This function is DEPRECATED. Please use sccomp_simulate instead.
Usage
simulate_data(
.data,
.estimate_object,
formula_composition,
formula_variability = NULL,
.sample = NULL,
.cell_group = NULL,
.coefficients = NULL,
variability_multiplier = 1,
number_of_draws = 1,
mcmc_seed = sample_seed(),
cores = detectCores(),
sig_figs = 9,
cache_stan_model = sccomp_stan_models_cache_dir
)Arguments
- .data
A tibble including a cell_group name column | sample name column | read counts column | factor columns | Pvalue column | a significance column
- .estimate_object
The result of sccomp_estimate execution. This is used for sampling from real-data properties.
- formula_composition
A formula. The formula describing the model for differential abundance, for example ~treatment
- formula_variability
A formula. The formula describing the model for differential variability, for example ~treatment
- .sample
A column name as symbol. The sample identifier
- .cell_group
A column name as symbol. The cell_group identifier
- .coefficients
The column names for coefficients, for example, c(b_0, b_1). Use the
coefficientsargument ofsccomp_simulate()instead.- variability_multiplier
A real scalar. This can be used for artificially increasing the variability of the simulation for benchmarking purposes.
- number_of_draws
An integer. How may copies of the data you want to draw from the model joint posterior distribution.
- mcmc_seed
An integer. Used for Markov-chain Monte Carlo reproducibility. By default a random number is sampled from 1 to 999999. This itself can be controlled by set.seed()
- cores
Integer, the number of cores to be used for parallel calculations.
- sig_figs
Number of significant figures to use for Stan model output. Default is 9.
- cache_stan_model
A character string specifying the cache directory for compiled Stan models. The sccomp version will be automatically appended to ensure version isolation. Default is
sccomp_stan_models_cache_dirwhich points to~/.sccomp_models.
Value
See sccomp_simulate.