View source: R/population2sample.test.R
population2sample.test | R Documentation |
Identify differences of partial correlations between two populations
in two groups of time series data by
controlling the rate of the false discovery proportion (FDP) exceeding c0
at \alpha
, considering time dependence.
Input two popEst
class objects returned by population.est
(the number of individuals in two groups can be different).
population2sample.test(
popEst1,
popEst2,
alpha = 0.05,
c0 = 0.1,
targetSet = NULL,
MBT = 5000,
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 |
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 coefficients between
the j-th variable and the k-th variable in two populations
are identified to be 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.
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.
## 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(pc1, pc2)
# conducting hypothesis test and returning simplified results
Res_s = population2sample.test(pc1, pc2, simplify = TRUE)
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