pcls2_bias_variance_MSE: pcls2_bias_variance_MSE

In Jawad-Boulahfa/nnls2d: Comparison between the Non-negative least squares model and the multiple linear regression model (using the standard uniform distribution)

Description

Calculate the bias, the variance and the MSE of each component of the estimator of beta in the positively constrained least squares problem with 2 explanatory variables.

Usage

pcls2_bias_variance_MSE(X, beta = c(0, 1), sigma = 1)

Arguments

X	a dataframe containing the explanatory variables (2 columns)
beta	parameter vector
sigma	standard deviation of the Gaussian noise

Value

a dataframe containing the bias, the variance and the MSE of each component of the estimator of beta

Examples

results <- pcls2_bias_variance_MSE(X = data.frame(runif(3), runif(3)), beta = c(1,0.5), sigma = 2)

Jawad-Boulahfa/nnls2d documentation built on Sept. 3, 2020, 10:37 p.m.