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Generate a random AnansiWeb or MultiFactor

Source: R/randomAnansi.R
randomAnansi.Rd

Randomly generate a valid AnansiWeb or MultiFactor object.

Usage

randomWeb(
  n_samples = 10,
  n_reps = 1L,
  n_features_x = 8,
  n_features_y = 12,
  sparseness = 0.5,
  tableY = NULL,
  tableX = NULL,
  dictionary = NULL
)

randomMultiFactor(n_types = 6, n_features = 100, sparseness = 0.5)

krebsDemoWeb(n_samples = 100, n_reps = 4L)

Arguments

n_samples, n_reps

Numeric scalar Number of samples and repeated measures of those samples to be generated. Ignored if tableY and tableX are provided. (defaults: 10 samples without repeats)

n_features_y, n_features_x

Numeric scalar Number of features to be generated. Ignored if tableY and tableX are provided.

sparseness

Numeric scalar, proportion: How rare are connections

tableY, tableX

A table containing features of interest. Rows should be samples and columns should be features. Y and X refer to the position of the features in a formula: Y ~ X.

dictionary

A binary adjacency matrix of class Matrix, or coercible to Matrix.

n_types

Numeric scalar, number of types of features to generate

n_features

Numeric scalar, number of features per type

Value

a randomly generated object of the specified class.

See also

AnansiWeb(), MultiFactor()

Examples

# Make a random AnansiWeb object
randomWeb()
#> AnansiWeb S4 object with 10 observations:
#>     tableY: y (12 features)
#>     tableX: x (8 features)
#> Accessors: tableX(), tableY(), dictionary(), metadata().
krebsDemoWeb()
#> AnansiWeb S4 object with 400 observations:
#>     tableY: Enzyme (8 features)
#>     tableX: Metabolite (9 features)
#> Accessors: tableX(), tableY(), dictionary(), metadata().
randomMultiFactor()
#> A list of class MultiFactor,
#>     6 feature types across 5 edge lists.
#> 
#>       a   b   c   d   e   f
#> a2b 100 100   .   .   .   .
#> b2c   . 100 100   .   .   .
#> c2d   .   . 100 100   .   .
#> d2e   .   .   . 100 100   .
#> e2f   .   .   .   . 100 100
#> 
#> Values represent unique feature names in that edge list.
#> 
#> Levels:
#> 
#> a : 100 Levels: a_001 a_002 ... a_100 
#> b : 100 Levels: b_001 b_002 ... b_100 
#> c : 100 Levels: c_001 c_002 ... c_100 
#> d : 100 Levels: d_001 d_002 ... d_100 
#> e : 100 Levels: e_001 e_002 ... e_100 
#> f : 100 Levels: f_001 f_002 ... f_100