AicBic

Description

Compute the AIC and BIC criteria for lags from 1 to q.max

Usage

AicBic(mat.y, q.max, mat.x = NULL, trend = FALSE, intercept = TRUE)

Arguments

Value

A data frame object that contains all AIC (first row) and BIC (second row) for all the q.max lags tested.

Examples

data(example_data)
aic.bic <- AicBic(mat.y = example_data,
 q.max = 2,
  trend = FALSE,
   intercept = TRUE)

Beta

Description

#Compute the matrix of parameters and the covariance matrix of errors in OLS, FGLS, or IGLS mode.

Usage

Beta(mat.z, mat.y.ex, n.eq, p, est.mode, iter)

Arguments

Value

A list with the matrix of beta parameters as first element and the covariance matrix of error as second element.

ConfidenceInterval

Description

Compute the confidence interval in time unit.

Usage

ConfidenceInterval(mat.g, mat.s, mat.sigma, mat.r, mat.beta, cv, p)

Arguments

Value

The difference in time unit around the break

ConformableMatrix

Description

Compute the list of matrices with correct dimensions to pass later in the computation

Usage

ConformableMatrix(mat.y, q, mat.x = NULL, trend = FALSE, intercept = TRUE)

Arguments

Value

A list of conformed matrices

Examples

data(example_data)
conf.matrix <- ConformableMatrix(mat.y = example_data, q = 2)

Fstat

Description

Compute the f-statistic for the break test

Usage

Fstat(mat.r, mat.beta, mat.z, p, mat.sigma)

Arguments

Value

The f-statistic scalar

Lags

Description

Compute the lags for the mat.y time series matrix

Usage

Lags(mat.y, q)

Arguments

Value

A list of original (dependent) and lagged (independent) time series matrix

Examples

data(example_data)
list.lags <- Lags(mat.y = example_data, q = 2)

Main

Description

Entry point for the whole computation of the algorithm of Bai, Lumsdaine, and Stock (1998)

Usage

Main(
  mat.y,
  mat.x = NULL,
  trend = FALSE,
  intercept = TRUE,
  ci = c(0.9, 0.95, 0.99),
  est.mode = "OLS",
  iter = 3,
  aic.bic.mode = "AIC",
  q.max = 2,
  trim = 0.15,
  pos.break = FALSE
)

Arguments

Value

A list of the vector of f-statistics, the maximum f-statistic retained, the confidence interval, the critical values, the break date, the original matrix of time series tested, the matrix with breaking and not breaking covariates, the index of the break in the time series, the size of the break (mean.shift), the optimal "AIC" or "BIC", a ggplot object (g1), and the trimmed dates.

Examples

data(example_data)
list.results <- Main(mat.y = example_data, q = 2)

PlotStats

Description

Generate a ggplot2 object to depict the break and the time series tested

Usage

PlotStats(my.dates, my.vars, f.stat, mat.ci = mat.ci, mat.y)

Arguments

Value

A ggplot2 object

Sigma

Description

#compute the covariance matrix of errors as in Bai, Lumsdaine, and Stock (1998)

Usage

Sigma(mat.z, mat.y.ex, mat.beta, n.eq)

Arguments

Value

The covariance matrix of errors

Simul

Description

#Simulate data to test the functions

Usage

Simul(n = 100, p = 5, intensity = 1, when.break = 0.5)

Arguments

Value

A matrix of time series with a common break

Examples

data(example_data)
simul.data <- Simul(n = 100, p = 5, intensity = 1, when.break = 0.5)

Vdistr

Description

Computes the critical values for a vector of confidence intervals proposed (ci)

Usage

Vdistr(ci)

Arguments

Value

A vector of critical values

Examples

vect.cv <- Vdistr(ci = c(0.9, 0.95, 0.99))

Example MultibreakeR simulated data

Description

Data generated with the function Simul() of the multibreakeR package with 100 time series observations (n = 100), five time series (p = 5), a break intensity of 1 (intensity = 1), and a break occuring at 35% of the sample (when.break = 0.35). These are also the default arguments of the Simul() function.

Usage

data(example_data)

Format

A matrix object

Source

https://github.com/loicym/multibreakeR

References

MultibreakeR generated data

Examples

list.breaks <- Main(mat.y = Simul(p = 2, when.break = 0.5),
 mat.x = NULL,
  trend = FALSE,
   intercept = TRUE,
    ci = c(0.9, 0.95, 0.99),
     est.mode = "OLS",
      iter = 3,
       aic.bic.mode = "AIC",
        q.max = 2,
         trim = 0.4,
          pos.break = FALSE)

Tidy eval helpers

Description

This page lists the tidy eval tools reexported in this package from rlang. To learn about using tidy eval in scripts and packages at a high level, see the dplyr programming vignette and the ggplot2 in packages vignette. The Metaprogramming section of Advanced R may also be useful for a deeper dive.

• The tidy eval operators ⁠{{⁠, ⁠!!⁠, and ⁠!!!⁠ are syntactic constructs which are specially interpreted by tidy eval functions. You will mostly need ⁠{{⁠, as ⁠!!⁠ and ⁠!!!⁠ are more advanced operators which you should not have to use in simple cases.

  The curly-curly operator ⁠{{⁠ allows you to tunnel data-variables passed from function arguments inside other tidy eval functions. ⁠{{⁠ is designed for individual arguments. To pass multiple arguments contained in dots, use ... in the normal way.

  my_function <- function(data, var, ...) {
    data %>%
      group_by(...) %>%
      summarise(mean = mean({{ var }}))
  }
  

• enquo() and enquos() delay the execution of one or several function arguments. The former returns a single expression, the latter returns a list of expressions. Once defused, expressions will no longer evaluate on their own. They must be injected back into an evaluation context with ⁠!!⁠ (for a single expression) and ⁠!!!⁠ (for a list of expressions).

  my_function <- function(data, var, ...) {
    # Defuse
    var <- enquo(var)
    dots <- enquos(...)
  
    # Inject
    data %>%
      group_by(!!!dots) %>%
      summarise(mean = mean(!!var))
  }
  

  In this simple case, the code is equivalent to the usage of ⁠{{⁠ and ... above. Defusing with enquo() or enquos() is only needed in more complex cases, for instance if you need to inspect or modify the expressions in some way.

• The .data pronoun is an object that represents the current slice of data. If you have a variable name in a string, use the .data pronoun to subset that variable with [[.

  my_var <- "disp"
  mtcars %>% summarise(mean = mean(.data[[my_var]]))
  

• Another tidy eval operator is ⁠:=⁠. It makes it possible to use glue and curly-curly syntax on the LHS of =. For technical reasons, the R language doesn't support complex expressions on the left of =, so we use ⁠:=⁠ as a workaround.

  my_function <- function(data, var, suffix = "foo") {
    # Use `{{` to tunnel function arguments and the usual glue
    # operator `{` to interpolate plain strings.
    data %>%
      summarise("{{ var }}_mean_{suffix}" := mean({{ var }}))
  }
  

• Many tidy eval functions like dplyr::mutate() or dplyr::summarise() give an automatic name to unnamed inputs. If you need to create the same sort of automatic names by yourself, use as_label(). For instance, the glue-tunnelling syntax above can be reproduced manually with:

  my_function <- function(data, var, suffix = "foo") {
    var <- enquo(var)
    prefix <- as_label(var)
    data %>%
      summarise("{prefix}_mean_{suffix}" := mean(!!var))
  }
  

  Expressions defused with enquo() (or tunnelled with ⁠{{⁠) need not be simple column names, they can be arbitrarily complex. as_label() handles those cases gracefully. If your code assumes a simple column name, use as_name() instead. This is safer because it throws an error if the input is not a name as expected.

Value

No return value