checkDataGenerationInputs {batchmix}

Check data generation inputs

Description

Checks that the inputs for the “generateBatchData“ function are correct. For internal use only.

Usage

checkDataGenerationInputs(
  N,
  P,
  group_means,
  group_std_devs,
  batch_shift,
  batch_scale,
  group_weights,
  batch_weights,
  type,
  group_dfs,
  frac_known,
  permute_variables,
  scale_data
)

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

A K x B matrix of the expected proportion of N in each group in each batch.

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.

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.

scale_data

Logical indicating if data should be mean centred and standardised.

Value

No return value, called for side effects.

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)
group_std_devs <- rep(2, K)
batch_scale <- rep(1.2, B)
group_weights <- rep(1 / K, K)
batch_weights <- rep(1 / B, B)
type <- "MVT"
group_dfs <- c(4, 7)
frac_known <- 0.3
permute_variables <- TRUE
scale_data <- FALSE

checkDataGenerationInputs(
  N,
  P,
  group_means,
  group_std_devs,
  batch_shift,
  batch_scale,
  group_weights,
  batch_weights,
  type,
  group_dfs,
  frac_known,
  permute_variables,
  scale_data
)