Description Usage Arguments Value Examples
Estimate the goodness-of-fit between tree models and data.
1 2 3 4 5 6 7 8 9 10 |
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.
1 2 3 4 5 6 7 8 | ## Not run:
# 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 tree-likeness of the tree data.
fit <- treefit::treefit(list(expression=star))
## End(Not run)
|
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