# ARp: Autoregressive model of order _p_ In CoSMoS: Complete Stochastic Modelling Solution

## Description

Generates time series from an Autoregressive model of order p.

## Usage

 `1` ```ARp(margdist, margarg, acsvalue, actfpara, n, p = NULL, p0 = 0) ```

## Arguments

 `margdist` target marginal distribution `margarg` list of marginal distribution arguments `acsvalue` target auto-correlation structure (from lag 0) `actfpara` auto-correlation structure transformation parameters `n` number of values `p` integer - model order (if NULL - limits maximum model order according to auto-correlation structure values) `p0` probability zero

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42``` ```library(CoSMoS) ## choose the marginal distribution as Pareto type II with corresponding parameters dist <- 'paretoII' distarg <- list(scale = 1, shape = .3) p0 <- .5 ## estimate rho 'x' and 'z' points using ACTI pnts <- actpnts(margdist = dist, margarg = distarg, p0 = p0) ## fit ACTF fit <- fitactf(pnts) ## define target auto-correlation structure and model order order <- 1000 acsvalue <- acs(id = 'weibull', t = 0:order, scale = 10, shape = .75) ## limit ACS lag (recomended) system.time(val <- ARp(margdist = dist, margarg = distarg, acsvalue = acsvalue, actfpara = fit, n = 5000, p0 = p0)) ## order w/o limit system.time(val <- ARp(margdist = dist, margarg = distarg, acsvalue = acsvalue, actfpara = fit, n = 5000, p = order, p0 = p0)) ## see the result ggplot() + geom_col(aes(x = seq_along(val), y = val)) + labs(x = '', y = 'value') + theme_classic() ```

CoSMoS documentation built on May 30, 2021, 1:06 a.m.