GGMridge (version 1.4)

lambda.pcut.cv1: Choose the Tuning Parameter of the Ridge Inverse and Thresholding Level of the Empirical p-Values. Calculate total prediction error for test data after fitting partial correlations from train data for all values of lambda and pcut.

Description

Choose the Tuning Parameter of the Ridge Inverse and Thresholding Level of the Empirical p-Values.

Calculate total prediction error for test data after fitting partial correlations from train data for all values of lambda and pcut.

Usage

lambda.pcut.cv1(train, test, lambda, pcut)

Value

Total prediction error for all the candidate lambda and pvalue cutoff values.

Arguments

train

An n x p data matrix from which the model is fitted.

test

An m x p data matrix from which the model is evaluated.

lambda

A vector of candidate tuning parameters.

pcut

A vector of candidate cutoffs of pvalues.

Author

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]]
 
 ###############################
 # Split into train/test sets
 ###############################
 testindex <- sample(1L:n, 10L)
 
 train <- data[-testindex,,drop = FALSE]
 stdTrain <- scale(x = train, center = TRUE, scale = TRUE)
 
 test <- data[testindex,,drop = FALSE]
 stdTest <- scale(x = test, center = TRUE, scale = TRUE)
 
 ###############################
 # Calculate total prediction 
 # errors for all candidate 
 # lambda and p-value cutoffs
 ###############################
 lambda.array <- seq(from = 0.1, to = 5, length = 10) * (n - 1.0)
 pcut.array <- seq(from = 0.01, to = 0.05, by = 0.01)
 tpe <- lambda.pcut.cv1(train = stdTrain,
                        test = stdTest,
                        lambda = lambda.array,
                        pcut = pcut.array)