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 '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 '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, ...)

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

# 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 '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.