observations. And the ML estimate of square of σ is the mean squared deviation of the observations from the estimated μ.

A "list" object of two numeric components, μ and σ.

MLestimates is used internally in other function(s) of ROCit.

# Find the two parameters
set.seed(10)
points <- rnorm(200, 10, 5)
ML <- MLestimates(points)
message("The ML estimates are: mean = ", round(ML$mu, 3),
        " , SD = ", round(ML$sigma, 3))

#-----------------------------------------

# Superimpose smooth curve over hostogram
set.seed(100)
x <- rnorm(400)
hist(x, probability = TRUE, col = "gray90")
ML <- MLestimates(x)
x <- seq(-3, 3, 0.01)
density <- dnorm(x, mean = ML$mu, sd = ML$sigma)
lines(density~x, lwd = 2)