View source: R/population.est.R
population.est | 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 √{\log(p)/n_i} for each individual.
If a scalar, the penalty parameters used in each individual are the same.
If a m-length vector, the penalty parameters for each individual are specified in order.
And if |
type |
a character string representing the method of estimation. |
alpha |
a numeric scalar, default value is |
ind.ci |
a logical indicating whether to compute 1-α confidence intervals of each subject, default value is |
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]])
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