Description Usage Arguments Value Author(s) Examples
Convenience harness to run COMMUNAL on a range of data subsets, for a fixed set of parameters. In the data format, the columns are the items to be clustered. The rows are (optionally) first sorted by variance. The top x
rows are used for clustering by COMMUNAL
, for each x
in varRange
.
Output is used by plotRange3D
to generate 3D plot.
1 2 | clusterRange(dataMtx, ks, varRange, validation = "all", verbose = T, ...,
parallel = F, mc.cores = NULL, row.order = NULL)
|
dataMtx |
The data for input to |
ks |
The range of K to test with |
varRange |
Numeric vector of how many items of data matrix to cluster.
|
validation |
Validation measures to use in |
verbose |
Whether to be verbose. Very helpful in identifying points of failure or delay; but, the output is very verbose. |
... |
Arguments to pass down to |
parallel |
clusterRange performs the same COMMUNAL run for each data subset; parallel capabilities have been implemented using the parallel::mclapply(). This function DOES NOT RUN ON WINDOWS MACHINES (sorry). |
mc.cores |
optionally set the number of cores to use. Ignored if not parallel. |
row.order |
If left NULL, the data is reordered in descending order of variance. Otherwise, data is subsetted by row.order according to varRange. So, if varRange = c(100,500), then the first subset will be the rows of data at row.order[1:100], and the second subset will be at row.order[1:500]. |
all.results |
list of |
varRange |
the |
Albert Chen and Timothy E Sweeney
Maintainer: Albert Chen acc2015@stanford.edu
1 2 3 4 5 6 7 8 9 10 11 12 13 | ## Not run:
## To identify k, use clusterRange and plotRange3D to visualize validation measures
data(BRCA.100) # 533 tissues to cluster, with measurements of 100 genes each
varRange <- c(50, 75, 100)
clus.methods <- c("hierarchical", "kmeans")
validation <- c('wb.ratio', 'dunn', 'avg.silwidth')
range.results <- clusterRange(BRCA.100, varRange, ks=2:5, clus.methods=clus.methods,
validation=validation)
plot.data <- plotRange3D(range.results, ks=2:5, clus.methods, validation)
## Note: the BRCA.results dataset was generated by running clusterRange on
## a larger range than the one here (with a larger input dataset)
## End(Not run)
|
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