# Bootstrap Lasso Partial Ridge Does residual (or paired) bootstrap Lasso+Partial Ridge and produces confidence intervals for regression coefficients. ```r bootLPR(x, y, lambda2, B = 500, type.boot = "paired", alpha = 0.05, OLS = TRUE, cv.method = "cv", nfolds = 10, foldid, cv.OLS = TRUE, tau = 0, parallel = FALSE, standardize = TRUE, intercept = TRUE, parallel.boot = FALSE, ncores.boot = 1, ...) ``` ## Arguments - `x`: Input matrix as in glmnet, of dimension nobs x nvars; each row is an observation vector. - `y`: Response variable. - `lambda2`: Tuning parameter in the Partial Ridge. If missing, lambda2 will be set to 1/nobs, where nobs is the number of observations. - `B`: Number of replications in the bootstrap -- default is 500. - `type.boot`: Bootstrap method which can take one of the following two values: "residual" or "paired". The default is residual. - `alpha`: Significance level -- default is 0.05. - `OLS`: If TRUE, this function uses Lasso+OLS estimator to compute the residuals for residual bootstrap Lasso+Partial Ridge; otherwise, it uses Lasso estimator to compute the residuals for residual bootstrap Lasso+Partial Ridge. The default value is TRUE. This argument can be ignored for paired bootstrap Lasso+Partial Ridge. - `cv.method`: The method used to select lambda in the Lasso -- can be cv, cv1se, and escv; the default is cv. - `nfolds, foldid, cv.OLS, tau, parallel`: Arguments that can be passed to escv.glmnet. - `standardize`: Logical flag for x variable standardization, prior to fitting the model. Default is standardize=TRUE. - `intercept`: Should intercept be fitted (default is TRUE) or set to zero (FALSE). - `parallel.boot`: If TRUE, use parallel foreach to run the bootstrap replication. Must register parallel before hand, such as doParallel or others. See the example below. - `ncores.boot`: Number of cores used in the bootstrap replication. - `...`: Other arguments that can be passed to glmnet. ## Details The function runs residual (type.boot="residual") or paired (type.boot="paired") bootstrap Lasso+Partial Ridge procedure, and produces confidence interval for each individual regression coefficient. If the argument OLS=TRUE, it uses Lasso+OLS estimator to compute the residuals for residual bootstrap Lasso+Partial Ridge; otherwise, it uses Lasso estimator to compute the residuals. Note that there are two arguments related to parallel, "parallel" and "parallel.boot": "parallel" is used for parallel foreach in the escv.glmnet; while, "paralle.boot" is used for the parallel foreach in the bootstrap replication precodure. ## Returns A list consisting of the following elements is returned. - **lambda.opt**: The optimal value of lambda selected by cv/cv1se/escv. - **Beta**: Lasso+OLS (if OLS=TRUE) or Lasso (if OLS=FALSE) estimate of the regression coefficients. - **Beta.LPR**: Lasso+Partial Ridge estimate of the regression coefficients. - **interval**: A 2 by p matrix containing the bootstrap Lasso+OLS (if OLS=TRUE) or bootstrap Lasso (if OLS=FALSE) confidence intervals -- the first row is the lower bounds of the confidence intervals for each of the coefficients and the second row is the upper bounds of the confidence intervals. - **interval.LPR**: A 2 by p matrix containing the bootstrap Lasso+Partial Ridge confidence intervals -- the first row is the lower bounds of the confidence intervals for each of the coefficients and the second row is the upper bounds of the confidence intervals. ## Examples ```r library("glmnet") library("mvtnorm") ## generate the data set.seed(2015) n <- 200 # number of obs p <- 500 s <- 10 beta <- rep(0, p) beta[1:s] <- runif(s, 1/3, 1) x <- rmvnorm(n = n, mean = rep(0, p), method = "svd") signal <- sqrt(mean((x %*% beta)^2)) sigma <- as.numeric(signal / sqrt(10)) # SNR=10 y <- x %*% beta + rnorm(n) ## residual bootstrap Lasso+Partial Ridge set.seed(0) obj <- bootLPR(x = x, y = y, type.boot = "residual", B = 10) # confidence interval obj$interval sum((obj$interval[1,]<=beta) & (obj$interval[2,]>=beta)) ## using parallel in the bootstrap replication #library("doParallel") #registerDoParallel(2) #set.seed(0) #system.time(obj <- bootLPR(x = x, y = y)) #system.time(obj <- bootLPR(x = x, y = y, parallel.boot = TRUE, ncores.boot = 2)) ```

bootLPR function