| nlhc {nlnet} | R Documentation |
Non-Linear Hierarchical Clustering
Description
The non-linear hierarchical clustering based on DCOL
Usage
nlhc(array, hamil.method = "nn", concorde.path = NA,
use.normal.approx = FALSE, normalization = "standardize", combine.linear = TRUE,
use.traditional.hclust = FALSE, method.traditional.hclust = "average")
Arguments
array |
the data matrix with no missing values |
hamil.method |
the method to find the hamiltonian path. |
concorde.path |
If using the Concorde TSP Solver, the local directory of the solver |
use.normal.approx |
whether to use the normal approximation for the null hypothesis. |
normalization |
the normalization method for the array. |
combine.linear |
whether linear association should be found by correlation to combine with nonlinear association found by DCOL. |
use.traditional.hclust |
whether traditional agglomerative clustering should be used. |
method.traditional.hclust |
the method to pass on to hclust() if traditional method is chosen. |
Details
Hamil.method: It is passed onto the function tsp of library TSP. To use linkern method, the user needs to install concord as instructed in TSP.
use.normal.approx: If TRUE, normal approximation is used for every feature, AND all covariances are assumed to be zero. If FALSE, generates permutation based null distribution - mean vector and a variance-covariance matrix.
normalization: There are three choices - "standardize" means removing the mean of each row and make the standard deviation one; "normal_score" means normal score transformation; "none" means do nothing. In that case we still assume some normalization has been done by the user such that each row has approximately mean 0 and sd 1.
combine.linear: The two pieces of information is combined at the start to initiate the distance matrix.
Value
Returns a hclust object same as the output of hclust(). Reference: help(hclust)
merge |
an n-1 by 2 matrix. Row i of merge describes the merging of clusters at step i of the clustering. If an element j in the row is negative, then observation -j was merged at this stage. If j is positive then the merge was with the cluster formed at the (earlier) stage j of the algorithm. |
height |
a set of n-1 real values, the value of the criterion associated with the clusterig method for the particular agglomeration |
order |
a vector giving the permutation of the original observations suitable for plotting, in the sense that a cluster plot using this ordering and matrix merge will not have crossings of the branches. |
labels |
labels for each of the objects being clustered |
call |
the call which produced the result |
dist.method |
the distance that has been used to create d |
height.0 |
original calculation of merging height |
Author(s)
Tianwei Yu <tianwei.yu@emory.edu>
References
http://www.ncbi.nlm.nih.gov/pubmed/24334400
See Also
Examples
## generating the data matrix & hiden clusters as a sample
input<-data.gen(n.genes=40, n.grps=4)
## now input includes data matrix and hiden clusters, so get the matrix as input.
input<-input$data
nlhc.data<-nlhc(input)
plot(nlhc.data)
##get the merge from the input.
merge<-nlhc.data$merge