individual.test {BrainCon} | R Documentation |
Identify nonzero individual-level partial correlations in time series data
by controlling the rate of the false discovery proportion (FDP) exceeding c0
at \alpha
, considering time dependence.
Input an indEst
class object returned by individual.est
or population.est
.
individual.test(
indEst,
alpha = 0.05,
c0 = 0.1,
targetSet = NULL,
MBT = 3000,
simplify = !is.null(targetSet)
)
indEst |
An |
alpha |
significance level, default value is |
c0 |
threshold of the exceedance rate of FDP,
default value is |
targetSet |
a two-column matrix. Each row contains two index corresponding to a pair of variables of interest.
If |
MBT |
times of multiplier bootstrap, default value is |
simplify |
a logical indicating whether results should be simplified if possible. |
If simplify
is FALSE
, a p*p
matrix with values 0 or 1 is returned.
If the j-th row and k-th column of the matrix is 1,
then the partial correlation coefficient between
the j-th variable and the k-th variable is identified to be nonzero.
And if simplify
is TRUE
, a two-column matrix is returned,
indicating the row index and the column index of recovered nonzero partial correlations.
We only retain the results which the row index is less than the column index.
Those with larger test statistics are sorted first.
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.
population.est
for making inferences on one individual in the population.
## Quick example for the individual-level inference
data(indsim)
# estimating partial correlation coefficients by scaled lasso
pc = individual.est(indsim)
# conducting hypothesis test
Res = individual.test(pc)