## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(collapse = TRUE, comment = "#>", fig.width = 6, fig.height = 4)

## -----------------------------------------------------------------------------
library(biglasso)

data(colon)
X <- colon$X
y <- colon$y
dim(X)

## -----------------------------------------------------------------------------
X[1:5, 1:5]

## -----------------------------------------------------------------------------
# convert X to a big.matrix object
# X.bm is a pointer to the data matrix
X.bm <- as.big.matrix(X)
str(X.bm)

## -----------------------------------------------------------------------------
dim(X.bm)

## -----------------------------------------------------------------------------
X.bm[1:5, 1:5]

## -----------------------------------------------------------------------------
# fit entire solution path
fit <- biglasso(X.bm, y)
plot(fit)

## -----------------------------------------------------------------------------
cvfit <- cv.biglasso(X.bm, y, seed = 1234, nfolds = 10)

## -----------------------------------------------------------------------------
par(mfrow = c(2, 2), mar = c(3.5, 3.5, 3, 1), mgp = c(2.5, 0.5, 0))
plot(cvfit, type = "all")

## -----------------------------------------------------------------------------
summary(cvfit)

## -----------------------------------------------------------------------------
coef(cvfit)[which(coef(cvfit) != 0), ]

## -----------------------------------------------------------------------------
data(Heart)
X <- Heart$X
y <- Heart$y
X.bm <- as.big.matrix(X)
fit <- biglasso(X.bm, y, family = "binomial")
plot(fit)

## -----------------------------------------------------------------------------
library(survival)
X <- heart[, 4:7]
y <- Surv(heart$stop - heart$start, heart$event)
X.bm <- as.big.matrix(X)
fit <- biglasso(X.bm, y, family = "cox")
plot(fit)

## -----------------------------------------------------------------------------
set.seed(10101)
n <- 300
p <- 300
m <- 5
s <- 10
b <- 1
x <- matrix(rnorm(n * p), n, p)
beta <- matrix(seq(from = -b, to = b, length.out = s * m), s, m)
y <- x[, 1:s] %*% beta + matrix(rnorm(n * m, 0, 1), n, m)
x.bm <- as.big.matrix(x)
fit <- biglasso(x.bm, y, family = "mgaussian")
plot(fit)

## -----------------------------------------------------------------------------
# The data has 200 observations, 600 features, and 10 non-zero coefficients.
# This is not actually very big, but vignettes in R are supposed to render
# quickly, so we'll have to pretend that this can't fit in memory.
# Much larger data can be handled in the same way.
if (!file.exists("BigX.bin")) {
  X <- matrix(rnorm(1000 * 5000), 1000, 5000)
  beta <- c(-5:5)
  y <- as.numeric(X[, 1:11] %*% beta)
  write.csv(X, "BigX.csv", row.names = FALSE)
  write.csv(y, "y.csv", row.names = FALSE)
  X.bm <- setupX("BigX.csv", header = TRUE)
}

## ----warning=F----------------------------------------------------------------
rm(list = c("X", "X.bm", "y")) # Pretend starting a new session
X.bm <- attach.big.matrix("BigX.desc")
y <- read.csv("y.csv")[, 1]

## -----------------------------------------------------------------------------
system.time({
  fit <- biglasso(X.bm, y)
})

## -----------------------------------------------------------------------------
plot(fit)

## -----------------------------------------------------------------------------
cvfit <- cv.biglasso(X.bm, y, seed = 1234, ncores = 2, nfolds = 10)

## -----------------------------------------------------------------------------
par(mfrow = c(2, 2), mar = c(3.5, 3.5, 3, 1), mgp = c(2.5, 0.5, 0))
plot(cvfit, type = "all")