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Simulate a Matrix with Metadata

Source: R/sim_mat.R
sim_mat.Rd

sim_mat() generates random normal data with optional compound-symmetric column correlation. Values can optionally be scaled to the interval [0, 1] column-wise. The function also creates feature metadata for columns and sample metadata for rows, and can inject NA values into a specified proportion of matrix cells across a specified proportion of columns.

Usage

sim_mat(
  n = 100,
  p = 100,
  rho = 0.5,
  n_col_groups = 2,
  n_row_groups = 1,
  perc_total_na = 0.1,
  perc_col_na = 0.5,
  beta = TRUE
)

Arguments

n

Integer. Number of rows, interpreted as samples. Defaults to 100.

p

Integer. Number of columns, interpreted as features. Defaults to 100.

rho

Numeric. Compound-symmetric column correlation before optional scaling. Defaults to 0.5.

n_col_groups

Integer. Number of groups to assign to features. Defaults to 2.

n_row_groups

Integer. Number of groups to assign to samples. Defaults to 1.

perc_total_na

Numeric scalar between 0 and 1. Proportion of all matrix cells to set to NA. Defaults to 0.1.

perc_col_na

Numeric scalar between 0 and 1. Proportion of columns across which injected NA values are spread. Defaults to 0.5.

beta

Logical. If TRUE, scale values to the interval [0, 1] column-wise.

Value

An object of class slideimp_sim. This is a list containing:

  • input: a numeric matrix of dimension \(n \times p\) containing the simulated values and injected missing values.

  • col_group: a data frame with \(p\) rows mapping each feature to a group.

  • row_group: a data frame with \(n\) rows mapping each sample to a group.

Details

Generate a numeric matrix with optional row and column metadata and added missing values.

Examples

set.seed(123)
sim_data <- sim_mat(n = 50, p = 10, rho = 0.5)
sim_data
#> $col_group (2 column groups)
#>    feature  group
#> 1 feature1 group1
#> 2 feature2 group1
#> 3 feature3 group1
#> 4 feature4 group1
#> 5 feature5 group2
#> 6 feature6 group2
#> # Showing 6 of 10 rows
#> 
#> $row_group (1 row groups)
#>    sample  group
#> 1 sample1 group1
#> 2 sample2 group1
#> 3 sample3 group1
#> 4 sample4 group1
#> 5 sample5 group1
#> 6 sample6 group1
#> # Showing 6 of 50 rows
#> 
#> $input (50 x 10)
#>          feature1  feature2  feature3  feature4  feature5  feature6
#> sample1 0.3855027 0.2859279        NA 0.7034804 0.3949006 0.3390209
#> sample2 0.3939132 0.5091468 0.5414625 0.6130915 0.4153076 0.3899470
#> sample3 0.7020675 0.7302534 0.7618661 0.6474409 0.6996583 0.6687105
#> sample4 0.6887438 0.4568955 0.3007332 0.5369357 0.5503469 0.3900968
#> sample5 0.4220865 0.3630904 0.4552229        NA 0.7723463 0.5073267
#> sample6 1.0000000 0.7918423 0.6874920 0.6385426 0.7579935 0.9167009
#> # Showing 6 of 50 rows and 6 of 10 columns