ggrasp (version 1.2)

ggrasp.recluster: ggrasp.recluster

Description

recalculates a threshold and the resulting cluster using the previously defined Gaussian Mixture Model and provided threshold-determining factors. Requires the ggrasp.cluster to already have run

Usage

ggrasp.recluster(x, z.limit = 1, min.lambda = 0.005, left.dist = 1)

Arguments

x

the GGRaSP object for which the ranks will be added.

z.limit

All Gaussian distributions with means within this number of standard deviations will be reduced to only the larger distribution. Defaults to 1. Set to 0 to keep all non-overlapping distributions.

min.lambda

All Gaussian distributions with lambda value (proportion of the total distribution) below this value are removed before calculating the threshold. Default is 0.005. Set to 0 to keep all.

left.dist

Number giving the number Gaussian distribution model immediately to the left of the threshold used. 1 is the default. Only value between 1 and k-1 where k is the total number of number of Gaussian distributions.

Value

A GGRaSP object with the recalculated thresholds and the medoids using a previously generated GMM

Examples

# NOT RUN {
#The following data is from Chavda et al 2016 which phylotyped Enterobacter genomes
# Our example uses the data underpinning the tree shown in Figure 2

#Loading the tree 
library(ggrasp);
tree.file <- system.file("extdata", "Enter.kSNP.tree", package="ggrasp")
Enter.tree <- ggrasp.load(tree.file, file.format = "tree");

#Clustering the tree using a threshold estimated by Gaussian Mixture Models (GMMs)
# }
# NOT RUN {
Enter.tree.cluster <- ggrasp.cluster(Enter.tree)
# }
# NOT RUN {

#Use print to get a list of the medoids selected
# }
# NOT RUN {
print(Enter.tree.cluster)
# }
# NOT RUN {
#Re-clustering the tree using a threshold estimated by the GMMs but without the distribution
#cleaning (i.e. removing the overlapping and low count distributions)
# }
# NOT RUN {
Enter.tree.reclust <- ggrasp.recluster(Enter.tree.cluster, z.limit=0, min.lambda = 0)
# }
# NOT RUN {
# }