# ne.lambda.cv: Choose the Tuning Parameter of a Ridge Regression Using... In GGMridge: Gaussian Graphical Models Using Ridge Penalty Followed by Thresholding and Reestimation

 ne.lambda.cv R Documentation

## Choose the Tuning Parameter of a Ridge Regression Using Cross-Validation

### Description

Choose the tuning parameter of a ridge regression using cross-validation.

### Usage

` ne.lambda.cv(y, x, lambda, fold) `

### Arguments

 `y ` Length n response vector. `x ` n x p matrix for covariates with p variables and n sample size. `lambda ` A numeric vector for candidate tuning parameters for a ridge regression. `fold ` fold-cross validation used to choose the tuning parameter.

### Value

 `lambda ` The selected tuning parameter, which minimizes the prediction error. `spe ` The prediction error for all of the candidate lambda values.

Min Jin Ha

### References

Ha, M. J. and Sun, W. (2014). Partial correlation matrix estimation using ridge penalty followed by thresholding and re-estimation. Biometrics, 70, 762–770.

### Examples

```  p <- 100 # number of variables
n <- 50 # sample size

###############################
# Simulate data
###############################
simulation <- simulateData(G = p, etaA = 0.02, n = n, r = 1)
data <- simulation\$data[[1L]]
stddat <- scale(x = data, center = TRUE, scale = TRUE)

X <- stddat[,-1L,drop = FALSE]
y <- stddat[,1L]

fit.lambda <- ne.lambda.cv(y = y,
x = X,
lambda = seq(from = 0.01, to = 1,by = 0.1),
fold = 10L)

lambda <- fit.lambda\$lambda[which.min(fit.lambda\$spe)]
```

GGMridge documentation built on June 7, 2022, 5:07 p.m.