Find change points efficiently in mean variance change models

fastcpd_meanvariance(), fastcpd.meanvariance(), fastcpd_mv(), fastcpd.mv() are wrapper functions of fastcpd() to find the meanvariance change. The function is similar to fastcpd() except that the data is by default a matrix or data frame or a vector with each row / element as an observation and thus a formula is not required here.

Usage

fastcpd_meanvariance(data, ...)

fastcpd.meanvariance(data, ...)

fastcpd_mv(data, ...)

fastcpd.mv(data, ...)

Arguments

data: A matrix, a data frame or a vector.
...: Other arguments passed to fastcpd(), for example, segment_count.

Value

A fastcpd object.

Examples

set.seed(1)
data <- c(
  rnorm(3000, 0, 1),
  rnorm(1000, 10, 1),
  rnorm(3000, 10, 20),
  rnorm(1000, 0, 1)
)
system.time(result <- fastcpd.mv(data))
#> Warning: argument is not a function
#>    user  system elapsed 
#>   0.005   0.000   0.005 
summary(result)
#> 
#> Call:
#> fastcpd.mv(data = data)
#> 
#> Change points:
#> 3000 4000 7000 
#> 
#> Cost values:
#> 110.9701 44.33282 9001.941 7.441331 
#> 
#> Parameters:
#>     segment 1 segment 2  segment 3    segment 4
#> 1 -0.00420034 10.016722   9.650309 -0.004211938
#> 2  1.07141035  1.078801 401.934668  1.002069791
plot(result)

set.seed(1)
p <- 3
data <- if (requireNamespace("mvtnorm", quietly = TRUE)) {
  rbind(
    mvtnorm::rmvnorm(2e+5, mean = rep(0, p), sigma = diag(1, p)),
    mvtnorm::rmvnorm(1e+5, mean = rep(50, p), sigma = diag(1, p)),
    mvtnorm::rmvnorm(2e+5, mean = rep(0, p), sigma = diag(100, p)),
    mvtnorm::rmvnorm(2e+5, mean = rep(0, p), sigma = diag(1, p)),
    mvtnorm::rmvnorm(1e+5, mean = rep(50, p), sigma = diag(1, p)),
    mvtnorm::rmvnorm(2e+5, mean = rep(50, p), sigma = diag(100, p))
  )
} else {
  rbind(
    matrix(rnorm(p * 2e+5, mean = 0, sd = 1), ncol = p),
    matrix(rnorm(p * 1e+5, mean = 50, sd = 1), ncol = p),
    matrix(rnorm(p * 2e+5, mean = 0, sd = 10), ncol = p),
    matrix(rnorm(p * 2e+5, mean = 0, sd = 1), ncol = p),
    matrix(rnorm(p * 1e+5, mean = 50, sd = 1), ncol = p),
    matrix(rnorm(p * 2e+5, mean = 50, sd = 10), ncol = p)
  )
}
system.time(result <- fastcpd.mv(data))
#> Warning: argument is not a function
#>    user  system elapsed 
#>   0.710   0.167   0.877 
summary(result)
#> 
#> Call:
#> fastcpd.mv(data = data)
#> 
#> Change points:
#> 2e+05 3e+05 500408 7e+05 8e+05 
#> 
#> Cost values:
#> 108.8674 -43.02536 1385124 -50.71083 377.4763 1381660 
#> 
#> Parameters:
#>       segment 1     segment 2     segment 3     segment 4    segment 5
#> 1   0.001011082 50.0003528125  -0.016501485  0.0033379848 49.995348620
#> 2  -0.001572291 50.0047380197   0.027308617 -0.0002731255 49.995608790
#> 3  -0.001048280 49.9949663194  -0.000285863 -0.0022305825 50.004925725
#> 4   1.001716184  1.0053160771  99.837900270  1.0049251320  1.011184355
#> 5   0.003146441 -0.0061509213   0.155411984  0.0033208418  0.002837378
#> 6   0.001702982 -0.0009432632  -0.174243448 -0.0030404684 -0.001092170
#> 7   0.003146441 -0.0061509213   0.155411984  0.0033208418  0.002837378
#> 8   0.998570871  0.9954591727 100.257688784  0.9940791908  0.999139034
#> 9   0.001008951 -0.0062795529   0.070720757  0.0006369845 -0.001649613
#> 10  0.001702982 -0.0009432632  -0.174243448 -0.0030404684 -0.001092170
#> 11  0.001008951 -0.0062795529   0.070720757  0.0006369845 -0.001649613
#> 12  1.000100554  0.9971197656 100.588745799  0.9998191884  0.995957111
#>       segment 6
#> 1   50.00765767
#> 2   49.97689987
#> 3   50.01812646
#> 4  100.43738403
#> 5    0.09929858
#> 6    0.04291338
#> 7    0.09929858
#> 8   99.71858356
#> 9   -0.09614237
#> 10   0.04291338
#> 11  -0.09614237
#> 12  99.88324093
result@thetas[seq_len(p), ]
#>      segment 1 segment 2    segment 3     segment 4 segment 5 segment 6
#> 1  0.001011082  50.00035 -0.016501485  0.0033379848  49.99535  50.00766
#> 2 -0.001572291  50.00474  0.027308617 -0.0002731255  49.99561  49.97690
#> 3 -0.001048280  49.99497 -0.000285863 -0.0022305825  50.00493  50.01813
lapply(result@thetas[seq_len(p^2) + p, ], function(thetas) matrix(thetas, p))
#> $`segment 1`
#>             [,1]        [,2]        [,3]
#> [1,] 1.001716184 0.003146441 0.001702982
#> [2,] 0.003146441 0.998570871 0.001008951
#> [3,] 0.001702982 0.001008951 1.000100554
#> 
#> $`segment 2`
#>               [,1]         [,2]          [,3]
#> [1,]  1.0053160771 -0.006150921 -0.0009432632
#> [2,] -0.0061509213  0.995459173 -0.0062795529
#> [3,] -0.0009432632 -0.006279553  0.9971197656
#> 
#> $`segment 3`
#>            [,1]         [,2]         [,3]
#> [1,] 99.8379003   0.15541198  -0.17424345
#> [2,]  0.1554120 100.25768878   0.07072076
#> [3,] -0.1742434   0.07072076 100.58874580
#> 
#> $`segment 4`
#>              [,1]         [,2]          [,3]
#> [1,]  1.004925132 0.0033208418 -0.0030404684
#> [2,]  0.003320842 0.9940791908  0.0006369845
#> [3,] -0.003040468 0.0006369845  0.9998191884
#> 
#> $`segment 5`
#>              [,1]         [,2]         [,3]
#> [1,]  1.011184355  0.002837378 -0.001092170
#> [2,]  0.002837378  0.999139034 -0.001649613
#> [3,] -0.001092170 -0.001649613  0.995957111
#> 
#> $`segment 6`
#>              [,1]        [,2]        [,3]
#> [1,] 100.43738403  0.09929858  0.04291338
#> [2,]   0.09929858 99.71858356 -0.09614237
#> [3,]   0.04291338 -0.09614237 99.88324093
#>

Find change points efficiently in mean variance change models

Usage

Arguments

Value

See also

Examples