Create parameters list for eNODAL_obj object.

Create parameters list for eNODAL_obj object.

createParams(
  gam_k = 29,
  gam = TRUE,
  test_method = "F",
  h_adj = TRUE,
  adj_method = "BH",
  sig_level = c(LC = 0.01, Linear = 0.01, Sig = 0.01, Interaction = 0.05, Meta1 = 0.05,
    Meta2 = 0.05),
  adapt = FALSE,
  test_func = "gam",
  sig_test = "LC-test",
  save_dist = FALSE,
  q0 = 0,
  knn.k = 40,
  dist_thresh = 0.4,
  eps = NULL,
  minPts = NULL,
  minClusterSize = NULL,
  kcluster = NULL,
  consensus_param = list(clmethod = "louvian", knn.k = 15, dist_thresh = 0.75),
  ...
)

Arguments

gam_k	Parameter k in GAM model. By default is 29.
gam	Whether use create GAM formula. By default is TRUE. If no continuous variable, set FALSE.
test_method	Testing method. Can be chosen from "F", "globaltest", "Tmax", "Chisq", "Cp". By default is "F"
h_adj	Indicator of whether use hierarchical adjustment. By default is TRUE
adj_method	Pvalue adjust method. See p.adjust. By default is "BH".
sig_level	Significance level of different test. By default is set below: LC = 0.01, Linear = 0.01, Interaction = 0.05, Meta1 = 0.05, Meta2 = 0.05.
adapt	Indicator of whether to use adaptive way to decide testing type.
test_func	Testing function used. By default is "gam".
sig_test	Method for test between sig vs. non-sig. Can be choosen from "LC-test", "lm" or "gam.
save_dist	Save distance result. By default is FALSE.
q0	Parameter q0 in apcluster. Byt default is 0. If it is NULL, will not use apcluster.
knn.k	Number of k-nearest neighbour to create knn graph in Louvian algorithm. By default is 40, If it is NULL, will not use Louvian.
dist_thresh	Threshold of distance when creating knn graph in Louvian algorithm. By default is 0.4, If it is NULL, will not use Louvian.
eps	Parameter eps in dbscan. If it is NULL, will not use dbscan.
minPts	Parameter minPts in dbscan. If it is NULL, will not use dbscan.
minClusterSize	Parameter minClusterSize in dbscan. If it is NULL, will not use dbscan.
kcluster	Number of clusters if using fixed number of clustering method.
consensus_param	Parameter list for consensus clustering. Format: list(clmethod = ..., params = ...), available method and parameters listed above. By default is Louvian, with knn.k = 15, dist_thresh = 0.3.
...	Other parameters can be passed to eNODAL_obj.

Value

A list contain parameters in eNODAL_obj object.