Description Usage Arguments Details Value References Examples

View source: R/master_functions.R

`autocorrelation_coeff_plot`

Computes the 1-alpha upper confidence bounds for the functional
autocorrelation coefficients at lags h = 1:K under both weak white noise (WWN) and strong white
noise (SWN) assumptions. It plots the coefficients as well as the bounds for all lags h = 1:K.
Note, the SWN bound is constant, while the WWN is dependent on the lag.

1 2 3 4 5 6 7 8 | ```
autocorrelation_coeff_plot(
f_data,
K = 20,
alpha = 0.05,
M = NULL,
low_disc = FALSE,
wwn_bound = TRUE
)
``` |

`f_data` |
The functional data matrix with observed functions in the columns. |

`K` |
A positive Integer value. The maximum lag for which to compute the single-lag test (tests will be computed for lags h in 1:K). |

`alpha` |
A numeric value between 0 and 1 specifying the significance level to be used in the single-lag test. The default value is 0.05. |

`M` |
A positive Integer value. Determines the number of Monte-Carlo simulations employed in the Welch-Satterthwaite approximation of the limiting distribution of the test statistics, for each test. |

`low_disc` |
A Boolean value, FALSE by default. If given TRUE, uses low-discrepancy sampling in the Monte-Carlo method. Note, low-discrepancy sampling will yield deterministic results. Requires the 'fOptions' package. |

`wwn_bound` |
A Boolean value allowing the user to turn off the weak white noise bound. TRUE by default. Speeds up computation when FALSE. |

This function computes and plots autocorrelation coefficients at lag h, for h in 1:K. It also computes an estimated asymptotic 1 - alpha confidence bound, under the assumption that the series forms a weak white noise. Additionally, it computes a similar (constant) bound under the assumption the series form a strong white noise. Please see the vignette or the references for a more complete treatment.

Plot of the estimated autocorrelation coefficients for lags h in 1:K with the weak white noise 1-alpha upper confidence bound for each lag, as well as the constant strong white noise 1-alpha confidence bound.

[1] Kokoszka P., & Rice G., & Shang H.L. (2017). Inference for the autocovariance of a functional time series under conditional heteroscedasticity. Journal of Multivariate Analysis, 162, 32-50.

1 2 3 | ```
b <- brown_motion(75, 40)
autocorrelation_coeff_plot(b)
autocorrelation_coeff_plot(b, M = 200, low_disc = TRUE)
``` |

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