Function reference • pcts

Overview of package pcts

pcts-package: Periodically Correlated and Periodically Integrated Time Series

Create and manipulate periodic ts

pcts(): Create objects from periodic time series classes

nCycles() nTicks() nVariables(): Basic information about periodic ts objects

availStart() availEnd(): Time of first or last non-NA value

Vec() tsMatrix() tsVector() tsVec() pcMatrix() pcArray() pctsArray(): Core data of periodic time series

window(<PeriodicTS>) window(<PeriodicMTS>) na.trim(<PeriodicTS>) na.trim(<PeriodicMTS>) frequency(<PeriodicTimeSeries>) deltat(<PeriodicTimeSeries>) cycle(<PeriodicTimeSeries>) time(<PeriodicTimeSeries>) start(<Cyclic>) end(<Cyclic>): Periodic methods for base R functions

boxplot(<PeriodicTimeSeries>) monthplot(<PeriodicTimeSeries>): Plot periodic time series

pcMean(): Compute periodic mean

pcApply(): Apply a function to each season

Fit periodic ts models

fitPM(): Fit periodic time series models

pclsdf(): Fit PAR models using least squares

pclspiar(): Fit a periodically integrated autoregressive model

fit_trigPAR_optim(): Fit a subset trigonometric PAR model

num2pcpar(): Fit PAR model using sample autocorrelations

pcarma_acvf2model(): Fit a PC-ARMA model to a periodic autocovariance function

Characteristics of periodic objects

unitSeason() unitCycle() seqSeasons() allSeasons() `unitSeason<-`() `unitCycle<-`() `allSeasons<-`(): Get names of seasons

nSeasons-methods: Number of seasons for a periodic object

pi1ar2par() piar2par(): Convert PIAR coefficients to PAR coefficients

maxLag-methods: Methods for function maxLag() in package 'pcts'

autocorrelations-methods: Compute autocorrelations and periodic autocorrelations

autocovariances-methods: Compute autocovariances and periodic autocovariances

partialAutocorrelations-methods: Compute periodic partial autocorrelations

partialAutocovariances-methods: Compute periodic partial autocovariances

partialCoefficients-methods: Compute periodic partial coefficients

partialVariances-methods: Compute periodic partial variances

backwardPartialCoefficients-methods: Compute periodic backward partial coefficients

backwardPartialVariances-methods: Compute periodic backward partial variances

pc_sdfactor(): Compute normalising factors

Tests and asymptotics

pcTest(): Test for periodicity

test_piar(): Test for periodic integration

pwn_McLeodLjungBox_test(): McLeod-Ljung-Box test for periodic white noise

periodic_acf1_test(): McLeod's test for periodic autocorrelation

meanvarcheck() meancovmat(): Asymptotic covariance matrix of periodic mean

parcovmatlist(): Compute asymptotic covariance matrix for PAR model

pcacf_pwn_var(): Variances of sample periodic autocorrelations

Simulation

sim_pwn(): Simulate periodic white noise

sim_pc(): Simulate periodically correlated ARMA series

sim_parCoef(): Generate a periodic autoregression model

sim_parAcvf(): Create a random periodic autocovariance function

Datasets

dataFranses1996: Example data from Franses (1996)

Fraser2017: Fraser River at Hope, mean monthly flow

four_stocks_since2016_01_01: Data for four stocks since 2016-01-01

pcts_exdata(): Periodic time series objects for examples

Time series classes

PeriodicTS-class: Class "PeriodicTS"

PeriodicMTS-class: Class "PeriodicMTS"

PeriodicTS_ts-class: Class "PeriodicTS_ts"

PeriodicMTS_ts-class: Class "PeriodicMTS_ts"

PeriodicTS_zooreg-class: Class "PeriodicTS_zooreg"

PeriodicMTS_zooreg-class: Class "PeriodicMTS_zooreg"

PeriodicTimeSeries-class: Class PeriodicTimeSeries

zoo-class: Class zoo made S4

zooreg-class: Virtual S4 class zooreg

Cycle classes

pcCycle() BuiltinCycle(): Create or extract Cycle objects

BareCycle-class: Class BareCycle

BasicCycle-class: Class BasicCycle

BuiltinCycle-class: Class "BuiltinCycle" and its subclasses in package 'pcts'

PartialCycle-class: Class PartialCycle

SimpleCycle-class: Class SimpleCycle

Cyclic-class: Class "Cyclic"

modelCycle() `modelCycle<-`(): Get the cycle of a periodic object

unitCycle-methods: Methods for unitCycle() in package pcts

unitCycle: Methods for `unitCycle<-` in package pcts

unitSeason-methods: Methods for unitSeason() in package pcts

unitSeason: Methods for `unitSeason<-` in package pcts

seqSeasons-methods: Methods for seqSeasons() in package pcts

Datetimes and dates

Cyclic() as.Date(<Cyclic>) date(<Cyclic>) as.Date(<PeriodicTimeSeries>): Create objects from class Cyclic

Pctime() as_Pctime() `[`(<Pctime>) `[[`(<Pctime>): Pctime objects

as_date-methods: Replace methods for as_date in package pcts

as_datetime-methods: Methods for as_datetime in package pcts

date: Replace methods for date in package pcts

Periodic filtering

pc.filter(): Applies a periodic ARMA filter to a time series

pc.filter.xarma(): Filter time series with periodic arma filters

Periodic AR computations

pcAR2acf(): Compute periodic autocorrelations from PAR coefficients

pdSafeParOrder(): Functions for some basic operations with seasons

permean2intercept() intercept2permean(): Convert between periodic centering and intercepts

pc.acf.parModel() pcacfMat(): Compute PAR autocovariance matrix

alg1(): Periodic Levinson-Durbin algorithm

alg1util(): Give partial periodic autocorrelations or other partial prediction quantities for a pcAcvf object.

pcAr.ss(): Compute the sum of squares for a given PAR model

Periodic ARMA computations

pcarma_acvf_lazy() pcarma_h_lazy() pcarma_acvf_system() pcarma_param_system() pcarma_h(): Functions to compute various characteristics of a PCARMA model

pc.hat.h(): function to compute estimates of the h weights

pcarma_prepare() pcarma_unvec() pcarma_tovec(): Functions for work with a simple list specification of pcarma models

Classes

FittedPeriodicArModel-class: Class FittedPeriodicArModel

FittedPeriodicArmaModel-class: Class FittedPeriodicArmaModel

ModelCycleSpec-class: Class ModelCycleSpec

PartialPeriodicAutocorrelations-class: Class PartialPeriodicAutocorrelations

PeriodicArModel-class: Class PeriodicArModel

PeriodicArModel(): Create objects from class PeriodicArModel

PeriodicArmaFilter-class: Class "PeriodicArmaFilter"

PeriodicArmaModel-class: Class PeriodicArmaModel

PeriodicArmaSpec-class: Class PeriodicArmaSpec

PeriodicAutocorrelations-class: Class PeriodicAutocorrelations

PeriodicAutocovarianceModel-class: Class PeriodicAutocovarianceModel

PeriodicAutocovarianceSpec-class: Class PeriodicAutocovarianceSpec

PeriodicAutocovariances-class: Class PeriodicAutocovariances

PeriodicBJFilter-class: Class PeriodicBJFilter

PeriodicFilterModel-class: Class PeriodicFilterModel

PeriodicIntegratedArmaSpec-class: Class PeriodicIntegratedArmaSpec

PeriodicInterceptSpec-class: Class PeriodicInterceptSpec

PeriodicMaModel-class: Class PeriodicMaModel

PeriodicSPFilter-class: Class PeriodicSPFilter

PeriodicVector(): Class PeriodicVector

PiPeriodicArModel-class: Class PiPeriodicArModel

PiPeriodicArmaModel-class: Class PiPeriodicArmaModel

PiPeriodicMaModel-class: Class PiPeriodicMaModel

SamplePeriodicAutocorrelations-class: Class SamplePeriodicAutocorrelations

SamplePeriodicAutocovariances-class: Class SamplePeriodicAutocovariances

SiPeriodicArModel-class: Class SiPeriodicArModel

SiPeriodicArmaModel-class: Class SiPeriodicArmaModel

SiPeriodicMaModel-class: Class SiPeriodicMaModel

SubsetPM-class: Class SubsetPM

Multi-companion form

permodelmf(): Compute the multi-companion form of a per model

mC.ss() xx.ss(): Create environment for mc-fitting

Other

ex1f: An example PAR autocorrelation function

filterCoef-methods: Get the coefficients of a periodic filter

toSeason() toSeasonPair() ttTosl() ttmatToslPairs(): Functions for some basic operations with seasons

pcts-deprecated: Deprecated Functions in Package pcts

sigmaSq-methods: Methods for sigmaSq in package pcts

[-methods: Indexing of objects from classes in package pcts

[: Index assignments for objects from classes in package pcts

[[-methods: Methods for function`[[` in package 'pcts'

$-methods: Methods for function$ in package 'pcts'