Description Usage Arguments Details Value Examples
This function implements the group percentage difference test on a network statistic. This test assesses if the percent change in the network statistic due to the network manipulation is significantly different between groups. Percent change is calculated as the difference between the target and original statistic divided by the original statistic.
1 2 | group_perc_diff_test(netSampleStatSet, grouping.variable,
p.adjust = "BH", non.parametric = F)
|
netSampleStatSet |
Input |
grouping.variable |
character name of sample level grouping variable |
p.adjust |
character string for requested multiple comparisons adjustment. Defaults to Benjamani-Hochberg |
non.parametric |
Logical. if true, test is performed using Wilcox test. If false, t-test. Defaults to false. |
If the sample has 2 groups, this test is performed using a t-test or Wilcox test. If the sample has 3 or more groups, the test is performed using a 1-way ANOVA, or Kruskal-Wallis test. Differences are tested at each network manipulation.
A data frame containing original and adjusted p.values.
1 2 3 4 5 6 | data(GroupA)
GroupA_Net = as_NetSample(GroupA, 1:20, node.variables = list(community = c(rep(1, 10), rep(2,10))),
sample.variables = list(group = c(rep(1, 10), rep(2,10))))
Jackknife_GroupA_Net = net_apply(GroupA_Net, node_jackknife)
GlobEff_GroupA_Net = net_stat_apply(Jackknife_GroupA_Net, global_efficiency)
group_diff_test(GlobEff_GroupA_Net, grouping.variable = "group")
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.