knitr::opts_chunk$set(echo = TRUE, cache = TRUE, tidy = TRUE)
This is a short effort to give users an idea of how long the functions take to process. The benchmarks were performed using the default R install on Travis CI.
We will be estimating a tri-diagonal precision matrix with dimension $p = 100$:
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library(CVglasso) library(microbenchmark) # generate data from tri-diagonal (sparse) matrix # compute covariance matrix (can confirm inverse is tri-diagonal) S = matrix(0, nrow = 100, ncol = 100) for (i in 1:100){ for (j in 1:100){ S[i, j] = 0.7^(abs(i - j)) } } # generate 1000 x 100 matrix with rows drawn from iid N_p(0, S) set.seed(123) Z = matrix(rnorm(1000*100), nrow = 1000, ncol = 100) out = eigen(S, symmetric = TRUE) S.sqrt = out$vectors %*% diag(out$values^0.5) %*% t(out$vectors) X = Z %*% S.sqrt # calculate sample covariance matrix sample = (nrow(X) - 1)/nrow(X)*cov(X)
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# benchmark CVglasso - defaults microbenchmark(CVglasso(S = sample, lam = 0.1, trace = "none"))
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# benchmark CVglasso - tolerance 1e-6 microbenchmark(CVglasso(S = sample, lam = 0.1, tol = 1e-6, trace = "none"))
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lam
:
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# benchmark CVglasso CV - default parameter grid microbenchmark(CVglasso(X, trace = "none"), times = 5)
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cores = 2
) cross validation:
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# benchmark CVglasso parallel CV microbenchmark(CVglasso(X, cores = 2, trace = "none"), times = 5)
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