quick-start.R
In lsgl: Linear Multiple Output Sparse Group Lasso

## ---- echo = FALSE-------------------------------------------------------
set.seed(150)

## ----results="hide"------------------------------------------------------
library(lsgl)

## ----eval = FALSE--------------------------------------------------------
#  X <- # load design matrix (of size N x p)
#  Y <- # load response matrix (of size N x K)

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data(AirlineTicketPrices)
dim(X)
dim(Y)

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idx <- sample(1:nrow(X), size = 50)

Xtest <- X[idx, ]
Ytest <- Y[idx, ]
X <- X[-idx, ]
Y <- Y[-idx, ]

## ------------------------------------------------------------------------
cl <- makeCluster(2)
registerDoParallel(cl)

# Do cross validation -- this may take some time
fit.cv <- lsgl::cv(X, Y, fold = 10, alpha = 0.5, lambda = 0.01, use_parallel = TRUE)

stopCluster(cl)

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fit.cv

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fit <- lsgl::fit(X, Y, alpha = 0.5, lambda = 0.01)

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fit

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features(fit)[[best_model(fit.cv)]][1:10] # Ten first non-zero features in best model

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image(parameters(fit)[[best_model(fit.cv)]])

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coef(fit, best_model(fit.cv))[,1:5] # First 5 non-zero parameters of best model

## ----eval = FALSE--------------------------------------------------------
#  Xtest <- # load matrix with test data (of size M x p)

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res <- predict(fit, Xtest)

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image(Ytest, main = "Observed prices")
image(res$Yhat[[best_model(fit.cv)]], main = "Predicted prices")

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plot(Err(fit, Xtest, Ytest), xlab = "lambda index")