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

## Description

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.

## Usage

 `1` ```MLestimates(x) ```

## Arguments

 `x` A numeric vector.

## Details

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.

## Value

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

## Comment

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17``` ```# 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) ```

ROCit documentation built on July 1, 2020, 11:28 p.m.