This function calculates wavelet variance confidence intervals
for the unbiased and block averaged discrete wavelet variance estimates.
Given *var{tau(j)}* are the
time independent unbiased wavelet variance estimates at scales
*tau(j)* where
*j* are the
decomposition levels, the approximate
*100(1-2p)*% confidence interval is given by

*[n * var{tau(j)} / Q(1-p), n * var{tau(j)} / Q(p)]*

where *Q(p)* is the
*p x 100* percentage
point for a chi-squared distribution with n degrees of freedom distribution.

1 | ```
wavVarConfidence(wvar, edof, probability=0.95)
``` |

`wvar` |
a vector containing the block-averaged unbiased wavelet variance estimates. |

`edof` |
a vector containing the equivalent degrees of freedom estimates. See |

`probability` |
the probability desired for the confidence
intervals. Supported probabilities are 0.005, .025, .05, .95, .975, and .995. Default: |

a list of the low and high confidence interval limits for
levels *1,..., J*.

D. B. Percival and A. T. Walden,
*Wavelet Methods for Time Series Analysis*, Cambridge University Press, 2000.

`wavVar`

, `wavEDOF`

.

1 2 3 4 5 6 | ```
## first calculate the EDOF for the ocean series
edof <- wavEDOF(ocean)
## calculate the 95% confidence intervals for EDOF
## mode 1
wavVarConfidence(edof$variance.unbiased, edof$EDOF1)
``` |

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