Merge branch 'development' of github.com:PredictiveEcology/fireSenseU…

…tils into development

R/predictIgnition.R

@@ -1,26 +1,30 @@
 #' Predictions from ignition model
 #'
-#' @param model formula of fitted model (`sim$fireSense_IgnitionFitted[["formula"]][-2]`)
+#' @param model the model used to fit ingition data - must have a predict method
 #' @param data data for prediction
-#' @param coefs model coefficients (`sim$fireSense_IgnitionFitted$coef`)
 #' @param rescaleFactor spatial rescaling factor when predicted and fitted data are at different scales.
-#'     Calculaed as: (predResolution/fitResolution)^2
+#'     Calculated as: (predResolution/fitResolution)^2
 #' @param lambdaRescaleFactor  If the data for fitting has been sampled for pseudo-absences,
 #'    this imposes a new baseline probability of fire occurrences, hence predictions need to be adjusted.
 #'    If the  original fire prob. is (total no. fires)/(total no. fires + total no. absences), and the fire
 #'    probability imposed by sampling is (total no. fires)/(total no. fires + no. sampled pseudo-absences), to
 #'    adjust predicted values, one needs to multiply them by (total no. fires + no. sampled pseudo-absences/(total no. fires + total no. absences)
-#' @param linkinv family link function (`sim$fireSense_IgnitionFitted$family$linkinv`)
 #'
 #' @return vector of predicted values.
 #'
 #' @export
 
-predictIgnition <- function(model, data, coefs, rescaleFactor, lambdaRescaleFactor, linkinv) {
-  mm <- model.matrix(model, data)
-  pred <- mm %*% coefs
-  pred <- drop(pred)
-  pred <- linkinv(pred)
-  pred <- pred * rescaleFactor
-  pred * lambdaRescaleFactor
+predictIgnition <- function(model, data, rescaleFactor, lambdaRescaleFactor) {
+
+  ignitions <- predict(model, newdata = data)
+  ignitions <- ignitions * rescaleFactor * lambdaRescaleFactor
+  return(ignitions)
+
+  # mm <- model.matrix(model, data)
+  # pred <- mm %*% coefs
+  # pred <- drop(pred)
+  # pred <- linkinv(pred)
+  # pred <- pred * rescaleFactor
+  # pred * lambdaRescaleFactor
+
 }