# bbm_data_generate This is to generate the simulation data based on Beta-bionomial mixture model ```r bbm_data_generate(S=3, G=50, K=3, prob=rep(1,times=3), alpha_band=c(2,6), beta_band=c(2,6), nb_mu=100,nb_size=0.2, plotf = FALSE, max_cor=0.5) ``` ## Arguments - `S`: Number of samples in the simulated data - `G`: Number of sites in the simulated data - `K`: Number of clusters that exist in the simulated data - `prob`: the cluster weight for each cluster - `alpha_band`: the region used to generate the parameter of beta distribution alpha - `beta_band`: the region used to generate the parameter of beta distribution beta - `nb_mu`: alternative parametrization via mean for Negative Binomial distribution - `nb_size`: target for number of successful trials, or dispersion parameter (the shape parameter of the gamma mixing distribution) for Negative binomial distrition. Must be strictly positive, need not be integer. - `plotf`: option for whether plot the generated data according to clusters or not - `max_cor`: The maximized correlation allowed for the simulated data, which used to guarantee the data is not highly correlated. ## Returns The function returns simulation data generated based on beta binomial mixture model ## Details The Dirichlet Process based beta-binomial mixture model clustering ## References Reference coming soon! ## Author(s) Lin Zhang, PhD ## Examples ```r set.seed(123455) S <- 4 G <- 100 K <- 3 nb_mu <- 100 nb_size <- 0.8 prob <- c(1,1,1) mat <- bbm_data_generate(S=S,G=G,K=K,prob=prob,alpha_band=c(2,6),beta_band=c(2,6), nb_mu=nb_mu,nb_size=nb_size, plotf = TRUE, max_cor=0.5) table(mat$gamma) pie(mat$gamma) id <- order(mat$gamma); c <- mat$gamma[id] mat_ratio <- (mat$k+1)/(mat$n+1); heatmap(mat_ratio[id,], Rowv = NA, Colv = NA, scale="none", RowSideColors=as.character(c), xlab = "4 samples", ylab="100 RNA methylation sites") ```

bbm_data_generate function