N_break_point

Description

Look for several breakpoints

Usage

  N_break_point(serie, n_max = 1, n_period=10,
                          seed=FALSE, auto_select = FALSE,
                          alpha = NULL,method='SNHT',dstr='norm',
                          seed_set = 9658, change_random_seed = TRUE,
                          seed_method = 6842)

Arguments

Details

Compute homogeneity test for all possible breaks in the serie considering several breakpoints. NA values are allow. In order to guarantee same result for the same input seed_set and seed_method (if method in SNHT or buishand) must be given.

Value

N_break_point returns a list with the breakpoints index, it's p value and how many breakpoints are. If auto_select = F, a list with one list as specify for each n breakpoint tried

breaks

index where the breakpoints are found

p.value

p value of the test

n

how many breakpoints are looked for

References

Hurtado, S. I., Zaninelli, P. G., & Agosta, E. A. (2020). A multi-breakpoint methodology to detect changes in climatic time series. An application to wet season precipitation in subtropical Argentina. Atmospheric Research, 104955.

Examples

  # Make a serie with three jumps, same as yamamoto example
  set.seed(524)
  x <- c(rnorm(30,1,1),rnorm(30,2,1),rnorm(30,1,1),rnorm(20,2,1))

  # Look up to 5 breaks using pettit
  break_prosition <- N_break_point(serie=x, n_max = 5, method='pettit',
                                    auto_select=TRUE,alpha=0.1)

  plot(x)
  abline(v = break_prosition$breaks, col='red')

Buishand Range Test and Standard Normal Homogeneity Test

Description

Compute Buishand Range Test or Standard Normal Homogeneity Test for a serie, NAs allow in both Test

Usage

  SNHT(serie,n_period=10,dstr='norm',simulations = 1000,
                  seed_set = 9658, change_random_seed = TRUE)

  Buishand_R(serie,n_period=10,dstr='norm',simulations = 1000,
                  seed_set = 9658, change_random_seed = TRUE)

Arguments

Details

SNHT compute Standard Normal Homogeneity Test where NA values are allow. In order to guarantee same result for the same input seed_set must be given.

Buishand_R Compute Buishand Range Test for Homogeneity where NA values are allow. In order to guarantee same result for the same input seed_set must be given.

Value

SNHT and Buishand_R returns a list with the breakpoint index and it's p value

breaks

index where the breakpoint is found

p.value

p value of the test

References

- Alexandersson, H., jan 1986. A homogeneity test applied to precipitation data. Journal of Climatology 6 (6), 661–675. URL http://doi.wiley.com/10.1002/joc.3370060607

- Buishand, T., aug 1982. Some methods for testing the homogeneity of rainfall records. Journal of Hydrology 58 (1-2), 11–27. URL https://doi.org/10.1016/0022-1694(82)90066-X

Examples

  # Make a serie with one breakpoint
  x <- c(rnorm(60,1,1),rnorm(40,2,1))

  # Look for break using SNHT, Buishand_R can be used in exactly the same way
  break_prosition <- SNHT(serie = x)

  plot(x)
  abline(v = break_prosition$breaks)

Mann-Whitney-Wilcoxon Test, Student t-test and Pettit Test

Description

Compute Rolling Mann-Whitney-Wilcoxon Test, Rolling Student t-test and Pettit test for homogeneity, NAs allow.

Usage

  stu(serie,n_period=10)

  man.whi(serie,n_period=10)

  pettit(serie,n_period=10)

Arguments

Details

man.whi compute Mann-Whitney-Wilcoxon Test and stu the Student t-test for all possible breaks in the serie and get the most significant break.In both test NA values are allow.

pettit Compute the Pettit Test for Homogeneity. NA values are allow.

Value

pettit, man.whi and stu returns a list with the breakpoint index and its p value

breaks

index where the breakpoint is found

p.value

p value of the test

References

- Ruxton, G. D., jul 2006. The unequal variance t-test is an underused alternative to Student’s t-test and the Mann–Whitney U test. Behavioral Ecology 17 (4), 688–690. URL: http://academic.oup.com/beheco/article/17/4/688/215960/The-unequal-variance-ttest-is-an-underused

- Mann, H. B., Whitney, D. R., mar 1947. On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other. The Annals of Mathematical Statistics 18 (1), 50–60. URL http://projecteuclid.org/euclid.aoms/1177730491

- Pettitt, A. N., 1979. A Non-Parametric Approach to the Change-Point Problem. Applied Statistics 28 (2), 126. URL https://www.jstor.org/stable/10.2307/2346729?origin=crossref

Examples

  # Make a serie with one breakpoint
  x <- c(rnorm(60,1,1),rnorm(40,2,1))

  # Look for break using pettit(), man.whi() and stu()
  break_prosition_pettit <- pettit(serie = x)
  break_prosition_man.whi <- man.whi(serie = x)
  break_prosition_stu <- stu(serie = x)

  plot(x)
  abline(v = break_prosition_pettit$breaks,col='red')
  abline(v = break_prosition_man.whi$breaks,col='blue')
  abline(v = break_prosition_stu$breaks,col= 'green')

Yamamoto

Description

Compute Yamamoto Test for climate jumps in a serie.

Usage

  yamamoto(serie, alpha = 0.1, n_period = 10)

Arguments

Details

yamamoto compute the Yamamoto Test.

Value

yamamoto returns a list with the breakpoints indexes and the amount

breaks

vector of indexes where the breakpoint is found

n

Amount of breakpoints

References

Yamamoto, R., Iwashima, T., Kazadi, S. N., & Hoshiai, M. (1985). Climatic jump: a hypothesis in climate diagnosis. Journal of the Meteorological Society of Japan. Ser. II, 63(6), 1157-1160.

Examples

  # Make a serie with three jumps, same as N_break_point example
  set.seed(524)
  x <- c(rnorm(30,1,1),rnorm(30,2,1),rnorm(30,1,1),rnorm(20,2,1))

  # Look for break using yamamoto()
  break_prosition <- yamamoto(serie = x)

  plot(x)
  abline(v = break_prosition$breaks, col='red')