population2sample.test.MinPv {BrainCon} | R Documentation |
Identify differences of partial correlations between two populations
in two groups of time series data,
based on controlling the rate of the false discovery proportion (FDP) exceeding c0
at \alpha
. The method is based on the minimum of the p-values.
Input two popEst
class objects returned by population.est
(the number of individuals in two groups can be different).
population2sample.test.MinPv(
popEst1,
popEst2,
alpha = 0.05,
c0 = 0.1,
targetSet = NULL,
simplify = !is.null(targetSet)
)
popEst1 |
A |
popEst2 |
A |
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 |
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, and 1 means unequal.
And if simplify
is TRUE
, a two-column matrix is returned,
indicating the row index and the column index of recovered unequal partial correlations.
Those with lower p values are sorted first.
Genovese C., and Wasserman L. (2006). Exceedance Control of the False Discovery Proportion, Journal of the American Statistical Association, 101, 1408-1417
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 two-sample case inference
data(popsimA)
data(popsimB)
# estimating partial correlation coefficients by lasso (scaled lasso does the same)
pc1 = population.est(popsimA, type = 'l')
pc2 = population.est(popsimB, type = 'l')
# conducting hypothesis test
Res = population2sample.test.MinPv(pc1, pc2)