correlationMatrix {amber} | R Documentation |

This function produces a correlation matrices for mean values, *bias*, *rmse*,
*crmse*, *phase*, and corresponding scores. The input data consist of netCDF files
produced by scores.grid.time and scores.grid.notime.

correlationMatrix(metric, fileList = FALSE, myFiles = NA, myRows = NA, myColumns = NA, inputDir, outputDir = FALSE, significanceLevel = 0.01, plot.width = 8, plot.height = 6.8, plot.margin = c(10, 10, 1, 4))

`metric` |
A string that indicates for what statistical metric the correlation matrix should be computed. Options are 'mod-mean', ref-mean', bias', 'crmse', 'phase', 'bias-score', 'crmse-score', 'phase-score', or 'iav-score'. |

`fileList` |
Logical, FALSE by default. Should be set to TRUE if user defines a list of files that shall be included in the analysis. If set to FALSE, the function will search for all files whose names include the string of the metric defined above. |

`myFiles` |
Names of NetCDF files that shall be included in the analysis. Only relevant if fileList is TRUE. |

`myRows` |
Optional: the user can highlight relations between variables by specifying variable names along the rows and columns of a matrix. Those relations are then highlighted by plotting a polygon around the corresponding grid cells and by plotting the corresponding value. |

`myColumns` |
Optional: Same as myRows but for variable names listed along the columns of the matrix. |

`inputDir` |
A string that gives the location of NetCDF files produced by scores.grid.time, e.g. '/home/project/study'. |

`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. |

`significanceLevel` |
A number that gives the desired significance level of a correlation, e.g. 0.01 |

`plot.width` |
A number that gives the plot width, e.g. 8 |

`plot.height` |
A number that gives the plot height, e.g. 6.8 |

`plot.margin` |
An R object that gives the plot margin, e.g. c(10, 10, 1, 4) |

A list with the Spearman correlation coefficient and corresponding p-values, and a Figure of the correlation matrix

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) inputDir <- paste(system.file('extdata', package = 'amber'), 'zonalMeanStats', sep = '/') correlationMatrix(metric = 'bias', inputDir = inputDir) # Alternatively, use a list of file names myFiles <- c('GPP-FluxCom-bias.nc', 'LAI-AVHRR-bias.nc', 'ALBS-CERES-bias.nc') # You can specify certain relationships to highlight them in your correlation matrix myRows <- c('LAI.AVHRR', 'ALBS.CERES') myColumns <- c('GPP.FluxCom', 'GPP.FluxCom') correlationMatrix(metric = 'bias', fileList = TRUE, myFiles = myFiles, myRows = myRows, myColumns = myColumns, inputDir = inputDir) #donttest

[Package *amber* version 1.0.3 Index]