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set.seed(1)
n <- 300
p_true <- 4
p <- 40
x <- mvtnorm::rmvnorm(n, rep(0, p), diag(p))
theta_0 <- rbind(
runif(p_true, 1, 4),
runif(p_true, -5, 5),
runif(p_true, 1, 4),
runif(p_true, -3, 3)
)
theta_0 <- cbind(theta_0, matrix(0, ncol = p - p_true, nrow = 4))
y <- c(
x[1:(n * 0.25), ] %*% theta_0[1, ] + rnorm(n * 0.25),
x[(n * 0.25 + 1):(n * 0.5), ] %*% theta_0[2, ] + rnorm(n * 0.25),
x[(n * 0.5 + 1):(n * 0.75), ] %*% theta_0[3, ] + rnorm(n * 0.25),
x[(n * 0.75 + 1):n, ] %*% theta_0[4, ] + rnorm(n * 0.25)
)
small_lasso <- cbind.data.frame(y, x)result <- fastcpd.lasso(small_lasso, segment_count = 2, r.progress = FALSE)
summary(result)
#>
#> Call:
#> fastcpd.lasso(data = small_lasso, segment_count = 2, r.progress = FALSE)
#>
#> Change points:
#> 73 151
#>
#> Cost values:
#> 403.3531 330.8874 1131.242
#>
#> Parameters:
#> 40 x 3 sparse Matrix of class "dgCMatrix"
#> segment 1 segment 2 segment 3
#> [1,] 0.2725645 3.2847730 0.3945321
#> [2,] 1.0710180 -1.0318006 0.2333177
#> [3,] 2.0691658 0.8655912 .
#> [4,] 0.2865536 -2.7810274 1.4952166
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#> [40,] . . .result_vanilla_percentage <- fastcpd.lasso(
small_lasso, segment_count = 2, vanilla_percentage = 0.5,
r.progress = FALSE
)
summary(result_vanilla_percentage)
#>
#> Call:
#> fastcpd.lasso(data = small_lasso, segment_count = 2, vanilla_percentage = 0.5,
#> r.progress = FALSE)
#>
#> Change points:
#> 74 150
#>
#> Cost values:
#> 404.3949 296.7204 1162.25
#>
#> Parameters:
#> 40 x 3 sparse Matrix of class "dgCMatrix"
#> segment 1 segment 2 segment 3
#> [1,] 0.2835972 3.208983 0.4299211
#> [2,] 1.0913097 -1.306468 0.3019345
#> [3,] 2.0550480 0.697885 .
#> [4,] 0.2928737 -2.771848 1.4996787
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#> [40,] . . .result_multiple_epochs <- fastcpd.lasso(
small_lasso,
segment_count = 2,
multiple_epochs = function(segment_length) {
if (segment_length < 25) 1 else 0
},
r.progress = FALSE
)
summary(result_multiple_epochs)
#>
#> Call:
#> fastcpd.lasso(data = small_lasso, segment_count = 2, multiple_epochs = function(segment_length) {
#> if (segment_length < 25)
#> 1
#> else 0
#> }, r.progress = FALSE)
#>
#> Change points:
#> 74 151 227
#>
#> Cost values:
#> 404.3949 323.0533 394.7117 245.3975
#>
#> Parameters:
#> 40 x 4 sparse Matrix of class "dgCMatrix"
#> segment 1 segment 2 segment 3 segment 4
#> [1,] 0.2835972 3.269615 0.9960117 .
#> [2,] 1.0913097 -1.089741 1.6936173 .
#> [3,] 2.0550480 0.896723 1.5956191 .
#> [4,] 0.2928737 -2.769824 2.1702226 0.1701821
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#> [40,] . . . .This document is generated by the following code:
R -e 'knitr::knit("vignettes/examples-advanced.Rmd.original", output = "vignettes/examples-advanced.Rmd")'knitr::opts_chunk$set(
collapse = TRUE, comment = "#>", eval = TRUE, warning = FALSE
)
library(fastcpd)
set.seed(1)
n <- 300
p_true <- 4
p <- 40
x <- mvtnorm::rmvnorm(n, rep(0, p), diag(p))
theta_0 <- rbind(
runif(p_true, 1, 4),
runif(p_true, -5, 5),
runif(p_true, 1, 4),
runif(p_true, -3, 3)
)
theta_0 <- cbind(theta_0, matrix(0, ncol = p - p_true, nrow = 4))
y <- c(
x[1:(n * 0.25), ] %*% theta_0[1, ] + rnorm(n * 0.25),
x[(n * 0.25 + 1):(n * 0.5), ] %*% theta_0[2, ] + rnorm(n * 0.25),
x[(n * 0.5 + 1):(n * 0.75), ] %*% theta_0[3, ] + rnorm(n * 0.25),
x[(n * 0.75 + 1):n, ] %*% theta_0[4, ] + rnorm(n * 0.25)
)
small_lasso <- cbind.data.frame(y, x)
result <- fastcpd.lasso(small_lasso, segment_count = 2, r.progress = FALSE)
summary(result)
set.seed(1)
n <- 300
p_true <- 4
p <- 40
x <- mvtnorm::rmvnorm(n, rep(0, p), diag(p))
theta_0 <- rbind(
runif(p_true, 1, 4),
runif(p_true, -5, 5),
runif(p_true, 1, 4),
runif(p_true, -3, 3)
)
theta_0 <- cbind(theta_0, matrix(0, ncol = p - p_true, nrow = 4))
y <- c(
x[1:(n * 0.25), ] %*% theta_0[1, ] + rnorm(n * 0.25),
x[(n * 0.25 + 1):(n * 0.5), ] %*% theta_0[2, ] + rnorm(n * 0.25),
x[(n * 0.5 + 1):(n * 0.75), ] %*% theta_0[3, ] + rnorm(n * 0.25),
x[(n * 0.75 + 1):n, ] %*% theta_0[4, ] + rnorm(n * 0.25)
)
small_lasso <- cbind.data.frame(y, x)
result_vanilla_percentage <- fastcpd.lasso(
small_lasso, segment_count = 2, vanilla_percentage = 0.5,
r.progress = FALSE
)
summary(result_vanilla_percentage)
set.seed(1)
n <- 300
p_true <- 4
p <- 40
x <- mvtnorm::rmvnorm(n, rep(0, p), diag(p))
theta_0 <- rbind(
runif(p_true, 1, 4),
runif(p_true, -5, 5),
runif(p_true, 1, 4),
runif(p_true, -3, 3)
)
theta_0 <- cbind(theta_0, matrix(0, ncol = p - p_true, nrow = 4))
y <- c(
x[1:(n * 0.25), ] %*% theta_0[1, ] + rnorm(n * 0.25),
x[(n * 0.25 + 1):(n * 0.5), ] %*% theta_0[2, ] + rnorm(n * 0.25),
x[(n * 0.5 + 1):(n * 0.75), ] %*% theta_0[3, ] + rnorm(n * 0.25),
x[(n * 0.75 + 1):n, ] %*% theta_0[4, ] + rnorm(n * 0.25)
)
small_lasso <- cbind.data.frame(y, x)
result_multiple_epochs <- fastcpd.lasso(
small_lasso,
segment_count = 2,
multiple_epochs = function(segment_length) {
if (segment_length < 25) 1 else 0
},
r.progress = FALSE
)
summary(result_multiple_epochs)These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.