Description Usage Arguments Value References Examples

View source: R/individual.est.R

Estimate individual-level partial correlation coefficients in time series data
with *1-α* confidence interval.
It's not a joint confidence interval for multiple tests.

1 | ```
individual.est(X, alpha = 0.05, lambda = NULL, ci = TRUE)
``` |

`X` |
time series data of an individual which is a |

`alpha` |
significance level, default value is |

`lambda` |
a penalty parameter used in lasso of order |

`ci` |
a logical indicating whether to compute |

An `indEst`

class object containing two or four components.

`coef`

a *p*p* partial correlation coefficients matrix.

`ci.lower`

a *p*p* numeric matrix containing the lower bound of *1-α* confidence interval,
returned if `ci`

is `TRUE`

.

`ci.upper`

a *p*p* numeric matrix containing the upper bound of *1-α* confidence interval,
returned if `ci`

is `TRUE`

.

`asym.ex`

a matrix measuring the asymptotical expansion of estimates, which will be used for multiple tests.

Qiu Y. and Zhou X. (2021).
Inference on multi-level partial correlations
based on multi-subject time series data,
*Journal of the American Statistical Association*, 00, 1-15

1 2 3 | ```
## Quick example for the individual-level estimates
data(indsim)
pc = individual.est(indsim) # estimating partial correlation coefficients
``` |

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