Objects exported from other packages

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

broom

augment, glance, tidy

stats

AIC, BIC, as.ts, coef, fitted, logLik, nobs, residuals, time

vctrs

vec_cast, vec_ptype2

zoo

index

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 'cga'
as.ts(x, ...)

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

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

# S3 method for class 'segmented'
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 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).