| get_network {cape} | R Documentation | 
Convert the final results to an adjacency matrix.
Description
This function converts the significant cape 
interactions to an adjacency matrix, which 
is then used by plot_network
Usage
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
)
Arguments
| 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. | 
Value
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.