population.est: Estimate population-level partial correlation coefficients

View source: R/population.est.R

population.estR Documentation

Estimate population-level partial correlation coefficients


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.


  lambda = NULL,
  type = c("slasso", "lasso"),
  alpha = 0.05,
  ind.ci = FALSE



If each individual shares the same number of periods of time, Z can be a n*p*m dimensional array, where m is number of individuals. In general, Z should be a m-length list, and each element in the list is a n_i*p matrix, where n_i stands for the number of periods of time of the i-th individual.


a scalar or a m-length vector, representing the penalty parameters of order \sqrt{\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 NULL, penalty parameters are specified by type. More details about the penalty parameters are in individual.est.


a character string representing the method of estimation. "slasso" means scaled lasso, and "lasso" means lasso. Default value is "slasso".


a numeric scalar, default value is 0.05. It is used when ind.ci is TRUE.


a logical indicating whether to compute 1-\alpha confidence intervals of each subject, default value is FALSE.


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
# 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]])

BrainCon documentation built on May 31, 2023, 6:36 p.m.