Package index • MegaLMM

All functions
`Image()`	Modification to `image()`.
`K`	Genomic Relationship matrix of 502 lines
`MegaLMM_control()`	Set MegaLMM run parameters
`MegaLMM_priors()`	Set MegaLMM priors
`SingleSite_regression_sampler_parallel()`	Draws samples from all “fixed" coefficients (fixed and random) of a set of parallel linear regression models, conditional on the variance components.
`bs_binomial_model()`	Sample Eta given B-splines individual-level model with binomial observations
`bs_diff()`	B spline basis with option of centering and differencing coefficients
`cis_eQTL_model()`	Sample cis_eQTL coefficients
`clear_Posterior()`	Resets Posterior samples
`combined_model()`	Sample Eta given a list of different observation_models
`create_data_matrices()`	Create data matrices for MegaLMM from a "tall" data.frame
`estimate_memory_initialization_MegaLMM()`	Estimate the memory requirment for a fully initialized MegaLMM model
`estimate_memory_posterior()`	Estimates the memory required to store a set of posterior samples collected by `sample_MegaLMM`
`find_candidate_states()`	Finds the set of variance component proportions within a specified distance from a starting proportion
`get_posterior_FUN()`	Calculates the posterior mean of a function of parameters
`get_posterior_HPDinterval()`	Calculates Highest Posteriod Density intervals of a function of parameters
`get_posterior_mean()`	Calculates posterior mean of a function of parameters
`%**%`	Multiply matrices
`initialize_MegaLMM()`	Initialized Gibbs sampler for MegaLMM model
`initialize_variables_MegaLMM()`	Initialize MegaLMM variables
`load_posterior_param()`	load the posterior samples of a single parameter from all saved chunks
`load_posterior_param_old()`	load the posterior samples of a single parameter from all saved chunks
`matrix_multiply_toDense()`	Multiplies two matrices (sparse or dense by dense), returns the product as a dense matrix
`missing_data_model()`	Sample missing data
`plot(<MegaLMM_state>)`	Make plots of current MegaLMM state
`plot_diagnostics()`	Plots diagnostic plots for the fit of a MegaLMM model
`print.MegaLMM_state()`	Print statistics on current MegaLMM state
`regression_model()`	Sample Eta given regression-splines individual-level model
`regression_sampler_parallel()`	Draws samples from all “fixed" coefficients (fixed and random) of a set of parallel linear regression models, conditional on the variance components.
`reload_Posterior()`	Re-loads a full Posterior list with all parameters
`reorder_factors()`	Re-orders factors in decreasing order of magnitude
`rstdnorm_mat()`	Draws a matrix of standard normal variables
`sample_MegaLMM()`	Run MegaLMM Gibbs sampler
`save_posterior_chunk()`	Saves a chunk of posterior samples
`save_posterior_sample()`	Saves current state in Posterior
`set_priors_MegaLMM()`	Set priors for MegaLMM model.
`setup_model_MegaLMM()`	Set up a MegaLMM model
`summary.MegaLMM_state()`	Print more detailed statistics on current MegaLMM state
`toDense()`	Converts dense Matrix to "matrix" faster!
`traceplot_array()`	Make a trace plot of a set of related parameters from MegaLMM
`voom_model()`	Sample Eta given a voom observation model
`yield_data`	Yield BLUPs for 502 lines across 19 trials

Reference

All functions