mse {jjb}

Mean Squared Error (MSE)

Description

Calculates the mean square of the model by taking the mean of the sum of squares between the truth, y, and the predicted, \hat{y} at each observation i.

Usage

mse(y, yhat)

Arguments

y

A vector of the true y values

yhat

A vector of predicted \hat{y} values.

Details

The equation for MSE is:

\frac{1}{n}\sum\limits_{i = 1}^n {{{\left( {{y_i} - {{\hat y}_i}} \right)}^2}}

Value

The MSE in numeric form.

Examples

# Set seed for reproducibility
set.seed(100)

# Generate data
n = 1e2

y = rnorm(n)
yhat = rnorm(n, 0.5)

# Compute
o = mse(y, yhat)