nlhc: Non-Linear Hierarchical Clustering
In nlnet: Nonlinear Network, Clustering, and Variable Selection Based on DCOL

Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/nlhc.R

The non-linear hierarchical clustering based on DCOL

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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")

`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.

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.

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

Tianwei Yu <tianwei.yu@emory.edu>

http://www.ncbi.nlm.nih.gov/pubmed/24334400

data.gen

 ## 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