R/plot-methods.R

In RoyChristian/R2MCDS: Calling MCDS from R.

#' Title Plot a detection histogram of apparent density and the predicted detction curve for the model.
#'
#'@param model A \code{distanceFit} object
#'
#'@details
#' Make a histogram of the observations and the predicted detection curve in function of the distance class. 
#'@section Author:Christian Roy
#'@examples
#'data(alcidae)
#'alcids <- mcds.filter(alcidae,
#'                      transect.id = "WatchID",
#'                      distance.field = "Distance",
#'                      distance.labels = c("A", "B", "C", "D"),
#'                      distance.midpoints = c(25, 75, 150, 250),
#'                      effort.field = "WatchLenKm",
#'                      lat.field = "LatStart",
#'                      long.field = "LongStart",
#'                      sp.field = "Alpha",
#'                      date.field = "Date") 
#'
#'### Run analysis with the MCDS engine based on line transect data. Here, the WatchID is used as the sample.
#'dist.out1 <- mcds.wrap(alcids,
#'                       SMP_EFFORT="WatchLenKm",
#'                       DISTANCE="Distance",
#'                       SIZE="Count",
#'                       Type="Line",
#'                       units=list(Distance="Perp",
#'                                  Length_units="Kilometers",
#'                                  Distance_units="Meters",
#'                                  Area_units="Square kilometers"),
#'                       breaks=c(0,50,100,200,300),
#'                       estimator=list(c("HN","CO")),
#'                       STR_LABEL="STR_LABEL",
#'                       STR_AREA="STR_AREA",
#'                       SMP_LABEL="WatchID", 
#'                       path="c:/temp/distance",
#'                       pathMCDS="C:/Distance 7",
#'                       verbose=FALSE)
#'
#'summary(dist.out1)
#'plot(dist.out1)
#'
#' #END
#' 
#'@rdname plot
#'@export
plot.distanceFit <- function(model){
  #browser()
  Type <- model[['model_fitting']][['Global']][['Parameters']][['UNITS']][2]
  
  #browser()
  
  p <- observation_hist(model[['input_data']][['observations']],
                        count='SIZE',
                        dist.class='DISTANCE',
                        keep.class=as.character(unique(sort(as.numeric(model[['input_data']][['observations']]$DISTANCE)))),
                        breaks=model[['input_data']][['breaks']], color='powderblue', ungroup=F,
                        rescale=model$detection[['Global']][,'predicted'][1],
                        
                        Type = Type
  ) +
    labs(x = 'Distance',
         y = 'Detection probability',
         title = sub("\\,.*", "", model[['Key_Adj']])) # Add for more details about the plot
  
  pred.df <- data.frame(x=model$detection[['Global']][,'distance'],
                        y=model$detection[['Global']][,'predicted'])
  p + geom_line(data=pred.df, aes(x=x,y=y), linetype=1, size=1.25)
  
}