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.
generateBatchDataLogPoisson(
N,
P,
group_rates,
batch_rates,
group_weights,
batch_weights,
frac_known = 0.2,
permute_variables = TRUE,
scale_data = FALSE
)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.
The number of items (rows) to generate.
The number of columns in the generated dataset.
A vector of the group rates for the classes within a column.
A vector of the batch rates for the classes within a column. This is used to create a variable which has the sum of the appropriate batch and class rate, it might be better interpreted as the batch effect on the observed rate.
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.
A vector of the expected proportion of N in each batch.
The number of items with known labels.
Logical indicating if group and batch means and standard deviations should be permuted in each column or not (defaults to ``TRUE``).
Logical indicating if data should be mean centred and standardised (defaults to ``FALSE``).