get_network | R Documentation |
This function converts the significant cape
interactions to an adjacency matrix, which
is then used by plot_network
get_network( data_obj, geno_obj, p_or_q = 0.05, min_std_effect = 0, standardize = FALSE, collapse_linked_markers = TRUE, threshold_power = 1, verbose = FALSE, plot_linkage_blocks = FALSE )
data_obj |
a |
geno_obj |
a genotype object |
p_or_q |
A threshold indicating the maximum adjusted p value considered significant. If an fdr method has been used to correct for multiple testing, this value specifies the maximum q value considered significant. |
min_std_effect |
This numerical value offers an additional filtering method. If specified, only interactions with standardized effect sizes greater then the min_std_effect will be returned. |
standardize |
A logical value indicating whether the values returned in the adjacency matrix should be effect sizes (FALSE) or standardized effect sizes (TRUE). Defaults to FALSE. |
collapse_linked_markers |
A logical value. If TRUE markers are combined into linkage blocks based on correlation. If FALSE, each marker is treated as an independent observation. |
threshold_power |
A numerical value indicating the power to which to
raise the marker correlation matrix. This parameter is used in
|
verbose |
A logical value indicating whether to print algorithm progress to standard out. |
plot_linkage_blocks |
A logical value indicating whether to plot heatmaps showing the marker correlation structure and where the linkage block boundaries were drawn. |
This function returns the data object with an adjacency matrix defining
the final cape network based on the above parameters. The network is put into
the slot collapsed_net if collapse_linked_markers is set to TRUE, and full_net
if collapse_linked_markers is set to FALSE. run_cape
automatically
requests both networks be generated.
## Not run: data_obj <- get_network(data_obj, geno_obj) ## End(Not run)
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