R/predict.rem.fi.R

In DistanceDevelopment/mrds: Mark-Recapture Distance Sampling

#' @export
# see predict.ds for documentation
predict.rem.fi <- function(object,newdata=NULL,compute=FALSE, int.range=NULL,
                           integrate=FALSE,...){
  # Functions Used: pdot.dsr.integrate.logistic, is.linear.logistic,
  #                 predict.glm (could also use predict.gam eventually) 
  model <- object
  point <- model$meta.data$point
  width <- model$meta.data$width
  if(is.null(newdata)){
    newdata <- model$data
    newdata <- newdata[newdata$object %in% as.numeric(model$data$object),]
  }

  newdata$offsetvalue <- 0
  newdata2 <- newdata[newdata$observer==2,]
  newdata1 <- newdata[newdata$observer==1,]

  if(!integrate){
    p1 <- predict(model$mr,newdata1,type="response")
    p2 <- predict(model$mr,newdata2,type="response")
    fitted <- p1+p2-p1*p2

    if(is.null(newdata)){
      names(fitted) <- model$mr$data$object[model$mr$data$observer==1]
    }else{
      names(fitted) <- newdata$object[newdata$observer==1]
    }

    return(list(fitted = fitted,
                p1     = p1,
                p2     = p2))
  }else{
    left <- model$meta.data$left
    formula <- paste("~",as.character(model$mr$formula)[3],collapse="")

    if(inherits(model$mr, "gam")){
      integral.numeric <- TRUE
    }else{
      integral.numeric <- is.linear.logistic(newdata,formula,
                                             length(coef(model$mr)),width)
    }

    models <- list(g0model        = formula,
                   scalemodel     = NULL,
                   fullscalemodel = NULL)

    if(is.null(int.range)){
      pdot.list <- pdot.dsr.integrate.logistic(width, width, coef(model$mr),
                                               newdata,integral.numeric,FALSE,
                                               models, rem=TRUE, point=point)
    }else{
      pdot.list <- pdot.dsr.integrate.logistic(int.range, width, coef(model$mr),
                                               newdata,integral.numeric, FALSE,
                                               models, rem=TRUE, point=point)
    }

    if(left !=0){
      pdot.list$pdot <- pdot.list$pdot -
                        pdot.dsr.integrate.logistic(left, width, coef(model$mr),
                                                    newdata,integral.numeric,
                                                    FALSE, models, rem=TRUE,
                                                    point=point)$pdot
    }

    fitted <- pdot.list$pdot
   }

   if(is.null(newdata)){
     names(fitted) <- model$mr$data$object[model$mr$data$observer==1]
   }else{
     names(fitted) <- newdata$object[newdata$observer==1]
  }

  return(list(fitted=fitted))
}