Predicting model averaged clmm models to new data

fake.predict.clmm

# Arguments:
#  - model = a clmm model
#  - modelAvg = a clmm model average (object of class averaging)
#  - newdata = a dataframe of new data to apply the model to
# Returns a dataframe of predicted probabilities for each row and response level
fake.predict.clmm <- function(modelAvg, newdata) {
  # Actual prediction function
  pred <- function(eta, theta, cat = 1:(length(theta) + 1), inv.link = plogis) {
    Theta <- c(-1000, theta, 1000)
    sapply(cat, function(j) inv.link(Theta[j + 1] - eta) - inv.link(Theta[j] - eta))
  }
  
  # Multiply each row by the coefficients
  #coefs <- c(model$beta, unlist(model$ST))##turn off if a model average is used
  beta <- modelAvg$coefficients[2,3:12]
  coefs <- c(beta, unlist(modelAvg$ST))
  
  xbetas <- sweep(newdata, MARGIN=2, coefs, `*`)
  
  # Make predictions
  Theta<-modelAvg$coefficients[2,1:2]
  #pred.mat <- data.frame(pred(eta=rowSums(xbetas), theta=model$Theta))
  pred.mat <- data.frame(pred(eta=rowSums(xbetas), theta=Theta))
  #colnames(pred.mat) <- levels(model$model[,1])
  a<-attr(modelAvg, "modelList")
  colnames(pred.mat) <- levels(a[[1]]$model[,1])
   
  pred.mat
}

Feb 6 2016: modified the original by adding functionality to use model averaged coefficients