CV.search.DP.regression {changepoints} R Documentation

## Grid search based on cross-validation of dynamic programming for regression change points localisation with l_0 penalisation.

### Description

Perform grid search to select tuning parameters gamma (for l_0 penalty of DP) and lambda (for lasso penalty) based on cross-validation.

### Usage

CV.search.DP.regression(y, X, gamma_set, lambda_set, delta, eps = 0.001)


### Arguments

 y A numeric vector of response variable. X A numeric matrix of covariates with vertical axis being time. gamma_set A numeric vector of candidate tuning parameters associated with l_0 penalty of DP. lambda_set A numeric vector of candidate tuning parameters for lasso penalty. delta A strictly positive integer scalar of minimum spacing. eps A numeric scalar of precision level for convergence of lasso.

### Value

A list with the following structure:

 cpt_hat A list of vector of estimated change points K_hat A list of scalar of number of estimated change points test_error A list of vector of testing errors (each row corresponding to each gamma, and each column corresponding to each lambda) train_error A list of vector of training errors

Daren Wang

### References

Rinaldo, Wang, Wen, Willett and Yu (2020) <arxiv:2010.10410>

### Examples

d0 = 10
p = 20
n = 100
cpt_true = c(30, 70)
data = simu.change.regression(d0, cpt_true, p, n, sigma = 1, kappa = 9)
gamma_set = c(0.01, 0.1, 1)
lambda_set = c(0.01, 0.1, 1, 3)
temp = CV.search.DP.regression(y = data$y, X = data$X, gamma_set, lambda_set, delta = 2)
temp$test_error # test error result # find the indices of gamma_set and lambda_set which minimizes the test error min_idx = as.vector(arrayInd(which.min(temp$test_error), dim(temp$test_error))) gamma_set[min_idx[1]] lambda_set[min_idx[2]] cpt_init = unlist(temp$cpt_hat[min_idx[1], min_idx[2]])


[Package changepoints version 1.1.0 Index]