mse: Mean Squared Error (MSE)

In coatless/balamuta: Balamuta Miscellaneous

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)

coatless/balamuta documentation built on Nov. 16, 2023, 5:30 a.m.