# 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

## Description

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

## Usage

 `1` ``` 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

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ``` 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)] ```

### Example output

```Loading required package: mvtnorm
Loading required package: MASS

GGMridge was developed in support of IMPACT, a comprehensive research
program that aims to improve the health and longevity of people by
improving the clinical trial process. To learn more about our
research and available software visit www.impact.unc.edu.
```

GGMridge documentation built on May 2, 2019, 12:53 p.m.