# mwar.ani: Demonstration for "Moving Window Auto-Regression" In animation: A Gallery of Animations in Statistics and Utilities to Create Animations

## Description

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.

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10``` ```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, ... ) ```

## Arguments

 `x` univariate time-series (a single numerical vector); default to be `sin(seq(0, 2 * pi, length = 50)) + rnorm(50, sd = 0.2)` `k` an integer of the window width `conf` a positive number: the confidence intervals are computed as `c(ar1 - conf*s.e., ar1 + conf*s.e.)` `mat, widths, heights` arguments passed to `layout` to divide the device into 2 parts `lty.rect` the line type of the rectangles respresenting the moving “windows” `...` other arguments passed to `points` in the bottom plot (the centers of the arrows)

## Details

The AR(1) coefficients are computed by `arima`.

## Value

A list containing

 `phi ` the AR(1) coefficients `L ` lower bound of the confidence interval `U ` upper bound of the confidence interval

Yihui Xie

## References

Robert A. Meyer, Jr. Estimating coefficients that change over time. International Economic Review, 13(3):705-710, 1972.

`arima`