fake.predict.clmm

# Arguments:
#  - model = a clmm model
#  - 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(model, 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))
  xbetas <- sweep(newdata, MARGIN=2, coefs, `*`)
  
  # Make predictions
  pred.mat <- data.frame(pred(eta=rowSums(xbetas), theta=model$Theta))
  colnames(pred.mat) <- levels(model$model[,1])
  pred.mat
}

Does this do what it suggests it does..i.e. predict probabilitirs from a clmm model? I am abit confused by the 'fake' in the function name. I have posted question here [http://stackoverflow.com/questions/42150143/probability-predictions-with-model-averaged-cumulative-link-mixed-models-fitted] do you reckon you could revise your code to address this? thanks