checkDataGenerationInputs {batchmix} | R Documentation |
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
)