population.est {BrainCon} | R Documentation |
Estimate population-level partial correlation coefficients in time series data.
And also return coefficients for each individual.
Input time series data for population as a 3-dimensional array or a list.
population.est(
Z,
lambda = NULL,
type = c("slasso", "lasso"),
alpha = 0.05,
ind.ci = FALSE
)
Z |
If each individual shares the same number of periods of time, |
lambda |
a scalar or a m-length vector, representing the penalty parameters of order |
type |
a character string representing the method of estimation. |
alpha |
a numeric scalar, default value is |
ind.ci |
a logical indicating whether to compute |
A popEst
class object containing two components.
coef
a p*p
partial correlation coefficients matrix.
ind.est
a m
-length list, containing estimates for each individuals.
type
regression type in estimation.
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.
## Quick example for the population-level estimates
data(popsimA)
# estimating partial correlation coefficients by scaled lasso
pc = population.est(popsimA)
## Inference on the first subject in population
Res_1 = individual.test(pc$ind.est[[1]])