Objects exported from other packages

These objects are imported from other packages. Follow the links below to see their documentation.

generics

augment, glance, tidy

Usage

# S3 method for class 'mod_cpt'
as.ts(x, ...)

# S3 method for class 'mod_cpt'
nobs(object, ...)

# S3 method for class 'mod_cpt'
logLik(object, ...)

# S3 method for class 'mod_cpt'
fitted(object, ...)

# S3 method for class 'mod_cpt'
residuals(object, ...)

# S3 method for class 'mod_cpt'
coef(object, ...)

# S3 method for class 'mod_cpt'
augment(x, ...)

# S3 method for class 'mod_cpt'
tidy(x, ...)

# S3 method for class 'mod_cpt'
glance(x, ...)

# S3 method for class 'mod_cpt'
plot(x, ...)

# S3 method for class 'mod_cpt'
print(x, ...)

# S3 method for class 'mod_cpt'
summary(object, ...)

# S3 method for class 'seg_basket'
as.ts(x, ...)

# S3 method for class 'seg_basket'
plot(x, ...)

# S3 method for class 'seg_cpt'
as.ts(x, ...)

# S3 method for class 'seg_cpt'
glance(x, ...)

# S3 method for class 'seg_cpt'
nobs(object, ...)

# S3 method for class 'seg_cpt'
print(x, ...)

# S3 method for class 'seg_cpt'
summary(object, ...)

# S3 method for class 'tidycpt'
as.ts(x, ...)

# S3 method for class 'tidycpt'
augment(x, ...)

# S3 method for class 'tidycpt'
tidy(x, ...)

# S3 method for class 'tidycpt'
glance(x, ...)

# S3 method for class 'tidycpt'
plot(x, use_time_index = FALSE, ylab = NULL, ...)

# S3 method for class 'tidycpt'
print(x, ...)

# S3 method for class 'tidycpt'
summary(object, ...)

# S3 method for class 'meanshift_lnorm'
logLik(object, ...)

# S3 method for class 'nhpp'
logLik(object, ...)

# S3 method for class 'nhpp'
glance(x, ...)

# S3 method for class 'ga'
as.ts(x, ...)

# S3 method for class 'ga'
nobs(object, ...)

# S3 method for class 'cpt'
as.ts(x, ...)

# S3 method for class 'cpt'
logLik(object, ...)

# S3 method for class 'cpt'
nobs(object, ...)

# S3 method for class 'cptga'
as.ts(x, ...)

# S3 method for class 'cptga'
nobs(object, ...)

# S3 method for class 'segmented'
as.ts(x, ...)

# S3 method for class 'stepmented'
as.ts(x, ...)

# S3 method for class 'breakpointsfull'
as.ts(x, ...)

# S3 method for class 'breakpointsfull'
nobs(object, ...)

# S3 method for class 'wbs'
as.ts(x, ...)

# S3 method for class 'wbs'
nobs(object, ...)

Arguments

...

some methods for this generic function require additional arguments.

object

any object from which a log-likelihood value, or a contribution to a log-likelihood value, can be extracted.

use_time_index

Should the x-axis labels be the time indices? Or the time labels?

Examples

# Plot a meanshift model fit
plot(fit_meanshift_norm(CET, tau = 330))

plot(fit_meanshift_norm(CET, tau = 330), plot.title.position = "plot")


#' # Plot a trendshift model fit
plot(fit_trendshift(CET, tau = 330))


#' # Plot a quadratic polynomial model fit
plot(fit_lmshift(CET, tau = 330, deg_poly = 2))


#' # Plot a 4th degree polynomial model fit
plot(fit_lmshift(CET, tau = 330, deg_poly = 10))


# Plot a segmented time series
plot(segment(CET, method = "pelt"))


# Plot a segmented time series and show the time labels on the x-axis
plot(segment(CET, method = "pelt"), use_time_index = TRUE)
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.


# Label the y-axis correctly
segment(CET, method = "pelt") |>
  plot(use_time_index = TRUE, ylab = "Degrees Celsius")
#> Scale for x is already present.
#> Adding another scale for x, which will replace the existing scale.

# Summarize a tidycpt object
summary(segment(CET, method = "pelt"))
#> 
#> ── Summary of tidycpt object ───────────────────────────────────────────────────
#> → y: Contains 366 observations, ranging from 6.86  to 11.18  .
#> ℹ Segmenter (class cpt )
#> → A: Used the PELT algorithm from the changepoint  package.
#> → τ: Found 5 changepoint(s).
#> → f: Reported a fitness value of Inf using the MBIC penalty.
#> ℹ Model
#> → M: Fit the meanvar  model.
#> → θ: Estimated 2 parameter(s), for each of 6 region(s).
summary(segment(DataCPSim, method = "pelt"))
#> 
#> ── Summary of tidycpt object ───────────────────────────────────────────────────
#> → y: Contains 1096 observations, ranging from 13.67  to 298.98  .
#> ℹ Segmenter (class cpt )
#> → A: Used the PELT algorithm from the changepoint  package.
#> → τ: Found 3 changepoint(s).
#> → f: Reported a fitness value of 9.40 k using the MBIC penalty.
#> ℹ Model
#> → M: Fit the meanvar  model.
#> → θ: Estimated 2 parameter(s), for each of 4 region(s).