ggplot.visitation_forecast {VisitorCounts}R Documentation

visitation_forecast ggPlot Method

Description

Methods for plotting objects of the class "visitation_forecast".

Usage

## S3 method for class 'visitation_forecast'
ggplot(
  data,
  mapping = aes(),
  difference = FALSE,
  log_outputs = FALSE,
  actual_visitation = NULL,
  xlab = "Time",
  ylab = "Fitted Value",
  pred_color = "#228B22",
  actual_color = "#FF0000",
  size = 1.5,
  main = "Forecasts for Visitation Model",
  plot_points = FALSE,
  date_breaks = "1 month",
  date_labels = "%y %b",
  ...
)

Arguments

data

An object of the "visitation_forecast" class.

mapping

Default list of aesthetic mappings to use for plot. If not specified, must be supplied in each layer added to the plot.

difference

A boolean to plot the differenced series.

log_outputs

A boolean to plot the logged outputs of the forecast.

actual_visitation

A timeseries object representing the actual visitation that will be plotted along site the visitation_forecast object.

xlab

A string that will be used for the xlabel of the plot.

ylab

A string that will be used for the ylabel of the plot.

pred_color

a String that will be used for the predicted series color of the plot.

actual_color

a String that will be used for the actual series color of the plot.

size

A number that represents the thickness of the lines being plotted.

main

A string that will be used for the title of the plot.

plot_points

a boolean to specify if the plot should be points or continous line.

date_breaks

A string to represent the distance between dates that the x-axis should be in. ex "1 month", "1 year".

date_labels

A string to represent the format of the x-axis time labels. ex

...

extra arguments to pass in

Value

No return value, called for plotting objects of the class "visitation_forecast".

Examples

#' #Example:

data("park_visitation")
data("flickr_userdays")

n_ahead <- 12
park <- "YELL"
pud_ts <- ts(park_visitation[park_visitation$park == park,]$pud, start = 2005, freq = 12)
pud_ts <- log(pud_ts)
trend_proxy <- log(flickr_userdays)

mf <- visitation_model(pud_ts,trend_proxy)
vf <- predict(mf,12, only_new = TRUE)
plot(vf)

[Package VisitorCounts version 2.0.3 Index]