Performs splines clustering using K-means

Performs splines clustering using K-means

# S4 method for Moanin
splines_kmeans(
  object,
  n_clusters = 10,
  init = "kmeans++",
  n_init = 10,
  max_iter = 300,
  random_seed = .Random.seed[1],
  fit_splines = TRUE,
  rescale = TRUE
)

Arguments

object	An object of class Moanin, an object containing all related information for time course data and the splines model that will be used (if applicable). See create_moanin_model for more details.
n_clusters	int optional, default: 10
init	["kmeans++", "random", "optimal_init"]
n_init	int, optional, default: 10 Number of initialization to perform.
max_iter	int, optional, default: 300 Maximum number of iteration to perform
random_seed	int, optional, default: NULL. Passed to argument seed in KMeans_rcpp. If NULL (default), set to .Random.seed[1].
fit_splines	boolean, optional, default: TRUE Whether to fit splines or not.
rescale	boolean, optional, default: TRUE Whether to rescale the data or not.

Value

A list in the format returned by KMeans_rcpp, with the following elements added or changed:

• centroids The centroids are rescaled so that they range from 0-1

• fit_splines Logical, the value of fit_splines given to the function

• rescale The value of rescale given to the function

Details

If Moanin object's slot has log_transform=TRUE, then the data will be transformed by the function log(x+1) before applying splines and clustering.

Examples

data(exampleData)
# Use the default options
moanin <- create_moanin_model(data=testData, meta=testMeta)
out <- splines_kmeans( moanin,n_clusters=5)
table(out$clusters)
#> 
#>   1   2   3   4   5 
#>  74 108 108 107 103