estimateVariance: Estimate noise variance given predictor X and response Y.

In ChubingZeng/classo: Regularized Regression with Differential Penalties Integrating External Information

Description

estimateVariance estimate noise variance.

Usage

estimateVariance(X, Y, n_rep = 5)

Arguments

X	predictor matrix of dimension n by p.
Y	continuous outcome vector of length n.
n_rep	number of repeated estimation. Default is 10.

Details

The estimateSigma function from selectiveInference is used repeatedly to estimate noise variance.

References

Stephen Reid, Jerome Friedman, and Rob Tibshirani (2014). A study of error variance estimation in lasso regression. arXiv:1311.5274.

See Also

selectiveInference

Examples

## simulate some data
set.seed(9)
n = 30
p = 10
sigma.square = 1
X = matrix(rnorm(n*p),n,p)
beta = c(2,-2,1,-1,rep(0,p-4))
Y = X%*%beta + rnorm(n,0,sqrt(sigma.square))

## estimate sigma square
sigma.square.est = estimateVariance(X,Y)
sigma.square.est

ChubingZeng/classo documentation built on June 4, 2019, 12:37 p.m.