Advanced usages

set.seed(1)
n <- 1500
p_true <- 6
p <- 50
x <- mvtnorm::rmvnorm(480, rep(0, p), diag(p))
theta_0 <- rbind(
  runif(p_true, -5, -2),
  runif(p_true, -3, 3),
  runif(p_true, 2, 5),
  runif(p_true, -5, 5)
)
theta_0 <- cbind(theta_0, matrix(0, ncol = p - p_true, nrow = 4))
y <- c(
  x[1:80, ] %*% theta_0[1, ] + rnorm(80, 0, 1),
  x[81:200, ] %*% theta_0[2, ] + rnorm(120, 0, 1),
  x[201:320, ] %*% theta_0[3, ] + rnorm(120, 0, 1),
  x[321:480, ] %*% theta_0[4, ] + rnorm(160, 0, 1)
)
result <- fastcpd(
  formula = y ~ . - 1,
  data = data.frame(y = y, x = x),
  family = "lasso"
)
summary(result)
#> 
#> Call:
#> fastcpd(formula = y ~ . - 1, data = data.frame(y = y, x = x), 
#>     family = "lasso")
#> 
#> Change points:
#> 79 202 325 
#> 
#> Cost values:
#> 185.7631 508.1195 337.1486 328.3755 
#> 
#> Parameters:
#> 50 x 4 sparse Matrix of class "dgCMatrix"
#>       segment 1  segment 2 segment 3 segment 4
#>  [1,] -1.815974 -1.5964536  3.838563 -2.399833
#>  [2,] -1.692328 -1.2734980  3.287314  2.170296
#>  [3,] -3.340743  0.8558482  4.075844  2.853726
#>  [4,] -4.075669  .          1.788200 -2.534484
#>  [5,] -3.972095  .          3.868924  2.308333
#>  [6,] -3.203743 -1.6359700  2.388598  3.302757
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Multiple epochs

result_multiple_epochs <- fastcpd(
  formula = y ~ . - 1,
  data = data.frame(y = y, x = x),
  family = "lasso",
  k = function(x) if (x < 20) 1 else 0
)
summary(result_multiple_epochs)
#> 
#> Call:
#> fastcpd(formula = y ~ . - 1, data = data.frame(y = y, x = x), 
#>     k = function(x) if (x < 20) 1 else 0, family = "lasso")
#> 
#> Change points:
#> 79 200 320 
#> 
#> Cost values:
#> 3511.248 1248.405 5457.215 5298.905 
#> 
#> Parameters:
#> 50 x 4 sparse Matrix of class "dgCMatrix"
#>       segment 1 segment 2 segment 3 segment 4
#>  [1,]         .         .         .         .
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Vanilla percentage

result_vanilla_percentage <- fastcpd(
  formula = y ~ . - 1,
  data = data.frame(y = y, x = x),
  family = "lasso",
  vanilla_percentage = 0.2
)
summary(result_vanilla_percentage)
#> 
#> Call:
#> fastcpd(formula = y ~ . - 1, data = data.frame(y = y, x = x), 
#>     family = "lasso", vanilla_percentage = 0.2)
#> 
#> Change points:
#> 80 202 325 
#> 
#> Cost values:
#> 189.9238 501.139 337.1486 328.3755 
#> 
#> Parameters:
#> 50 x 4 sparse Matrix of class "dgCMatrix"
#>       segment 1  segment 2 segment 3 segment 4
#>  [1,] -1.841843 -1.6334187  3.838563 -2.399833
#>  [2,] -1.692516 -1.2889968  3.287314  2.170296
#>  [3,] -3.329914  0.8624382  4.075844  2.853726
#>  [4,] -4.058322  .          1.788200 -2.534484
#>  [5,] -3.958522  .          3.868924  2.308333
#>  [6,] -3.197979 -1.6349725  2.388598  3.302757
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