consensus {maanova} | R Documentation

## Build consensus tree out of bootstrap cluster result

### Description

This is the function to build the consensus tree from the bootstrap clustering analysis. If the clustering algorithm is hierarchical clustering, the majority rule consensus tree will be built based on the given significance level. If the clustering algorithm is K-means, a consensus K-means group will be built.

### Usage

    consensus(macluster, level = 0.8, draw=TRUE)

### Arguments

`macluster` | An object of class `macluster`, which is the output of `macluster`
`level` | The significance level for the consensus tree. This is a numeric number between 0.5 and 1.
`draw` | A logical value to indicate whether to draw the consensus tree on screen or not.

### Value

An object of class `consensus.hc` or `consensus.kmean` according to the clustering method.

### Author(s)

Hao Wu hao@jax.org

### See Also

`macluster`

### Examples

    # load in data
    data(paigen)
    # make data object with rep 2
    paigen <- createData(paigen.raw, 2)
    # make interactive model
    model.int.fix <- makeModel(data=paigen,
          formula=~Dye+Array+Strain+Diet+Strain:Diet)
    # fit ANOVA model
    anova.int <- fitmaanova(paigen, model.int.fix)
    # test interaction effect
    ## Not run: test.int.fix <- matest(paigen, model.int.fix, term="Strain:Diet", n.perm=100)
    # pick significant genes - pick the genes selected by Fs test
    idx <- volcano(test.int.fix)$idx.Fs

    # do k-means cluster on genes
    gene.cluster <- macluster(anova.int, "Strain:Diet", idx, "gene",
        "kmean", kmean.ngroups=5)
    # get the consensus group
    consensus(gene.cluster, 0.5)

    # HC cluster on samples
    sample.cluster <- macluster(anova.int, "Strain:Diet", idx, "sample","hc")
    # get the consensus group
    consensus(sample.cluster, 0.5)## End(Not run)

[Package maanova version 0.98.8 Index]