Description Usage Arguments Value Author(s) References Examples
This function fills in a matrix of clusters that contains NAs, by estimating the missing cluster labels based on the available ones or based on the other datasets. The predictive accuracy of this method can also be estimated via crossvalidation.
1 
clLabels 
N X M matrix containing cluster labels. Element (n,m) contains the cluster label for element data point n in cluster m. 
data 
List of M datasets to be used for the label imputation. 
computeAccuracy 
Boolean. If TRUE, for each missing element, the performance of the predictive model used to estimate the corresponding missing label is computer. Default is FALSE. 
verbose 
Boolean. If TRUE, for each NA, the size of the matrix used to estimate its values is printed to screen. Default is FALSE. 
The output is a list containing:
fullClLabels 
the same matrix of clusters as the input matrix

nRows 
matrix where the item in position (i,j) indicates the
number of observations used in the predictive model used to estimate the
corresponding missing label in the 
nColumns 
matrix where the item in position (i,j) indicates the
number of covariates used in the predictive model used to
estimate the corresponding missing label in the 
accuracy 
a matrix where each element
corresponds to the predictive accuracy of the predictive model used to
estimate the corresponding label in the cluster label matrix. This is only
returned if the argument 
accuracy_random 
This is computed in the same way as 
Alessandra Cabassi alessandra.cabassi@mrcbsu.cam.ac.uk
The Cancer Genome Atlas, 2012. Comprehensive molecular portraits of human breast tumours. Nature, 487(7407), pp.61–70.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20  # Load data
data < list()
data[[1]] < as.matrix(read.csv(system.file("extdata", "dataset1.csv",
package = "coca"), row.names = 1))
data[[2]] < as.matrix(read.csv(system.file("extdata", "dataset2.csv",
package = "coca"), row.names = 1))
data[[3]] < as.matrix(read.csv(system.file("extdata", "dataset3.csv",
package = "coca"), row.names = 1))
# Build matrix of clusters
outputBuildMOC < buildMOC(data, M = 3, K = 6, distances = "cor")
# Extract matrix of clusters
clLabels < outputBuildMOC$clLabels
# Impute missing values using full datasets
outputFillMOC < fillMOC(clLabels, data)
# Extract full matrix of cluster labels
clLabels2 < outputFillMOC$fullClLabels

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