This function just fulfills a very naive idea about moving window regression using rectangles to denote the ``windows'' and move them, and the corresponding AR(1) coefficients as long as rough confidence intervals are computed for data points inside the ``windows'' during the process of moving.
mwar.ani(
x,
k = 15,
conf = 2,
mat = matrix(1:2, 2),
widths = rep(1, ncol(mat)),
heights = rep(1, nrow(mat)),
lty.rect = 2,
...
)univariate time-series (a single numerical vector); default to be
sin(seq(0, 2 * pi, length = 50)) + rnorm(50, sd = 0.2)
an integer of the window width
a positive number: the confidence intervals are computed as
c(ar1 - conf*s.e., ar1 + conf*s.e.)
arguments passed to layout to divide
the device into 2 parts
the line type of the rectangles respresenting the moving ``windows''
other arguments passed to points in the bottom
plot (the centers of the arrows)
A list containing
the AR(1) coefficients
lower bound of the confidence interval
upper bound of the confidence interval
The AR(1) coefficients are computed by arima.
Examples at https://yihui.org/animation/example/mwar-ani/
Robert A. Meyer, Jr. Estimating coefficients that change over time. International Economic Review, 13(3):705-710, 1972.