Description Usage Arguments Details Value Examples
View source: R/consensus_evaluate.R
Evaluates algorithms on internal/external validation indices. Poor performing algorithms can be trimmed from the ensemble. The remaining algorithms can be given weights before use in consensus functions.
1 2 
data 
data matrix with rows as samples and columns as variables 
... 
any number of objects outputted from 
cons.cl 
matrix of cluster assignments from consensus functions such as

ref.cl 
reference class 
k.method 
determines the method to choose k when no reference class is
given. When 
plot 
logical; if 
trim 
logical; if 
reweigh 
logical; if 
n 
an integer specifying the top 
This function always returns internal indices. If ref.cl
is not NULL
,
external indices are additionally shown. Relevant graphical displays are also
outputted. Algorithms are ranked across internal indices using Rank
Aggregation. Only the top n
algorithms are kept, the rest are trimmed.
consensus_evaluate
returns a list with the following elements
k
: if ref.cl
is not NULL
, this is the number of distinct classes
in the reference; otherwise the chosen k
is determined by the one giving
the largest mean PAC across algorithms
pac
: a data frame showing the PAC for each combination of algorithm
and cluster size
ii
: a list of data frames for all k showing internal evaluation
indices
ei
: a data frame showing external evaluation indices for k
trim.obj
: A list with 4 elements
alg.keep
: algorithms kept
alg.remove
: algorithms removed
rank.matrix
: a matrix of ranked algorithms for every internal
evaluation index
top.list
: final order of ranked algorithms
E.new
: A new version of a consensus_cluster
data object
1 2 3 4 5 6 7 8 9 10 11 12  # Consensus clustering for multiple algorithms
set.seed(911)
x < matrix(rnorm(500), ncol = 10)
CC < consensus_cluster(x, nk = 3:4, reps = 10, algorithms = c("ap", "km"),
progress = FALSE)
# Evaluate algorithms on internal/external indices and trim algorithms:
# remove those ranking low on internal indices
set.seed(1)
ref.cl < sample(1:4, 50, replace = TRUE)
z < consensus_evaluate(x, CC, ref.cl = ref.cl, n = 1, trim = TRUE)
str(z, max.level = 2)

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