knitr::opts_chunk$set( collapse = TRUE, comment = "#>" )

This vignette documents the implementation of NBR 0.1.3 for linear models.

We will analyze the `frontal3D`

dataset, which contains a 3D volume of 48 matrices, each matrix representing the functional connectivity between 28 nodes (in the frontal lobe). Phenotypic information (`frontal_phen`

) includes diagnostic GROUP (patient or control), sex, and age. We will test for a GROUP effect.

library(NBR) cmx <- NBR:::frontal3D # Load 3D array brain_labs <- NBR:::frontal_roi # Load node labels phen <- NBR:::frontal_phen # Load phenotypic info dim(cmx) # Show 3D array dimensions

We can plot the sample average matrix, with `lattice::levelplot`

.

library(lattice) avg_mx <- apply(cmx, 1:2, mean) # Set max-absolute value in order to set a color range centered in zero. flim <- max(abs(avg_mx)[is.finite(avg_mx)]) levelplot(avg_mx, main = "Average", ylab = "ROI", xlab = "ROI", at = seq(-flim, flim, length.out = 100))

As we can observe, this is a symmetric matrix with the pairwise connections of the 28 regions of interest (ROI) `brain_labs`

. The next step is to check the phenotypic information (stored in `phen`

) to perform statistic inferences edgewise. Before applying the NBR-LM, we check that the number of matrices (3rd dimension in the dataset) matches the number of observations in the `phen`

data.frame.

head(phen) nrow(phen) identical(nrow(phen), dim(cmx)[3])

The data.frame contains the individual information for diagnostic group, sex, and chronological age. So, we are all set to perform an NBR-LM. We are going to test the effect of diagnostic group with a minimal number of permutations to check that we have no errors.

set.seed(18900217) # Because R. Fisher is my hero before <- Sys.time() nbr_group <- nbr_lm_aov(net = cmx, nnodes = 28, idata = phen, mod = "~ Group", thrP = 0.01, nperm = 10) after <- Sys.time() show(after-before)

Although ten permutations is quite low to obtain a proper null distribution, we can see that they take several seconds to be performed. So we suggest to parallelizing to multiple CPU cores with `cores`

argument.

set.seed(18900217) library(parallel) before <- Sys.time() nbr_group <- nbr_lm_aov(net = cmx, nnodes = 28, idata = phen, mod = "~ Group", thrP = 0.01, nperm = 100, cores = detectCores()) after <- Sys.time() length(nbr_group)

NBR functions return a nested list of at least two lists. The first list encompasses all the individual significant edges, their corresponding component and statistical inference (p < 0.01, in this example). In this case all the significant edges belong to a single component.

# Plot significant component edge_mat <- array(0, dim(avg_mx)) edge_mat[nbr_group$components$Group[,2:3]] <- 1 levelplot(edge_mat, col.regions = rev(heat.colors(100)), main = "Component", ylab = "ROI", xlab = "ROI") show(nbr_group$fwe$Group)

As we can observe, significant edges are displayed in the upper triangle of the matrix, and the second list (`fwe`

) contains, for each term of the equation, the probability of the observed values to occur by chance, based on the null distribution.

**Any scripts or data that you put into this service are public.**

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.