amber (version 1.0.3)

scores.compare.ensemble: Summarize scores from multiple model runs in single figure.

Description

This function produces a figure that summarizes score values from multiple model runs. The figure has four columns, which give the multi-model mean scores, the total score range, the model with the lowest score and the model with the highest score. The respective inputs are created by the functions scores.fluxnet.csv or scores.fluxnet.nc, scores.grid.notime, scores.grid.time, and scores.runoff.

Usage

scores.compare.ensemble(mod.path.list = mod.path.list,
  modelIDs = modelIDs, myVariables = myVariables, plot.width = 10,
  plot.height = 10, myMargin = c(12, 0, 3, 0), outputDir = FALSE)

Arguments

mod.path.list

A list with paths for each model run, e.g. mod.path.list <- list(mod01.path, mod02.path, mod03.path)

modelIDs

An R object with the different model run IDs, e.g. c('CLASSIC.CRUJRAv2', 'CLASSIC.GSWP3W5E5', 'CLASSIC.CRUNCEP')

myVariables

An R object with variable names of variables that should be included in table, e.g. c('GPP', 'RECO', 'NEE')

plot.width

Number that gives the plot width, e.g. 6

plot.height

Number that gives the plot height, e.g. 5

myMargin

An R object that gives the figure margins, e.g. c(4, 13, 3, 4)

outputDir

A string that gives the output directory, e.g. '/home/project/study'. The output will only be written if the user specifies an output directory.

Value

A figure in PDF format that shows the ensemble scores, the total score range, the model with the lowest score and the model with the highest score.

Examples

# NOT RUN {
library(amber)
library(classInt)
library(doParallel)
library(foreach)
library(Hmisc)
library(latex2exp)
library(ncdf4)
library(parallel)
library(raster)
library(rgdal)
library(rgeos)
library(scico)
library(sp)
library(stats)
library(utils)
library(viridis)
library(xtable)

mod01.path <- system.file('extdata/model01', package = 'amber')
mod02.path <- system.file('extdata/model02', package = 'amber')
mod.path.list <- list(mod01.path, mod02.path)
modelIDs <- c('CLASSIC.CRUJRAv2', 'CLASSIC.GSWP3W5E5')
myVariables <- c('GPP', 'BURNT')
#myVariables <- c('RNS', 'RSS', 'RLS', 'ALBS', 'HFLS', 'HFSS', 'HFG', 'GPP', 'RECO',
#'NEE', 'FIRE', 'AGB', 'CVEG', 'CSOIL', 'LAI', 'BURNT', 'SNW', 'MRSLL', 'MRRO')

scores.compare.ensemble(mod.path.list = mod.path.list, modelIDs = modelIDs,
myVariables = myVariables, plot.width = 9.3, plot.height = 10,
myMargin = c(12, 0, 3, 0), outputDir = FALSE)

# }