brainGraph (version 1.0.0)

IndividualContributions: Approaches to estimate individual network contribution

Description

loo calculates the individual contribution to group network data for each subject in each group using a "leave-one-out" approach. The residuals of a single subject are excluded, and a correlation matrix is created. This is compared to the original correlation matrix using the Mantel test. aop calculates the individual contribution using an "add-one-patient" approach. The residuals of a single patient are added to those of a control group, and a correlation matrix is created.

Usage

loo(resids, corrs, level = c("global", "regional"))
aop(resids, index, corr.mat, level = c("global", "regional"))

Arguments

resids

Data table of model residuals

corrs

List of lists of correlation matrices (as output by corr.matrix).

level

Character string; the level at which you want to calculate contributions (either global or regional)

index

Integer; the row number (in resids) of the subject to be added

corr.mat

Numeric; correlation matrix of the control group

Value

A data.table with columns for

Study.ID

Subject identifier

Group

Group membership

IC

The value of the individual contribution

References

Saggar M., Hosseini S.M.H., Buno J.L., Quintin E., Raman M.M., Kesler S.R., Reiss A.L. (2015) Estimating individual contributions from group-based structural correlations networks. NeuroImage, 120:274-284. doi:10.1016/j.neuroimage.2015.07.006

Examples

## Not run: ------------------------------------
# IC <- loo(resids.all, corrs)
# RC <- loo(resids.all, corrs, level='regional')
## ---------------------------------------------
## Not run: ------------------------------------
# IC <- adply(which(resids.all[, Group == groups[2]]), .margins=1, function(x)
#             aop(resids.all, x, corrs[[1]][[1]]$R),
#             .parallel=T, .id=NULL)
## ---------------------------------------------