## ----include = FALSE----------------------------------------------------------
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)

## ----echo=TRUE----------------------------------------------------------------
require(ptLasso)

## -----------------------------------------------------------------------------
set.seed(1234)

# Define constants
n = 600          # Total number of samples
ntrain = 300     # Number of training samples
p = 100          # Number of features
sigma = 3        # Standard deviation of noise

# Generate covariate matrix
x = matrix(rnorm(n * p), n, p)

# Define coefficients for time points 1 and 2
beta1 = c(rep(2, 10), rep(0, p - 10))  # Coefs at time 1
beta2 = runif(p, 0.5, 2) * beta1       # Coefs at time 2, shared support with time 1

# Generate response variables for times 1 and 2
y = cbind(
  x %*% beta1 + sigma * rnorm(n),
  x %*% beta2 + sigma * rnorm(n)
)

# Split data into training and testing sets
xtest = x[-(1:ntrain), ]  # Test covariates
ytest = y[-(1:ntrain), ]  # Test response

x = x[1:ntrain, ]  # Train covariates
y = y[1:ntrain, ]  # Train response

## -----------------------------------------------------------------------------
fit = ptLasso(x, y, use.case = "timeSeries", alpha = 0)
plot(fit)

## -----------------------------------------------------------------------------
preds = predict(fit, xtest, ytest = ytest)
preds

## -----------------------------------------------------------------------------
cvfit = cv.ptLasso(x, y, use.case = "timeSeries")
plot(cvfit)
predict(cvfit, xtest, ytest = ytest)

## -----------------------------------------------------------------------------
fit = ptLasso(x, y, use.case = "multiresponse")

## -----------------------------------------------------------------------------
set.seed(1234)  # Set seed for reproducibility

# Define constants
n = 600          # Total number of samples
ntrain = 300     # Number of training samples
p = 100          # Number of features
sigma = 3        # Standard deviation of noise

# Covariates for times 1 and 2
x1 = matrix(rnorm(n * p), n, p)
x2 = x1 + matrix(0.2 * rnorm(n * p), n, p)  # Perturbed covariates for time 2
x = list(x1, x2)

# Define coefficients for time points 1 and 2
beta1 = c(rep(2, 10), rep(0, p - 10))  # Coefs at time 1
beta2 = runif(p, 0.5, 2) * beta1       # Coefs at time 2, shared support with time 1

# Response variables for times 1 and 2:
y = cbind(
  x[[1]] %*% beta1 + sigma * rnorm(n),
  x[[2]] %*% beta2 + sigma * rnorm(n)
)

# Split data into training and testing sets
xtest = lapply(x, function(xx) xx[-(1:ntrain), ])  # Test covariates
ytest = y[-(1:ntrain), ]  # Test response

x = lapply(x, function(xx) xx[1:ntrain, ])  # Train covariates
y = y[1:ntrain, ]  # Train response

## -----------------------------------------------------------------------------
str(x)

## -----------------------------------------------------------------------------
fit = ptLasso(x, y, use.case = "timeSeries", alpha = 0)
plot(fit)  # Plot the fitted model
predict(fit, xtest, ytest = ytest)  # Predict using the fitted model

# With cross validation:
cvfit = cv.ptLasso(x, y, use.case = "timeSeries")
plot(cvfit, plot.alphahat = TRUE)  # Plot cross-validated model
predict(cvfit, xtest, ytest = ytest)  # Predict using cross-validated model