batchmix (version 2.2.1)

generateBatchData: Generate batch data

Description

Generate data from K multivaraite normal or multivariate t distributions with additional noise from batches. Assumes independence across columns. In each column the parameters are randomly permuted for both the groups and batches.

Usage

generateBatchData(
  N,
  P,
  group_means,
  group_std_devs,
  batch_shift,
  batch_scale,
  group_weights,
  batch_weights,
  type = "MVN",
  group_dfs = NULL,
  frac_known = 0.2,
  permute_variables = TRUE,
  scale_data = FALSE
)

Value

A list of 5 objects; the data generated from the groups with and without batch effects, the label indicating the generating group, the batch label and the vector indicating training versus test.

Arguments

N

The number of items (rows) to generate.

P

The number of columns in the generated dataset.

group_means

A vector of the group means for a column.

group_std_devs

A vector of group standard deviations for a column.

batch_shift

A vector of batch means in a column.

batch_scale

A vector of batch standard deviations within a column.

group_weights

One of either a K x B matrix of the expected proportion of each batch in each group or a K-vector of the expected proportion of the entire dataset in each group.

batch_weights

A vector of the expected proportion of N in each batch.

type

A string indicating if data should be generated from multivariate normal ("MVN") or multivariate t ("MVT") densities (defaults to "MVN").

group_dfs

A K-vector of the group specific degrees of freedom.

frac_known

The number of items with known labels.

permute_variables

Logical indicating if group and batch means and standard deviations should be permuted in each column or not (defaults to ``TRUE``).

scale_data

Logical indicating if data should be mean centred and standardised (defaults to ``FALSE``).

Examples

N <- 500
P <- 2
K <- 2
B <- 5
mean_dist <- 4
batch_dist <- 0.3
group_means <- seq(1, K) * mean_dist
batch_shift <- rnorm(B, mean = batch_dist, sd = batch_dist)
std_dev <- rep(2, K)
batch_var <- rep(1.2, B)
group_weights <- rep(1 / K, K)
batch_weights <- rep(1 / B, B)
dfs <- c(4, 7)
my_data <- generateBatchData(
  N,
  P,
  group_means,
  std_dev,
  batch_shift,
  batch_var,
  group_weights,
  batch_weights,
  type = "MVT",
  group_dfs = dfs
)