Generate random networks: as described in the “The Raw Matrix (link wise)”
introduction, it is necessary to determine whether a Graph-Component with a certain
size as a result of the correlational analysis is non-random. Thus, amount and size of
Graph-Components of random networks (random association-matrices) need to be
compared against the true Graph-Components. GraphVar offers two options to create
subject specific random networks (see below). Subsequently, the subject specific
variable selected for correlation (e.g. age) undergoes link wise correlational analyses on
the original and the random networks. Only significant correlations of the user defined
variable (e.g. age) with a network link are considered for Graph-Components
(please refer to the “Network Inspector” section for determining the significance value of links
considered for Graph-Components
and for more info about p-values for true Graph-Components). To actually test against the random networks you have to select the
“Test against random networks (correlation)” checkbox. When selecting this option
GraphVar simultaneously performs non-parametric testing of the correlation strenght,
which gives you the option to choose between parametric and non-parametric p-values
for inspection of single links. However, significant links in the Graph-Components are
determined by the parametric p-values as it would be circular to create p-values from
random data and then use the same random data again for Graph-Components
(for more info refer to the “Test against random networks (Correlation)” option.