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Multinomial Generalized Linear Mixed Model (mnGLMM)

mnGLMM.Rd

Fits a multivariate generalized linear regression model for multinomially distributed response data. This function estimates driver-species relationships (B coefficients), observation-level dispersion, and species covariance structures via maximum likelihood or REML.

Usage

mnGLMM(
  Y,
  X = NULL,
  B.fixed = if (is.null(X)) matrix(c(0, rep(NA, ncol(Y) - 1)), nrow = 1, ncol = ncol(Y))
    else matrix(c(rep(0, (ncol(X) + 1) * ncol(Y)), rep(NA, (ncol(Y) - 1) * (ncol(X) +
    1))), nrow = ncol(X) + 1, ncol = ncol(Y)),
  B.start = if (is.null(X)) matrix(0, nrow = 1, ncol = ncol(Y)) else matrix(0, nrow =
    ncol(X) + 1, ncol = ncol(Y)),
  sigma.fixed = NA,
  sigma.start = 0.1,
  dispersion.fixed = 1,
  dispersion.start = 1,
  V.fixed = diag(ncol(Y)),
  V.start = diag(ncol(Y)),
  method = "bobyqa",
  optim.control = NULL,
  maxit.optim = 1e+05,
  REML = FALSE,
  compute.information.matrix = TRUE
)

Arguments

Y

A matrix of multinomially distributed count data (e.g., community count data).

X

A matrix of covariates (predictors), which may be of mixed type Covariates should be scaled when appropriate. Can be NULL.

B.fixed

A matrix indicating which B coefficients (driver-species relationships) to estimate. The number of columns must equal ncol(Y) (number of species), and the number of rows must equal ncol(X) + 1 (intercept + covariates).

B.start

A matrix of starting values for the B coefficients. Dimensions must match B.fixed.

sigma.fixed

Fixed value for the overall model variance. Use NA to estimate it from the model.

sigma.start

Starting value for estimating sigma.fixed (default is 0.1).

dispersion.fixed

Fixed dispersion parameter to account for over- or under-dispersion. A value of 1 corresponds to no extra dispersion (pure multinomial).

dispersion.start

Starting value for estimating dispersion.fixed.

V.fixed

A species-by-species covariance matrix representing environmental variation.

V.start

Starting values for V.fixed.

method

Optimization method. Acceptable values include "Nelder-Mead", "BFGS" (via optim), and "bobyqa" (via the minqa package).

optim.control

Optional list of control parameters passed to the optimizer. See the minqa package documentation for details.

maxit.optim

Maximum number of iterations for the optimizer (default is 1e+05). Increase if of optimizer needs more itterations.

REML

Logical. If TRUE, uses restricted maximum likelihood for variance estimation.

compute.information.matrix

Logical. If TRUE, computes the observed information matrix.

Value

An object of class "mnGLMM".