View source: R/individual.est.R
individual.est | R Documentation |
Estimate individual-level partial correlation coefficients in time series data
with 1-α confidence intervals.
Note that these are confidence intervals for single parameters, not simultaneous confidence intervals.
individual.est( X, lambda = NULL, type = c("slasso", "lasso"), alpha = 0.05, ci = TRUE )
X |
time series data of an individual which is a n*p numeric matrix, where n is the number of periods of time and p is the number of variables. |
lambda |
a penalty parameter of order √{\log(p)/n}.
If |
type |
a character string representing the method of estimation. |
alpha |
significance level, default value is |
ci |
a logical indicating whether to compute 1-α confidence interval, default value is |
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 asymptotic expansion of estimates, which will be used for multiple tests.
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.
Sun T. and Zhang C. (2012). Scaled Sparse Linear Regression, Biometrika, 99, 879–898.
Liu W. (2013). Gaussian Graphical Model Estimation With False Discovery Rate Control, The Annals of Statistics, 41, 2948–2978.
Ren Z., Sun T., Zhang C. and Zhou H. (2015). Asymptotic Normality and Optimalities in Estimation of Large Gaussian Graphical Models, The Annals of Statistics, 43, 991–1026.
population.est
.
## Quick example for the individual-level estimates data(indsim) # estimating partial correlation coefficients by scaled lasso pc = individual.est(indsim)
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