plot.arfimaMLM: ArfimaMLM plotting function

In pwkraft/ArfimaMLM: Arfima-MLM Estimation for Repeated Cross-Sectional Data

ArfimaMLM plotting function

Description

Function for plotting time-varying coefficients generated by Arfima-MLM package

Usage

## S3 method for class 'arfimaMLM'
plot(
  x,
  ...,
  CoefName,
  TimeVar,
  loess = NULL,
  title = NULL,
  xaxis = NULL,
  yaxis = NULL
)

Arguments

x	Object of class ArfimaMLM
...	Other arguments passed down, currently not implemented.
CoefName	Name of time varying coefficient you wish to plot.
TimeVar	Variable specifying unit of time.
loess	TRUE sets loess smoother on. FALSE disables features. Defaults to TRUE.
title	Character string to pass on to ggtitle.
xaxis	Character string to pass on to ggplot2. Defaults to "Date" if left blank.
yaxis	Character string to pass on to ggplot2. Defaults to coefficient name if left blank.

Author(s)

David L Stack, [e-mail]

Examples

require(fracdiff)
t = 100 # number of time points
n = 500 # number of observations within time point
N = t*n # total number of observations

### generate fractional ARIMA Time Series for y_t, x1_t, z1_t, z2_t
set.seed(123)
y_t <- fracdiff.sim(t, d=0.4, mu=10)$series
x1_t <- fracdiff.sim(t, d=0.3, mu=5)$series
z1_t <- fracdiff.sim(t, d=0.1, mu=2)$series
z2_t <- fracdiff.sim(t, d=0.25, mu=3)$series

### simulate data
data <- NULL; data$time <- rep(seq(1:t),each=n)
data <- data.frame(data)
data$x1 <- rnorm(N,rep(x1_t,each=n),2)
data$x2 <- rnorm(N,0,40)
data$z1 <- rnorm(N,rep(z1_t,each=n),3)
data$z2 <- rep(z2_t,each=n)
b1 <- 0.2+rep(rnorm(t,0,0.1),each=n)
data$y <- (b1*data$x1-0.05*data$x2+0.3*rep(z1_t,each=n)
           +0*data$z2+rnorm(N,rep(y_t,each=n),1))
m1 <- arfimaMLM(y.ydif ~ x1.xdif + x2 + z1.fd + z2.fd + (1 + x1.xdif|time)
                , data = data, timevar = "time")
plot(x = m1, 
     CoefName = "x1.xdif", 
     TimeVar = "time")

pwkraft/ArfimaMLM documentation built on March 29, 2022, 3:20 p.m.