# Run a BayesComm model `BC` is the main function for running BayesComm models. It is a wrapper function for `BCfit`; it checks inputs, sets up the model types and specifies a number of default `BCfit` settings. ```r BC(Y, X = NULL, model = "null", covlist = NULL, condition = NULL, its = 100, ...) ``` ## Arguments - `Y`: matrix of species presence/absence data - `X`: matrix of environmental covariates - `model`: type of model to run - `covlist`: optional list of which covariates to assign to each species - `condition`: matrix of conditioning variables - `its`: number of iterations for sampling phase - ``...``: further arguments to pass to `BCfit` ## Details `Y` must be a matrix with records as rows and species as columns and `X` a matrix with records as rows and covariates as columns. `model` must be one of: `"null"` (intercept only), `"environment"` (intercept & covariates), `"community"` (intercept & community matrix) or `"full"` (intercept, covariates & community matrix). `covlist` must have the same length as the number of species with, each element a vector of column indices for `X`. `covlist` defaults to `NULL`, which includes all covariates for all species. For more details of arguments for model fitting see `BCfit`. `condition` is an optional matrix of conditioning variables. These are fitted in the same way as `X` but are not removed in null and community models. ## Returns An object of class `bayescomm` containing the model call and parameter chains which can be viewed and manipulated using `window`, `plot`, `print` and `summary`. ## See Also `BCfit` ## Examples ```r # create fake data n <- 100 nsp <- 4 k <- 3 X <- matrix(c(rep(1, n), rnorm(n * k)), n) # covariate matrix W <- matrix(rnorm(nsp * nsp), nsp) W <- W %*% t(W) / 2 # true covariance matrix B <- matrix(rnorm(nsp * (k + 1), 0, 3), nsp) # true covariates mu <- apply(B, 1, function(b, x) x %*% b, X) # true mean e <- matrix(rnorm(n * nsp), n) %*% chol(W) # true e z <- mu + e # true z Y <- ifelse(z > 0, 1, 0) # true presence/absence # run BC (after removing intercept column from design matrix) m1 <- BC(Y, X[, -1], model = "full", its = 100) ```

BC function