R/setcov.R

In mrds: Mark-Recapture Distance Sampling

#' Creates design matrix for covariates in detection function
#'
#' This function creates a design matrix for the g(0) or scale covariates using
#' the input model formula. It returns a list which contains 2 elements: 1)
#' dim: the dimension (number of columns) of the design matrix, and 2) cov: the
#' constructed design matrix. This function is relatively simple because it
#' uses the built-in function \code{\link{model.matrix}} which does the
#' majority of the work.  This function handles 2 exceptions "~.", the null
#' model with 0 columns and "~1" the intercept only model - a column of 1s.  If
#' a model other than the 2 exceptions is provided, it calls
#' \code{\link{model.matrix}} to construct the columns. If any of the columns of
#' the design matrix are all 0's the column is removed.  This occurs when there
#' is no data for a particular factor.
#'
#'
#' @param dmat data matrix
#' @param model model formula
#' @return a design matrix for the specified data and model
#' @author Jeff Laake
#' @keywords utility
setcov <- function(dmat, model){
  # Null Model
  if(model=="~."){
    n <- 0
    x <- NULL
  # Intercept Model
  }else if(model == "~1"){
    n <- 1
    x <- matrix(rep(1, nrow(dmat)), ncol=1)
    colnames(x) <- "(Intercept)"
  # Covariate Model
  }else{
    x <- model.matrix(as.formula(model), data = dmat)
  }
  return(x)
}