plot.phenology: Plot the phenology from a result.

In phenology: Tools to Manage a Parametric Function that Describes Phenology and More

Plot the phenology from a result.

Description

The function plot.phenology plots the phenology graph from a result object.
If cofactors have been added, the plot does not show their effects.
plot.objects can be "observations", "ML" for maximum likelihood, "ML.SD" for dispersion of observations, "ML.quantiles" or "MCMC.quantiles" if a mcmc object is given

Usage

## S3 method for class 'phenology'
plot(
  x,
  ...,
  series = "all",
  moon = FALSE,
  replicate.CI = 10000,
  resultmcmc = NULL,
  season = NULL,
  chain = 1,
  replicate.CI.mcmc = "all",
  level = 0.95,
  plot.objects = c("observations", "ML", "ML.SD", "ML.quantiles", "MCMC.quantiles"),
  col.ML = "black",
  col.SD = "red",
  col.SD.polygon = rgb(red = 1, green = 0, blue = 0, alpha = 0.2),
  col.MCMC.quantiles = "purple",
  col.MCMC.quantiles.polygon = rgb(red = 160/255, green = 32/255, blue = 240/255, alpha =
    0.2),
  col.ML.quantiles = "black",
  col.ML.quantiles.polygon = rgb(red = 0, green = 0, blue = 0, alpha = 0.2),
  col.observations = "black",
  col.minimum.observations = "blue",
  col.grouped.observations = "green"
)

Arguments

x	A result file generated by fit_phenology
...	Parameters used by plot
series	Name or number of series to be plotted or 'all'
moon	If TRUE, the moon phase is ploted. Default is FALSE
replicate.CI	Number of replicates for estimation of confidence interval
resultmcmc	A mcmc object
season	Which season to plot
chain	The number of chain to be used in resultmcmc
replicate.CI.mcmc	Number of iterations to be used or "all"
level	Level to estimate confidence interval or credibility interval
plot.objects	What to plot?
col.ML	Color of the ML mean curve
col.SD	Color of the SD curve (distribution of observations)
col.SD.polygon	Color of the polygon of the SD curve. If FALSE not shown.
col.MCMC.quantiles	Color of the quantiles curve based on mcmc
col.MCMC.quantiles.polygon	Color of the credibility interval polygon based on MCMC. If FALSE not shown.
col.ML.quantiles	Color of the SE curve based on ML
col.ML.quantiles.polygon	Color of the confidence interval polygon based on ML. If FALSE not shown.
col.observations	Color of the points
col.minimum.observations	Color of the points indicating minimum counts
col.grouped.observations	Color of the lines indicating grouped observations

Details

plot.phenology plots the phenology.

Value

A list with four objects: synthesis is a data.frame with global estimate of nesting.
details_MCMC, details_ML and details_mean are lists with day by day information for each series.

Author(s)

Marc Girondot [e-mail]

See Also

Other Phenology model: AutoFitPhenology(), BE_to_LBLE(), Gratiot, LBLE_to_BE(), LBLE_to_L(), L_to_LBLE(), MarineTurtles_2002, MinBMinE_to_Min(), adapt_parameters(), add_SE(), add_phenology(), extract_result(), fit_phenology(), likelihood_phenology(), logLik.phenology(), map_Gratiot, map_phenology(), par_init(), phenology(), phenology2fitRMU(), phenology_MHmcmc(), phenology_MHmcmc_p(), plot.phenologymap(), plot_delta(), plot_phi(), print.phenology(), print.phenologymap(), print.phenologyout(), remove_site(), result_Gratiot, result_Gratiot1, result_Gratiot2, result_Gratiot_Flat, result_Gratiot_mcmc, summary.phenology(), summary.phenologymap(), summary.phenologyout()

Examples

## Not run: 
library(phenology)
# Read a file with data
data(Gratiot)
# Generate a formatted list nammed data_Gratiot 
data_Gratiot <- add_phenology(Gratiot, name = "Complete", 
		reference = as.Date("2001-01-01"), format="%d/%m/%Y")
# Generate initial points for the optimisation
parg <- par_init(data_Gratiot, fixed.parameters=NULL)
parg <- c('Max_Complete' = 33.076044848500167, 25, 
          'MinB_Complete' = 0.21758630798131923, 
          'MinE_Complete' = 0.42493953463205936, 
          'LengthB' = 96.158007568020523, 
          'Peak' = 174.62435300274245, 
          'LengthE' = 62.084876419654634, 
          'Flat' = 0, 
          'Theta' = 3.5864650991821954)
# Run the optimisation
result_Gratiot <- fit_phenology(data=data_Gratiot, 
		                             fitted.parameters=parg, 
		                             fixed.parameters=NULL)
data(result_Gratiot)
# Plot the phenology and get some stats
output <- plot(result_Gratiot)
# Plot only part of the nesting season
ptoutput <- plot(result_Gratiot, xlim=c(as.Date("2001-03-01"),as.Date("2001-08-31")))
# Use month names in English
Sys.setlocale(category = "LC_TIME", locale = "en_GB.UTF-8")
output <- plot(result_Gratiot)
# set back the month name in local R language
Sys.setlocale(category = "LC_TIME", locale = "")
# plot based on quantiles of mcmc object
plot(result_Gratiot, resultmcmc=result_Gratiot_mcmc, 
            plot.objects=c("observations", "MCMC.quantiles"))
plot(result_Gratiot, resultmcmc=result_Gratiot_mcmc, 
            plot.objects=c("observations", "ML.SD", "ML.quantiles"))
plot(result_Gratiot, resultmcmc=result_Gratiot_mcmc, 
            plot.objects=c("observations", "ML.SD", "MCMC.quantiles"))
plot(result_Gratiot, resultmcmc=result_Gratiot_mcmc, 
            plot.objects=c("observations", "ML.quantiles", "MCMC.quantiles"))

## End(Not run)

phenology documentation built on Sept. 11, 2024, 6:07 p.m.