Estimate the goodnessoffit between tree models and data.
treefit( target, name = NULL, perturbations = NULL, normalize = NULL, reduce_dimension = NULL, build_tree = NULL, max_p = 20, n_perturbations = 20 )
target  The target data to be estimated. It must be one of them:


name  The name of 
perturbations  How to perturbate the target data. If this is You can specify used perturbation methods as 
normalize  How to normalize counts data. If this is You can specify a function that normalizes counts data. 
reduce_dimension  How to reduce dimension of expression data. If this is You can specify a function that reduces dimension of expression data. 
build_tree  How to build a tree of expression data. If this is You can specify a function that builds tree of expression data. 
max_p  How many low dimension Laplacian eigenvectors are used. The default is 20. 
n_perturbations  How many times to perturb. The default is 20. 
An estimated result as a treefit
object. It has the
following attributes:
max_cca_distance
: The result of max canonical correlation
analysis distance as data.frame
.
rms_cca_distance
: The result of root mean square canonical
correlation analysis distance as data.frame
.
n_principal_paths_candidates
: The candidates of the number of
principal paths.
data.frame
of max_cca_distance
and rms_cca_distance
has the
same structure. They have the following columns:
p
: Dimensionality of the feature space of tree structures.
mean
: The mean of the target distance values.
standard_deviation
: The standard deviation of the target
distance values.
# Generate a star tree data that have normalized expression values # not count data. star < treefit::generate_2d_n_arms_star_data(300, 3, 0.1) # Estimate treelikeness of the tree data. fit < treefit::treefit(list(expression=star))