as.ts.mod_cpt | R Documentation |
These objects are imported from other packages. Follow the links below to see their documentation.
augment
, glance
, tidy
AIC
, as.ts
, BIC
, coef
, fitted
, logLik
, nobs
, residuals
, time
vec_cast
, vec_ptype2
index
## 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, ...)
... |
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? |
# 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)
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