changepoints: Change-points detected by WBS or BS
In wbs: Wild Binary Segmentation for Multiple Change-Point Detection
Description
Usage
Arguments
Details
Value
Examples
Description
The function applies user-specified stopping criteria to extract change-points from object
generated by wbs or sbs. For object of class 'sbs', the function returns
change-points whose corresponding test statistic exceeds threshold given in th. For object of class 'wbs',
the change-points can be also detected using information criteria with penalties specified in penalty.
Usage
1
2
3
4
5
6
7
8
9
10
changepoints(object, ...)
## S3 method for class 'sbs'
changepoints(object, th = NULL, th.const = 1.3,
Kmax = NULL, ...)
## S3 method for class 'wbs'
changepoints(object, th = NULL, th.const = 1.3,
Kmax = 50, penalty = c("ssic.penalty", "bic.penalty",
"mbic.penalty"), ...)
Arguments
object
an object of 'wbs' or 'sbs' class returned by, respectively, wbs and sbs functions
...
further arguments that may be passed to the penalty functions
th
a vector of positive scalars
th.const
a vector of positive scalars
Kmax
a maximum number of change-points to be detected
penalty
a character vector with names of penalty functions used
Details
For the change-point detection based on thresholding (object of class 'sbs' or 'wbs'), the user can either specify the thresholds in th directly,
determine the maximum number Kmax of change-points to be detected, or let th depend on th.const.
When Kmax is given, the function automatically sets th to the lowest threshold such that the number of detected change-points is lower or equal than Kmax.
Note that for the BS algorithm it might be not possible to find the threshold such that exactly Kmax change-points are found.
When th and Kmax are omitted, the threshold value is set to
th=sigma * th.const* sqrt(2 log(n)),
where sigma is the Median Absolute Deviation estimate of the noise level and n is the number of elements in x.
For the change-point detection based on information criteria (object of class 'wbs' only),
the user can specify both the maximum number of change-points (Kmax) and a type of the penalty used.
Parameter penalty should contain a list of characters with names of the functions of at least two arguments (n and cpt).
For each penalty given, the following information criterion is minimized over candidate sets of change-points cpt:
n/2 log(sigma_k)+ penalty(n,cpt),
where k denotes the number of elements in cpt, sigma_k is the corresponding maximum
likelihood estimator of the residual variance.
Value
sigma
Median Absolute Deviation estimate of the noise level
th
a vector of thresholds
no.cpt.th
the number of change-points detected for each value of th
cpt.th
a list with the change-points detected for each value of th
Kmax
a maximum number of change-points detected
ic.curve
a list with values of the chosen information criteria
no.cpt.ic
the number of change-points detected for each information criterion considered
cpt.ic
a list with the change-points detected for each information criterion considered
Examples
1
2
3
4
5
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7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
#we generates gaussian noise + Poisson process signal with 10 jumps on average
set.seed(10)
N <- rpois(1,10)
true.cpt <- sample(1000,N)
m1 <- matrix(rep(1:1000,N),1000,N,byrow=FALSE)
m2 <- matrix(rep(true.cpt,1000),1000,N,byrow=TRUE)
x <- rnorm(1000) + apply(m1>=m2,1,sum)
# we apply the BS and WBS algorithms with default values for their parameters
s <- sbs(x)
w <- wbs(x)
s.cpt <- changepoints(s)
s.cpt
w.cpt <- changepoints(w)
w.cpt
#we can use different stopping criteria, invoking sbs/wbs functions is not necessary
s.cpt <- changepoints(s,th.const=c(1,1.3))
s.cpt
w.cpt <- changepoints(w,th.const=c(1,1.3))
w.cpt
Example output
$sigma
[1] 1.006217
$th
[1] 4.86204
$no.cpt.th
[1] 7
$cpt.th
$cpt.th[[1]]
[1] 555 268 647 73 732 421 219
$Kmax
[1] 7
$sigma
[1] 1.006217
$th
[1] 4.86204
$no.cpt.th
[1] 7
$cpt.th
$cpt.th[[1]]
[1] 555 268 647 73 732 421 219
$Kmax
[1] 50
$ic.curve
$ic.curve$ssic.penalty
[1] 1229.71888 650.55995 271.63506 107.64885 71.07910 55.50606
[7] 40.84121 32.02118 27.55643 32.02112 37.33802 40.44132
[13] 45.53637 51.79466 58.16812 59.89631 65.59345 68.50119
[19] 71.21269 77.08181 83.22950 89.28098 91.67773 98.58633
[25] 105.52695 107.91348 110.83005 117.20743 124.23552 127.10714
[31] 130.12442 132.54164 135.10811 138.19542 142.00726 146.32010
[37] 149.10367 155.38742 158.05004 161.11693 167.48265 172.90659
[43] 176.11467 182.38557 187.87310 194.87468 198.25904 203.63321
[49] 209.51608 214.24121 217.95998
$ic.curve$bic.penalty
[1] 1229.71888 650.42515 271.36546 107.24445 70.53989 54.83206
[7] 40.03240 31.07757 26.47802 30.80792 35.99001 38.95851
[13] 43.91876 50.04225 56.28091 57.87430 63.43663 66.20958
[19] 68.78628 74.52059 80.53348 86.45017 88.71211 95.48591
[25] 102.29173 104.54347 107.32523 113.56781 120.46109 123.19792
[31] 126.08040 128.36281 130.79448 133.74700 137.42404 141.60207
[37] 144.25084 150.39979 152.92761 155.85970 162.09061 167.37976
[43] 170.45304 176.58914 181.94186 188.80864 192.05820 197.29757
[49] 203.04564 207.63597 211.21994
$ic.curve$mbic.penalty
[1] 1229.71888 653.17980 276.58585 114.60994 79.89162 66.26736
[7] 53.60211 46.44849 43.38724 49.24470 55.62840 60.08725
[13] 66.79693 74.09172 82.17533 84.02413 90.48860 94.30621
[19] 98.54950 105.90396 112.91882 120.29729 122.88255 131.11124
[25] 139.18857 141.72011 144.83205 152.73629 161.29182 164.54423
[31] 167.95201 170.87875 174.41243 178.26585 183.26892 188.41439
[37] 192.08795 199.44997 201.94074 205.96860 213.68523 219.63361
[43] 223.02907 230.19770 236.49529 244.99852 248.23437 254.22465
[49] 261.09697 266.34407 270.53401
$cpt.ic
$cpt.ic$ssic.penalty
[1] 555 268 647 73 732 421 219 612
$cpt.ic$bic.penalty
[1] 555 268 647 73 732 421 219 612
$cpt.ic$mbic.penalty
[1] 555 268 647 73 732 421 219 612
$no.cpt.ic
ssic.penalty bic.penalty mbic.penalty
8 8 8
$sigma
[1] 1.006217
$th
[1] 3.740031 4.862040
$no.cpt.th
[1] 8 7
$cpt.th
$cpt.th[[1]]
[1] 555 268 647 73 732 421 219 612
$cpt.th[[2]]
[1] 555 268 647 73 732 421 219
$Kmax
[1] 8
$sigma
[1] 1.006217
$th
[1] 3.740031 4.862040
$no.cpt.th
[1] 8 7
$cpt.th
$cpt.th[[1]]
[1] 555 268 647 73 732 421 219 612
$cpt.th[[2]]
[1] 555 268 647 73 732 421 219
$Kmax
[1] 50
$ic.curve
$ic.curve$ssic.penalty
[1] 1229.71888 650.55995 271.63506 107.64885 71.07910 55.50606
[7] 40.84121 32.02118 27.55643 32.02112 37.33802 40.44132
[13] 45.53637 51.79466 58.16812 59.89631 65.59345 68.50119
[19] 71.21269 77.08181 83.22950 89.28098 91.67773 98.58633
[25] 105.52695 107.91348 110.83005 117.20743 124.23552 127.10714
[31] 130.12442 132.54164 135.10811 138.19542 142.00726 146.32010
[37] 149.10367 155.38742 158.05004 161.11693 167.48265 172.90659
[43] 176.11467 182.38557 187.87310 194.87468 198.25904 203.63321
[49] 209.51608 214.24121 217.95998
$ic.curve$bic.penalty
[1] 1229.71888 650.42515 271.36546 107.24445 70.53989 54.83206
[7] 40.03240 31.07757 26.47802 30.80792 35.99001 38.95851
[13] 43.91876 50.04225 56.28091 57.87430 63.43663 66.20958
[19] 68.78628 74.52059 80.53348 86.45017 88.71211 95.48591
[25] 102.29173 104.54347 107.32523 113.56781 120.46109 123.19792
[31] 126.08040 128.36281 130.79448 133.74700 137.42404 141.60207
[37] 144.25084 150.39979 152.92761 155.85970 162.09061 167.37976
[43] 170.45304 176.58914 181.94186 188.80864 192.05820 197.29757
[49] 203.04564 207.63597 211.21994
$ic.curve$mbic.penalty
[1] 1229.71888 653.17980 276.58585 114.60994 79.89162 66.26736
[7] 53.60211 46.44849 43.38724 49.24470 55.62840 60.08725
[13] 66.79693 74.09172 82.17533 84.02413 90.48860 94.30621
[19] 98.54950 105.90396 112.91882 120.29729 122.88255 131.11124
[25] 139.18857 141.72011 144.83205 152.73629 161.29182 164.54423
[31] 167.95201 170.87875 174.41243 178.26585 183.26892 188.41439
[37] 192.08795 199.44997 201.94074 205.96860 213.68523 219.63361
[43] 223.02907 230.19770 236.49529 244.99852 248.23437 254.22465
[49] 261.09697 266.34407 270.53401
$cpt.ic
$cpt.ic$ssic.penalty
[1] 555 268 647 73 732 421 219 612
$cpt.ic$bic.penalty
[1] 555 268 647 73 732 421 219 612
$cpt.ic$mbic.penalty
[1] 555 268 647 73 732 421 219 612
$no.cpt.ic
ssic.penalty bic.penalty mbic.penalty
8 8 8
wbs documentation built on May 15, 2019, 1:04 a.m.
R Package Documentation
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Description Usage Arguments Details Value Examples
Description
The function applies user-specified stopping criteria to extract change-points from object
generated by wbs or sbs. For object of class 'sbs', the function returns
change-points whose corresponding test statistic exceeds threshold given in th. For object of class 'wbs',
the change-points can be also detected using information criteria with penalties specified in penalty.
Usage
1 2 3 4 5 6 7 8 9 10 | changepoints(object, ...)
## S3 method for class 'sbs'
changepoints(object, th = NULL, th.const = 1.3,
Kmax = NULL, ...)
## S3 method for class 'wbs'
changepoints(object, th = NULL, th.const = 1.3,
Kmax = 50, penalty = c("ssic.penalty", "bic.penalty",
"mbic.penalty"), ...)
|
Arguments
object |
an object of 'wbs' or 'sbs' class returned by, respectively, |
... |
further arguments that may be passed to the penalty functions |
th |
a vector of positive scalars |
th.const |
a vector of positive scalars |
Kmax |
a maximum number of change-points to be detected |
penalty |
a character vector with names of penalty functions used |
Details
For the change-point detection based on thresholding (object of class 'sbs' or 'wbs'), the user can either specify the thresholds in th directly,
determine the maximum number Kmax of change-points to be detected, or let th depend on th.const.
When Kmax is given, the function automatically sets th to the lowest threshold such that the number of detected change-points is lower or equal than Kmax.
Note that for the BS algorithm it might be not possible to find the threshold such that exactly Kmax change-points are found.
When th and Kmax are omitted, the threshold value is set to
th=sigma * th.const* sqrt(2 log(n)),
where sigma is the Median Absolute Deviation estimate of the noise level and n is the number of elements in x.
For the change-point detection based on information criteria (object of class 'wbs' only),
the user can specify both the maximum number of change-points (Kmax) and a type of the penalty used.
Parameter penalty should contain a list of characters with names of the functions of at least two arguments (n and cpt).
For each penalty given, the following information criterion is minimized over candidate sets of change-points cpt:
n/2 log(sigma_k)+ penalty(n,cpt),
where k denotes the number of elements in cpt, sigma_k is the corresponding maximum likelihood estimator of the residual variance.
Value
sigma |
Median Absolute Deviation estimate of the noise level |
th |
a vector of thresholds |
no.cpt.th |
the number of change-points detected for each value of |
cpt.th |
a list with the change-points detected for each value of |
Kmax |
a maximum number of change-points detected |
ic.curve |
a list with values of the chosen information criteria |
no.cpt.ic |
the number of change-points detected for each information criterion considered |
cpt.ic |
a list with the change-points detected for each information criterion considered |
Examples
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | #we generates gaussian noise + Poisson process signal with 10 jumps on average
set.seed(10)
N <- rpois(1,10)
true.cpt <- sample(1000,N)
m1 <- matrix(rep(1:1000,N),1000,N,byrow=FALSE)
m2 <- matrix(rep(true.cpt,1000),1000,N,byrow=TRUE)
x <- rnorm(1000) + apply(m1>=m2,1,sum)
# we apply the BS and WBS algorithms with default values for their parameters
s <- sbs(x)
w <- wbs(x)
s.cpt <- changepoints(s)
s.cpt
w.cpt <- changepoints(w)
w.cpt
#we can use different stopping criteria, invoking sbs/wbs functions is not necessary
s.cpt <- changepoints(s,th.const=c(1,1.3))
s.cpt
w.cpt <- changepoints(w,th.const=c(1,1.3))
w.cpt
|
Example output
$sigma
[1] 1.006217
$th
[1] 4.86204
$no.cpt.th
[1] 7
$cpt.th
$cpt.th[[1]]
[1] 555 268 647 73 732 421 219
$Kmax
[1] 7
$sigma
[1] 1.006217
$th
[1] 4.86204
$no.cpt.th
[1] 7
$cpt.th
$cpt.th[[1]]
[1] 555 268 647 73 732 421 219
$Kmax
[1] 50
$ic.curve
$ic.curve$ssic.penalty
[1] 1229.71888 650.55995 271.63506 107.64885 71.07910 55.50606
[7] 40.84121 32.02118 27.55643 32.02112 37.33802 40.44132
[13] 45.53637 51.79466 58.16812 59.89631 65.59345 68.50119
[19] 71.21269 77.08181 83.22950 89.28098 91.67773 98.58633
[25] 105.52695 107.91348 110.83005 117.20743 124.23552 127.10714
[31] 130.12442 132.54164 135.10811 138.19542 142.00726 146.32010
[37] 149.10367 155.38742 158.05004 161.11693 167.48265 172.90659
[43] 176.11467 182.38557 187.87310 194.87468 198.25904 203.63321
[49] 209.51608 214.24121 217.95998
$ic.curve$bic.penalty
[1] 1229.71888 650.42515 271.36546 107.24445 70.53989 54.83206
[7] 40.03240 31.07757 26.47802 30.80792 35.99001 38.95851
[13] 43.91876 50.04225 56.28091 57.87430 63.43663 66.20958
[19] 68.78628 74.52059 80.53348 86.45017 88.71211 95.48591
[25] 102.29173 104.54347 107.32523 113.56781 120.46109 123.19792
[31] 126.08040 128.36281 130.79448 133.74700 137.42404 141.60207
[37] 144.25084 150.39979 152.92761 155.85970 162.09061 167.37976
[43] 170.45304 176.58914 181.94186 188.80864 192.05820 197.29757
[49] 203.04564 207.63597 211.21994
$ic.curve$mbic.penalty
[1] 1229.71888 653.17980 276.58585 114.60994 79.89162 66.26736
[7] 53.60211 46.44849 43.38724 49.24470 55.62840 60.08725
[13] 66.79693 74.09172 82.17533 84.02413 90.48860 94.30621
[19] 98.54950 105.90396 112.91882 120.29729 122.88255 131.11124
[25] 139.18857 141.72011 144.83205 152.73629 161.29182 164.54423
[31] 167.95201 170.87875 174.41243 178.26585 183.26892 188.41439
[37] 192.08795 199.44997 201.94074 205.96860 213.68523 219.63361
[43] 223.02907 230.19770 236.49529 244.99852 248.23437 254.22465
[49] 261.09697 266.34407 270.53401
$cpt.ic
$cpt.ic$ssic.penalty
[1] 555 268 647 73 732 421 219 612
$cpt.ic$bic.penalty
[1] 555 268 647 73 732 421 219 612
$cpt.ic$mbic.penalty
[1] 555 268 647 73 732 421 219 612
$no.cpt.ic
ssic.penalty bic.penalty mbic.penalty
8 8 8
$sigma
[1] 1.006217
$th
[1] 3.740031 4.862040
$no.cpt.th
[1] 8 7
$cpt.th
$cpt.th[[1]]
[1] 555 268 647 73 732 421 219 612
$cpt.th[[2]]
[1] 555 268 647 73 732 421 219
$Kmax
[1] 8
$sigma
[1] 1.006217
$th
[1] 3.740031 4.862040
$no.cpt.th
[1] 8 7
$cpt.th
$cpt.th[[1]]
[1] 555 268 647 73 732 421 219 612
$cpt.th[[2]]
[1] 555 268 647 73 732 421 219
$Kmax
[1] 50
$ic.curve
$ic.curve$ssic.penalty
[1] 1229.71888 650.55995 271.63506 107.64885 71.07910 55.50606
[7] 40.84121 32.02118 27.55643 32.02112 37.33802 40.44132
[13] 45.53637 51.79466 58.16812 59.89631 65.59345 68.50119
[19] 71.21269 77.08181 83.22950 89.28098 91.67773 98.58633
[25] 105.52695 107.91348 110.83005 117.20743 124.23552 127.10714
[31] 130.12442 132.54164 135.10811 138.19542 142.00726 146.32010
[37] 149.10367 155.38742 158.05004 161.11693 167.48265 172.90659
[43] 176.11467 182.38557 187.87310 194.87468 198.25904 203.63321
[49] 209.51608 214.24121 217.95998
$ic.curve$bic.penalty
[1] 1229.71888 650.42515 271.36546 107.24445 70.53989 54.83206
[7] 40.03240 31.07757 26.47802 30.80792 35.99001 38.95851
[13] 43.91876 50.04225 56.28091 57.87430 63.43663 66.20958
[19] 68.78628 74.52059 80.53348 86.45017 88.71211 95.48591
[25] 102.29173 104.54347 107.32523 113.56781 120.46109 123.19792
[31] 126.08040 128.36281 130.79448 133.74700 137.42404 141.60207
[37] 144.25084 150.39979 152.92761 155.85970 162.09061 167.37976
[43] 170.45304 176.58914 181.94186 188.80864 192.05820 197.29757
[49] 203.04564 207.63597 211.21994
$ic.curve$mbic.penalty
[1] 1229.71888 653.17980 276.58585 114.60994 79.89162 66.26736
[7] 53.60211 46.44849 43.38724 49.24470 55.62840 60.08725
[13] 66.79693 74.09172 82.17533 84.02413 90.48860 94.30621
[19] 98.54950 105.90396 112.91882 120.29729 122.88255 131.11124
[25] 139.18857 141.72011 144.83205 152.73629 161.29182 164.54423
[31] 167.95201 170.87875 174.41243 178.26585 183.26892 188.41439
[37] 192.08795 199.44997 201.94074 205.96860 213.68523 219.63361
[43] 223.02907 230.19770 236.49529 244.99852 248.23437 254.22465
[49] 261.09697 266.34407 270.53401
$cpt.ic
$cpt.ic$ssic.penalty
[1] 555 268 647 73 732 421 219 612
$cpt.ic$bic.penalty
[1] 555 268 647 73 732 421 219 612
$cpt.ic$mbic.penalty
[1] 555 268 647 73 732 421 219 612
$no.cpt.ic
ssic.penalty bic.penalty mbic.penalty
8 8 8
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