strucchange: Interfaces for strucchange package for data science...
In intubate: Interface to Popular R Functions for Data Science Pipelines
Description
Usage
Arguments
Details
Value
Author(s)
Examples
Description
Interfaces to strucchange functions that can be used
in a pipeline implemented by magrittr.
Usage
1
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ntbt_breakpoints(data, ...)
ntbt_efp(data, ...)
ntbt_Fstats(data, ...)
ntbt_mefp(data, ...)
ntbt_recresid(data, ...)
ntbt_sctest(data, ...)
Arguments
data
data frame, tibble, list, ...
...
Other arguments passed to the corresponding interfaced function.
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
Examples
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## Not run:
library(intubate)
library(magrittr)
library(strucchange)
## ntbt_breakpoints: Dating Breaks
data("Nile")
d <- list(Nl = Nile)
## Original function to interface
breakpoints(Nl ~ 1, data = d)
## The interface puts data as first parameter
ntbt_breakpoints(d, Nl ~ 1)
## so it can be used easily in a pipeline.
d %>%
ntbt_breakpoints(Nl ~ 1)
## ntbt_efp: Empirical Fluctuation Processes
## Original function to interface
ocus.nile <- efp(Nl ~ 1, d, type = "OLS-CUSUM")
plot(ocus.nile)
## The interface puts data as first parameter
ocus.nile <- ntbt_efp(d, Nl ~ 1, type = "OLS-CUSUM")
plot(ocus.nile)
## so it can be used easily in a pipeline.
d %>%
ntbt_efp(Nl ~ 1, type = "OLS-CUSUM") %>%
plot()
## ntbt_Fstats: F Statistics
## Original function to interface
fs.nile <- Fstats(Nl ~ 1, data = d)
plot(fs.nile)
## The interface puts data as first parameter
fs.nile <- ntbt_Fstats(d, Nl ~ 1)
plot(fs.nile)
## so it can be used easily in a pipeline.
d %>%
ntbt_Fstats(Nl ~ 1) %>%
plot()
## ntbt_mefp: Monitoring of Empirical Fluctuation Processes
df1 <- data.frame(y = rnorm(300))
df1[150:300, "y"] <- df1[150:300, "y"] + 1
## Original function to interface
mefp(y ~ 1, data = df1[1:50,, drop = FALSE], type = "ME", h = 1, alpha = 0.05)
## The interface puts data as first parameter
ntbt_mefp(df1[1:50,, drop = FALSE], y ~ 1, type = "ME", h = 1, alpha = 0.05)
## so it can be used easily in a pipeline.
df1[1:50,, drop = FALSE] %>%
ntbt_mefp(y ~ 1, type = "ME", h = 1, alpha = 0.05)
## ntbt_recresid: Recursive Residuals
d1 <- list(x = rnorm(100) + rep(c(0, 2), each = 50))
## Original function to interface
recresid(x ~ 1, d1)
## The interface puts data as first parameter
ntbt_recresid(d1, x ~ 1)
## so it can be used easily in a pipeline.
d1 %>%
ntbt_recresid(x ~ 1)
## ntbt_sctest: Structural Change Tests in Linear Regression Models
data("longley")
## Original function to interface
sctest(Employed ~ Year + GNP.deflator + GNP + Armed.Forces, data = longley,
type = "Chow", point = 7)
## The interface puts data as first parameter
ntbt_sctest(longley, Employed ~ Year + GNP.deflator + GNP + Armed.Forces,
type = "Chow", point = 7)
## so it can be used easily in a pipeline.
longley %>%
ntbt_sctest(Employed ~ Year + GNP.deflator + GNP + Armed.Forces,
type = "Chow", point = 7)
## End(Not run)
Example output
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
Optimal 2-segment partition:
Call:
breakpoints.formula(formula = Nl ~ 1, data = d)
Breakpoints at observation number:
28
Corresponding to breakdates:
1898
Optimal 2-segment partition:
Call:
breakpoints.formula(formula = Nl ~ 1)
Breakpoints at observation number:
28
Corresponding to breakdates:
1898
Optimal 2-segment partition:
Call:
breakpoints.formula(formula = Nl ~ 1)
Breakpoints at observation number:
28
Corresponding to breakdates:
1898
Monitoring with ME test (moving estimates test)
Initial call:
mefp.formula(formula = y ~ 1, type = "ME", data = df1[1:50, , drop = FALSE], h = 1, alpha = 0.05)
Last call:
mefp.formula(formula = y ~ 1, type = "ME", data = df1[1:50, , drop = FALSE], h = 1, alpha = 0.05)
Significance level : 0.05
Critical value : 2.745928
History size : 50
Last point evaluated : 50
Parameter estimate on history :
(Intercept)
-0.04086475
Monitoring with ME test (moving estimates test)
Initial call:
mefp.formula(formula = y ~ 1, type = "ME", h = 1, alpha = 0.05)
Last call:
mefp.formula(formula = y ~ 1, type = "ME", h = 1, alpha = 0.05)
Significance level : 0.05
Critical value : 2.745928
History size : 50
Last point evaluated : 50
Parameter estimate on history :
(Intercept)
-0.04086475
Monitoring with ME test (moving estimates test)
Initial call:
mefp.formula(formula = y ~ 1, type = "ME", h = 1, alpha = 0.05)
Last call:
mefp.formula(formula = y ~ 1, type = "ME", h = 1, alpha = 0.05)
Significance level : 0.05
Critical value : 2.745928
History size : 50
Last point evaluated : 50
Parameter estimate on history :
(Intercept)
-0.04086475
[1] -0.84742456 0.63460439 0.16460140 -1.82085041 -0.09269521 0.61683494
[7] -0.22813883 -0.01804020 0.38545286 0.43179387 0.47249884 0.50615285
[13] 0.15246908 0.75547867 0.36549671 0.57828183 -1.05035365 0.33376842
[19] -1.48588592 0.06791984 0.60260434 -1.00302918 -0.12102735 0.54974603
[25] -1.39209691 -0.89466377 -1.67842843 -0.60945465 -2.32244299 2.55222863
[31] -1.93651653 -0.84631663 1.99532463 -0.93273161 1.24128210 0.35214449
[37] 0.44159178 -1.94295468 -0.16392386 1.10203644 -1.04072038 0.70122817
[43] -1.49485498 -0.18822885 1.56081446 -0.12635636 -0.21116296 0.34794490
[49] -0.33368612 2.58583368 0.48966990 2.04735815 1.81100062 1.15848241
[55] 1.32350257 1.19200098 2.90611616 1.52354400 2.08384252 2.78166534
[61] 1.98808489 2.10868455 1.13102941 1.38700027 1.32561180 0.74761973
[67] 1.69976338 0.92707248 0.52836016 1.10963569 2.20815691 0.08199166
[73] -0.24409303 2.11882821 0.37105706 1.28156250 0.79914225 1.81198448
[79] 1.74938738 1.88698099 0.60554132 2.83966700 2.07380256 1.36490851
[85] 1.61233010 1.29095389 -0.82022696 0.32077905 1.22862291 1.32636166
[91] 1.78803458 1.96133414 1.40228470 -0.23327935 1.02744344 1.33685620
[97] 1.22817081 0.66956850 2.62300471
[1] -0.84742456 0.63460439 0.16460140 -1.82085041 -0.09269521 0.61683494
[7] -0.22813883 -0.01804020 0.38545286 0.43179387 0.47249884 0.50615285
[13] 0.15246908 0.75547867 0.36549671 0.57828183 -1.05035365 0.33376842
[19] -1.48588592 0.06791984 0.60260434 -1.00302918 -0.12102735 0.54974603
[25] -1.39209691 -0.89466377 -1.67842843 -0.60945465 -2.32244299 2.55222863
[31] -1.93651653 -0.84631663 1.99532463 -0.93273161 1.24128210 0.35214449
[37] 0.44159178 -1.94295468 -0.16392386 1.10203644 -1.04072038 0.70122817
[43] -1.49485498 -0.18822885 1.56081446 -0.12635636 -0.21116296 0.34794490
[49] -0.33368612 2.58583368 0.48966990 2.04735815 1.81100062 1.15848241
[55] 1.32350257 1.19200098 2.90611616 1.52354400 2.08384252 2.78166534
[61] 1.98808489 2.10868455 1.13102941 1.38700027 1.32561180 0.74761973
[67] 1.69976338 0.92707248 0.52836016 1.10963569 2.20815691 0.08199166
[73] -0.24409303 2.11882821 0.37105706 1.28156250 0.79914225 1.81198448
[79] 1.74938738 1.88698099 0.60554132 2.83966700 2.07380256 1.36490851
[85] 1.61233010 1.29095389 -0.82022696 0.32077905 1.22862291 1.32636166
[91] 1.78803458 1.96133414 1.40228470 -0.23327935 1.02744344 1.33685620
[97] 1.22817081 0.66956850 2.62300471
[1] -0.84742456 0.63460439 0.16460140 -1.82085041 -0.09269521 0.61683494
[7] -0.22813883 -0.01804020 0.38545286 0.43179387 0.47249884 0.50615285
[13] 0.15246908 0.75547867 0.36549671 0.57828183 -1.05035365 0.33376842
[19] -1.48588592 0.06791984 0.60260434 -1.00302918 -0.12102735 0.54974603
[25] -1.39209691 -0.89466377 -1.67842843 -0.60945465 -2.32244299 2.55222863
[31] -1.93651653 -0.84631663 1.99532463 -0.93273161 1.24128210 0.35214449
[37] 0.44159178 -1.94295468 -0.16392386 1.10203644 -1.04072038 0.70122817
[43] -1.49485498 -0.18822885 1.56081446 -0.12635636 -0.21116296 0.34794490
[49] -0.33368612 2.58583368 0.48966990 2.04735815 1.81100062 1.15848241
[55] 1.32350257 1.19200098 2.90611616 1.52354400 2.08384252 2.78166534
[61] 1.98808489 2.10868455 1.13102941 1.38700027 1.32561180 0.74761973
[67] 1.69976338 0.92707248 0.52836016 1.10963569 2.20815691 0.08199166
[73] -0.24409303 2.11882821 0.37105706 1.28156250 0.79914225 1.81198448
[79] 1.74938738 1.88698099 0.60554132 2.83966700 2.07380256 1.36490851
[85] 1.61233010 1.29095389 -0.82022696 0.32077905 1.22862291 1.32636166
[91] 1.78803458 1.96133414 1.40228470 -0.23327935 1.02744344 1.33685620
[97] 1.22817081 0.66956850 2.62300471
Chow test
data: Employed ~ Year + GNP.deflator + GNP + Armed.Forces
F = 3.9268, p-value = 0.06307
Chow test
data: Employed ~ Year + GNP.deflator + GNP + Armed.Forces
F = 3.9268, p-value = 0.06307
Chow test
data: Employed ~ Year + GNP.deflator + GNP + Armed.Forces
F = 3.9268, p-value = 0.06307
intubate documentation built on May 2, 2019, 2:46 p.m.
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Description Usage Arguments Details Value Author(s) Examples
Description
Interfaces to strucchange functions that can be used
in a pipeline implemented by magrittr.
Usage
1 2 3 4 5 6 | ntbt_breakpoints(data, ...)
ntbt_efp(data, ...)
ntbt_Fstats(data, ...)
ntbt_mefp(data, ...)
ntbt_recresid(data, ...)
ntbt_sctest(data, ...)
|
Arguments
data |
data frame, tibble, list, ... |
... |
Other arguments passed to the corresponding interfaced function. |
Details
Interfaces call their corresponding interfaced function.
Value
Object returned by interfaced function.
Author(s)
Roberto Bertolusso
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 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 | ## Not run:
library(intubate)
library(magrittr)
library(strucchange)
## ntbt_breakpoints: Dating Breaks
data("Nile")
d <- list(Nl = Nile)
## Original function to interface
breakpoints(Nl ~ 1, data = d)
## The interface puts data as first parameter
ntbt_breakpoints(d, Nl ~ 1)
## so it can be used easily in a pipeline.
d %>%
ntbt_breakpoints(Nl ~ 1)
## ntbt_efp: Empirical Fluctuation Processes
## Original function to interface
ocus.nile <- efp(Nl ~ 1, d, type = "OLS-CUSUM")
plot(ocus.nile)
## The interface puts data as first parameter
ocus.nile <- ntbt_efp(d, Nl ~ 1, type = "OLS-CUSUM")
plot(ocus.nile)
## so it can be used easily in a pipeline.
d %>%
ntbt_efp(Nl ~ 1, type = "OLS-CUSUM") %>%
plot()
## ntbt_Fstats: F Statistics
## Original function to interface
fs.nile <- Fstats(Nl ~ 1, data = d)
plot(fs.nile)
## The interface puts data as first parameter
fs.nile <- ntbt_Fstats(d, Nl ~ 1)
plot(fs.nile)
## so it can be used easily in a pipeline.
d %>%
ntbt_Fstats(Nl ~ 1) %>%
plot()
## ntbt_mefp: Monitoring of Empirical Fluctuation Processes
df1 <- data.frame(y = rnorm(300))
df1[150:300, "y"] <- df1[150:300, "y"] + 1
## Original function to interface
mefp(y ~ 1, data = df1[1:50,, drop = FALSE], type = "ME", h = 1, alpha = 0.05)
## The interface puts data as first parameter
ntbt_mefp(df1[1:50,, drop = FALSE], y ~ 1, type = "ME", h = 1, alpha = 0.05)
## so it can be used easily in a pipeline.
df1[1:50,, drop = FALSE] %>%
ntbt_mefp(y ~ 1, type = "ME", h = 1, alpha = 0.05)
## ntbt_recresid: Recursive Residuals
d1 <- list(x = rnorm(100) + rep(c(0, 2), each = 50))
## Original function to interface
recresid(x ~ 1, d1)
## The interface puts data as first parameter
ntbt_recresid(d1, x ~ 1)
## so it can be used easily in a pipeline.
d1 %>%
ntbt_recresid(x ~ 1)
## ntbt_sctest: Structural Change Tests in Linear Regression Models
data("longley")
## Original function to interface
sctest(Employed ~ Year + GNP.deflator + GNP + Armed.Forces, data = longley,
type = "Chow", point = 7)
## The interface puts data as first parameter
ntbt_sctest(longley, Employed ~ Year + GNP.deflator + GNP + Armed.Forces,
type = "Chow", point = 7)
## so it can be used easily in a pipeline.
longley %>%
ntbt_sctest(Employed ~ Year + GNP.deflator + GNP + Armed.Forces,
type = "Chow", point = 7)
## End(Not run)
|
Example output
Loading required package: zoo
Attaching package: 'zoo'
The following objects are masked from 'package:base':
as.Date, as.Date.numeric
Loading required package: sandwich
Optimal 2-segment partition:
Call:
breakpoints.formula(formula = Nl ~ 1, data = d)
Breakpoints at observation number:
28
Corresponding to breakdates:
1898
Optimal 2-segment partition:
Call:
breakpoints.formula(formula = Nl ~ 1)
Breakpoints at observation number:
28
Corresponding to breakdates:
1898
Optimal 2-segment partition:
Call:
breakpoints.formula(formula = Nl ~ 1)
Breakpoints at observation number:
28
Corresponding to breakdates:
1898
Monitoring with ME test (moving estimates test)
Initial call:
mefp.formula(formula = y ~ 1, type = "ME", data = df1[1:50, , drop = FALSE], h = 1, alpha = 0.05)
Last call:
mefp.formula(formula = y ~ 1, type = "ME", data = df1[1:50, , drop = FALSE], h = 1, alpha = 0.05)
Significance level : 0.05
Critical value : 2.745928
History size : 50
Last point evaluated : 50
Parameter estimate on history :
(Intercept)
-0.04086475
Monitoring with ME test (moving estimates test)
Initial call:
mefp.formula(formula = y ~ 1, type = "ME", h = 1, alpha = 0.05)
Last call:
mefp.formula(formula = y ~ 1, type = "ME", h = 1, alpha = 0.05)
Significance level : 0.05
Critical value : 2.745928
History size : 50
Last point evaluated : 50
Parameter estimate on history :
(Intercept)
-0.04086475
Monitoring with ME test (moving estimates test)
Initial call:
mefp.formula(formula = y ~ 1, type = "ME", h = 1, alpha = 0.05)
Last call:
mefp.formula(formula = y ~ 1, type = "ME", h = 1, alpha = 0.05)
Significance level : 0.05
Critical value : 2.745928
History size : 50
Last point evaluated : 50
Parameter estimate on history :
(Intercept)
-0.04086475
[1] -0.84742456 0.63460439 0.16460140 -1.82085041 -0.09269521 0.61683494
[7] -0.22813883 -0.01804020 0.38545286 0.43179387 0.47249884 0.50615285
[13] 0.15246908 0.75547867 0.36549671 0.57828183 -1.05035365 0.33376842
[19] -1.48588592 0.06791984 0.60260434 -1.00302918 -0.12102735 0.54974603
[25] -1.39209691 -0.89466377 -1.67842843 -0.60945465 -2.32244299 2.55222863
[31] -1.93651653 -0.84631663 1.99532463 -0.93273161 1.24128210 0.35214449
[37] 0.44159178 -1.94295468 -0.16392386 1.10203644 -1.04072038 0.70122817
[43] -1.49485498 -0.18822885 1.56081446 -0.12635636 -0.21116296 0.34794490
[49] -0.33368612 2.58583368 0.48966990 2.04735815 1.81100062 1.15848241
[55] 1.32350257 1.19200098 2.90611616 1.52354400 2.08384252 2.78166534
[61] 1.98808489 2.10868455 1.13102941 1.38700027 1.32561180 0.74761973
[67] 1.69976338 0.92707248 0.52836016 1.10963569 2.20815691 0.08199166
[73] -0.24409303 2.11882821 0.37105706 1.28156250 0.79914225 1.81198448
[79] 1.74938738 1.88698099 0.60554132 2.83966700 2.07380256 1.36490851
[85] 1.61233010 1.29095389 -0.82022696 0.32077905 1.22862291 1.32636166
[91] 1.78803458 1.96133414 1.40228470 -0.23327935 1.02744344 1.33685620
[97] 1.22817081 0.66956850 2.62300471
[1] -0.84742456 0.63460439 0.16460140 -1.82085041 -0.09269521 0.61683494
[7] -0.22813883 -0.01804020 0.38545286 0.43179387 0.47249884 0.50615285
[13] 0.15246908 0.75547867 0.36549671 0.57828183 -1.05035365 0.33376842
[19] -1.48588592 0.06791984 0.60260434 -1.00302918 -0.12102735 0.54974603
[25] -1.39209691 -0.89466377 -1.67842843 -0.60945465 -2.32244299 2.55222863
[31] -1.93651653 -0.84631663 1.99532463 -0.93273161 1.24128210 0.35214449
[37] 0.44159178 -1.94295468 -0.16392386 1.10203644 -1.04072038 0.70122817
[43] -1.49485498 -0.18822885 1.56081446 -0.12635636 -0.21116296 0.34794490
[49] -0.33368612 2.58583368 0.48966990 2.04735815 1.81100062 1.15848241
[55] 1.32350257 1.19200098 2.90611616 1.52354400 2.08384252 2.78166534
[61] 1.98808489 2.10868455 1.13102941 1.38700027 1.32561180 0.74761973
[67] 1.69976338 0.92707248 0.52836016 1.10963569 2.20815691 0.08199166
[73] -0.24409303 2.11882821 0.37105706 1.28156250 0.79914225 1.81198448
[79] 1.74938738 1.88698099 0.60554132 2.83966700 2.07380256 1.36490851
[85] 1.61233010 1.29095389 -0.82022696 0.32077905 1.22862291 1.32636166
[91] 1.78803458 1.96133414 1.40228470 -0.23327935 1.02744344 1.33685620
[97] 1.22817081 0.66956850 2.62300471
[1] -0.84742456 0.63460439 0.16460140 -1.82085041 -0.09269521 0.61683494
[7] -0.22813883 -0.01804020 0.38545286 0.43179387 0.47249884 0.50615285
[13] 0.15246908 0.75547867 0.36549671 0.57828183 -1.05035365 0.33376842
[19] -1.48588592 0.06791984 0.60260434 -1.00302918 -0.12102735 0.54974603
[25] -1.39209691 -0.89466377 -1.67842843 -0.60945465 -2.32244299 2.55222863
[31] -1.93651653 -0.84631663 1.99532463 -0.93273161 1.24128210 0.35214449
[37] 0.44159178 -1.94295468 -0.16392386 1.10203644 -1.04072038 0.70122817
[43] -1.49485498 -0.18822885 1.56081446 -0.12635636 -0.21116296 0.34794490
[49] -0.33368612 2.58583368 0.48966990 2.04735815 1.81100062 1.15848241
[55] 1.32350257 1.19200098 2.90611616 1.52354400 2.08384252 2.78166534
[61] 1.98808489 2.10868455 1.13102941 1.38700027 1.32561180 0.74761973
[67] 1.69976338 0.92707248 0.52836016 1.10963569 2.20815691 0.08199166
[73] -0.24409303 2.11882821 0.37105706 1.28156250 0.79914225 1.81198448
[79] 1.74938738 1.88698099 0.60554132 2.83966700 2.07380256 1.36490851
[85] 1.61233010 1.29095389 -0.82022696 0.32077905 1.22862291 1.32636166
[91] 1.78803458 1.96133414 1.40228470 -0.23327935 1.02744344 1.33685620
[97] 1.22817081 0.66956850 2.62300471
Chow test
data: Employed ~ Year + GNP.deflator + GNP + Armed.Forces
F = 3.9268, p-value = 0.06307
Chow test
data: Employed ~ Year + GNP.deflator + GNP + Armed.Forces
F = 3.9268, p-value = 0.06307
Chow test
data: Employed ~ Year + GNP.deflator + GNP + Armed.Forces
F = 3.9268, p-value = 0.06307
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