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Merge branch 'Documentation'
This commit is contained in:
Roland Hollós
commit
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#' This is the function which is capable change multiple specific lines to other using their row numbers.
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#' This is the function which is capable to change multiple specific lines to others using their row numbers.
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#'
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#' he function uses the previous changspecline function to operate.
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##From now changespecline is in the forarcheologist file, because its no longer needed
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#' The function uses the previous changspecline function to operate.
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##From now changespecline is in the forarcheologist file, because itis no longer needed
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#'
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#' @author Roland Hollos
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#' @keywords internal
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#'rungetMuso
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#'
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#' This function runs the BBGC-MuSo model and reads in its outputfile in a very structured way.
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#' This function runs the Biome-BGCMuSo model and reads its outputfile in a well structured way.
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#'
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#' @author Roland Hollos
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#' @keywords internal
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#' musoMonte
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#'
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#' This function does monteCarlo on BiomeBGC-MuSo. It samples specified modell variables in given rangge from conditional multivariate uniform distribution, and runs the modell for each run.
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#' @author Roland Hollos
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#' @param settings A list of montecarlos environmental variables. It is generated by the setupMuso() function. In default the settings parameter is generated automatically.
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#' @param parameters This is a dataframe (heterogen data-matrix), which first column is the name of the parameters, the second is a numeric vector of the rownumbers of the given variable in the epc-fie, the last two column consist the endpont of the parameter-ranges, where the parameters will be randomized.
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#' @param calibrationPar You may want to change some parameters in your epc file, before you run the modell. You have to select the appropirate modell parameters. You can refence to these with the number of the line in the epc file where the variables are. It indexes from one. You should use a vector for this, like: c(1,5,8)
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#' @param inputDir The location of the input directory, this directory must content a viable pack of all inputfiles and the executable file.
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#' @param iterations number of the monteCarlo run.
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#' @param preTag It will be the name of the output files. For example preTag-1.csv, pretag-2csv...
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#' @param outputType This parameter can be "oneCsv", "moreCsv", and "netCDF". If "oneCsv" is choosen the function create 1 big csv file for all of the runs, if "moreCsv" is choosen, every modell output goes to separate files, if netCDF is selected the outputs will be put in a netCDF file. The default value of the outputTypes is "moreCsv". netCDF is not implemented yet.
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#' @param fun If you select a variable from the possible outputs (with specify the varIndex parameter), you have to provide a function which maps to a subset of real numbers. The most frequent possibilities are: mean, min, max, var, but you can define any function for your need.
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#' @param varIndex This parameter specify which parameter of the output will be used. You can extract this information from the ini-files. At the output parameter specifications, the parameters order will determine this number. For example, if you have set these output parameters: 412, 874, 926, 888, and you want to use 926, you should address varIndex with 3.
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#' @param debugging If you set this parameter, you can save every logfile, and RBBGCMuso will select those which contains errors.
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#' @param keepEpc if you set keepEpc also true, it will save every selected epc file, and put the wrong ones in the WRONGEPC directory.
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#' This function executes the Monte Carlo experiment with Biome-BGCMuSo (musoRand is called by this function). It samples the selected model parameters within user defined ranges from conditional multivariate uniform distribution, and then runs the model for each run.
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#' @author Roland HOLLOS
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#' @param settings A list of environmental variables for the Monte Carlo experiment. These settings are generated by the setupMuso function. By default the settings parameter is generated automatically.
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#' @param parameters This is a dataframe (heterogeneous data-matrix), where the first column is the name of the parameter, the second is a numeric vector of the rownumbers of the given variable in the input EPC file, and the last two columns describe the minimum and the maximum of the parameter (i.e. the parameter ranges), defining the interval for the randomization.
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#' @param calibrationPar You might want to change some parameters in your EPC file before you run the modell. You have to select the appropirate model parameters here. You can refer to the parameters by the number of the line in the EPC file where the variables are defined. The indexing of the lines starts at 1, and each line matters (like in any simple text file). You should use a vector for this selection like c(1,5,8)
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#' @param inputDir The location of the input directory for the Biome-BGCMuSo model. This directory must contain a viable pack of all input files and the model executable file.
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#' @param iterations Number of the Monte Carlo simulations.
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#' @param preTag This defines the name of the output files. This tag will be re-used so that the results will be like preTag-1.csv, preTag-2csv...
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#' @param outputType This parameter can be "oneCsv", "moreCsv", and "netCDF". If "oneCsv" is chosen the function creates one large csv file for all of the runs. If "moreCsv" is chosen, every model output goes to separate files. If netCDF is selected the output will be stored in a netCDF file. The default value of the outputTypes is "moreCsv". Note that netCDF is not implemented yet.
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#' @param fun If you select a variable from the possible outputs (by using the varIndex parameter), you have to provide a function which maps to a subset of real numbers. The most frequent possibilities are: mean, min, max, var, but you can define any function for your needs.
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#' @param varIndex This parameter specifies which parameter will be used for the Monte Carlo experiment from the output list of Biome-BGCMuSo (defined by the INI file). You can extract this information from the INI files. At the output parameter specifications, the parameter order will determine this number. For example, if you have set these output parameters: 412, 874, 926, 888, and you want to use 926 for the experiment, you should specify varIndex as 3.
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#' @param debugging If you set this parameter, you can save every logfile, and RBBGCMuso will select those which contains errors. This is useful to study why the model crashes with a given parameter set.
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#' @param keepEpc If you set keepEpc as TRUE, it will save every selected EPC file, and move the wrong ones into the WRONGEPC directory.
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#' @export
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musoMonte <- function(settings=NULL,
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#' musoRand
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#'
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#' This funtion samples uniformly from the chosen parameters of the Biome-BGCMuSo model, where the parameters are constrained by the model logic.
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#' @author Roland Hollos
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#' @param parameters This is a dataframe (heterogeneous data-matrix), where first column is the name of the parameters, the second is a numeric vector of the rownumbers of the given variable in the input-file, the last two column consist the endpont of the parameter-ranges, where the parameters will be randomized.
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#' @param constrains This is a matrics wich specify the constrain rules for the sampling. Further informations coming son.
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#' @param iteration The number of samples. We propose to use at least 3000 iteration, because it is generally fast and it can be subsampled later at any time.
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#' This funtion uses the Monte Carlo technique to uniformly sample the parameter space from user defined parameters of the Biome-BGCMuSo model. The sampling algorithm ensures that the parameters are constrained by the model logic which means that parameter dependencies are fully taken into account (parameter dependency means that e.g leaf C:N ratio must be smaller than C:N ratio of litter; more complicated rules apply to the allocation parameters where the allocation fractions to different plant compartments must sum up 1). This function implements a mathematically correct solution to provide uniform distriution for all selected parameters.
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#' @author Roland HOLLOS
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#' @param parameters This is a dataframe (heterogeneous data-matrix), where the first column is the name of the parameter, the second is a numeric vector of the rownumbers of the given variable in the input EPC file, and the last two columns describe the minimum and the maximum of the parameter (i.e. the parameter ranges), defining the interval for the randomization.
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#' @param constrains This is a matrix wich specify the constrain rules for the sampling. Parameter dependencies are described in the Biome-BGCMuSo User's Guide. Further informations is coming soon.
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#' @param iteration The number of samples for the Monte-Carlo experiment. We propose to use at least 3000 iteration because it is generally fast and it can be subsampled later at any time.
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#' @importFrom limSolve xsample
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#' @export
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#' musoSensi
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#'
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#' This function does regression based sensitivity analysis based on the output of musoMonte.
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#' @author Roland Hollos
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#' @param monteCarloFile If you run musoMonte function previously, you did not have to rerun the monteCarlo, just provide the preservedEpc.csv file with its path. If you do not set this parameter, musoSensi will fun the musoMonte function to get all of the information.
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#' @param outputFile The filename in which the output of musoSensi function will be saved. It's default value is: "sensitivity.csv"
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#' @param plotName The name of the output barplot. It's default value is: "sensitivity.jpg"
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#' @param settings A list of montecarlos environmental variables. It is generated by the setupMuso() function. In default the settings parameter is generated automatically.
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#' @param parameters This is a dataframe (heterogen data-matrix), which first column is the name of the parameters, the second is a numeric vector of the rownumbers of the given variable in the epc-fie, the last two column consist the endpont of the parameter-ranges, where the parameters will be randomized.
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#' @param calibrationPar You may want to change some parameters in your epc file, before you run the modell. You have to select the appropirate modell parameters. You can refence to these with the number of the line in the epc file where the variables are. It indexes from one. You should use a vector for this, like: c(1,5,8)
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#' @param inputDir The location of the input directory, this directory must content a viable pack of all inputfiles and the executable file.
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#' @param iterations number of the monteCarlo run.
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#' @param preTag It will be the name of the output files. For example preTag-1.csv, pretag-2csv...
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#' @param outputType This parameter can be "oneCsv", "moreCsv", and "netCDF". If "oneCsv" is choosen the function create 1 big csv file for all of the runs, if "moreCsv" is choosen, every modell output goes to separate files, if netCDF is selected the outputs will be put in a netCDF file. The default value of the outputTypes is "moreCsv". netCDF is not implemented yet.
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#' @param fun If you select a variable from the possible outputs (with specify the varIndex parameter), you have to provide a function which maps to a subset of real numbers. The most frequent possibilities are: mean, min, max, var, but you can define any function for your need.
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#' @param varIndex This parameter specify which parameter of the output will be used. You can extract this information from the ini-files. At the output parameter specifications, the parameters order will determine this number. For example, if you have set these output parameters: 412, 874, 926, 888, and you want to use 926, you should address varIndex with 3.
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#' @param skipSpinup With this parameter, you can turn of the spinup phase after the first spinup. I will decrease the time significantly.
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#' This function performs multiple liear regression based global sensitivity analysis based on the output of musoMonte. First the user has to select the parameters of interest with possible minimum and maximum values. After execution musoSensi will then assign weights to the predefined parameters which means that the importance of the parameters will be ranked. The sensitivity analysis calculates the effect of input variability on the output variability in Monte Carlo framework. The result will largely depend on the selected output variable (GPP, evapotranspiration, LAI, soil water content), and on the parameter ranges. Other factors like climate, management and site specific conditions might affect the results.
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#' @author Roland HOLLOS
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#' @param monteCarloFile If you run the musoMonte function previously, you do not have to re-run the monteCarlo experiment, simply provide the preservedEpc.csv file to musoSensi with its path. If you do not set this parameter, musoSensi will run the musoMonte function to get all necessary information.
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#' @param outputFile The filename in which the output of the musoSensi function will be saved. By default it is "sensitivity.csv"
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#' @param plotName The name of the output barplot. It's default value is "sensitivity.jpg"
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#' @param settings A list of environmental variables for the Monte Carlo experiment. These settings are generated by the setupMuso function. By default the settings parameter is generated automatically.
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#' @param parameters This is a dataframe (heterogeneous data-matrix), where the first column is the name of the parameter, the second is a numeric vector of the rownumbers of the given variable in the input EPC file, and the last two columns describe the minimum and the maximum of the parameter (i.e. the parameter ranges), defining the interval for the randomization.
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#' @param calibrationPar You might want to change some parameters in your EPC file before you run the modell. You have to select the appropirate model parameters here. You can refer to the parameters by the number of the line in the EPC file where the variables are defined. The indexing of the lines starts at 1, and each line matters (like in any simple text file). You should use a vector for this selection like c(1,5,8)
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#' @param inputDir The location of the input directory for the Biome-BGCMuSo model. This directory must contain a viable pack of all input files and the model executable file.
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#' @param iterations Number of the Monte Carlo simulations.
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#' @param preTag This defines the name of the output files. This tag will be re-used so that the results will be like preTag-1.csv, preTag-2csv...
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#' @param outputType This parameter can be "oneCsv", "moreCsv", and "netCDF". If "oneCsv" is chosen the function creates one large csv file for all of the runs. If "moreCsv" is chosen, every model output goes to separate files. If netCDF is selected the output will be stored in a netCDF file. The default value of the outputTypes is "moreCsv". Note that netCDF is not implemented yet.
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#' @param fun If you select a variable from the possible outputs (by using the varIndex parameter), you have to provide a function which maps to a subset of real numbers. The most frequent possibilities are: mean, min, max, var, but you can define any function for your needs.
|
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#' @param varIndex This parameter specifies which parameter will be used for the Monte Carlo experiment from the output list of Biome-BGCMuSo (defined by the INI file). You can extract this information from the INI files. At the output parameter specifications, the parameter order will determine this number. For example, if you have set these output parameters: 412, 874, 926, 888, and you want to use 926 for the experiment, you should specify varIndex as 3.
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#' @param skipSpinup With this parameter you can turn off the spinup phase after the first spinup was successfully executed (endpoint file is available). This option can dramatically decrease the time needed for the sensitivity analysis. Note that in case of natural vegetation this option might not be feasible. For croplands this is more feasible.
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#' @importFrom ggplot2 geom_bar ggplot aes theme element_text xlab ylab ggtitle ggsave scale_y_continuous
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#' @importFrom scales percent
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#' @export
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#' @author Roland HOLLOS
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#' @param settings RBBGCMuso uses variables that define the entire simulation environment. Those environment variables include the name of the INI files, the name of the meteorology files, the path to the model executable and its file name, the entire output list, the entire output variable matrix, the dependency rules for the EPC parameters etc. Using the runMuso function RBBGCMuso can automatically create those environment variables by inspecting the files in the working directory (this happens through the setupMuso function). It means that by default model setup is performed automatically in the background and the user has nothing to do. With this settings parameter we can force runMuso to skip automatic environment setup as we provide the environment settings to runMuso. In a typical situation the user can skip this option.
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#' @param timee The required timesteps in the model output. It can be "d", if it is daily, "m", if it is monthly, "y" if it is yearly. It is recommended to use daily data, as the yearly and monthly data is not well-tested yet.
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#' @param debugging If debugging is set to TRUE, after model execution the function copies the Biome-BGCMuSo log file into a LOG directory to stores it for further processing. If debugging is set to STAMPLOG instead of TRUE, it concatenates a number before the logfile, which is one plus the maximum of those present in the LOG directory. In each case the log files will be saved.
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#' @param debugging If debugging is set to TRUE, after model execution the function copies the Biome-BGCMuSo log file into a LOG directory and stores it for further processing. If debugging is set to STAMPLOG instead of TRUE, it concatenates a number before the logfile, which is one plus the maximum of those present in the LOG directory. In each case the log files will be saved.
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#' @param keepEpc If keepEpc is set to TRUE, the function keeps the EPC file and stamps it, and then copies it to the EPCS directory. If debugging is set to TRUE, it copies the wrong EPC files to the wrong epc directory.
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#' @param export If it is set to YES or you define a filename here, the function converts the output to the specific file format. For example, if you set export to "example.csv", it converts the output to "csv". If you set it to "example.xls" it converts the output to example.xls with the xlsx package. If the Excel converter package is not installed it gives back a warning message and converts the results to csv.
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#' @param silent If you set the silent parameter to TRUE, all of the model's output normally written to the screen will be suppressed. This option can be useful to increase the speed of the model execution.
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#'plot the BBGCMuso output
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#'plot the Biome-BGCMuSo output
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#'
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#' This function runs the BBGC-MuSo model and reads in its outputfile in a very structured way, and after that plot the results automaticly
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#' This function runs the Biome-BGCMuSo model and reads its output file in a well structured way, and after that it plots the results automatically. plotMuso is a convenient and quick method to create nice graphs from Biome-BGCMuSo output which is quite painful in other environments.
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#'
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#' @author Roland Hollos, Dora Hidy
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#' @param settings You have to run the setupMuso function before rungetMuso. It is its output which contains all of the necessary system variables. It sets the whole environment
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#' @param timee The required timesteps in the modell output. It can be "d", if it is daily, "m", if it's monthly, "y", it it is yearly
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#' @param debugging If it is TRUE, it copies the log file to a Log directory to store it, if it is stamplog it contatenate a number before the logfile, which is one more than the maximum of the represented ones in the LOG directory. If it is true or stamplog it collects the "wrong" logfiles
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#' @param keepEpc If TRUE, it keeps the epc file and stamp it, after these copies it to the EPCS directory. If debugging True or false, it copies the wrong epc files to the wrong epc directory.
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#' @param export if it is yes or you give a filename here, it converts the output to the specific extension. For example, if you set export to "example.csv", it converts the output to "csv", if you set it to "example.xls" it converts to example.xls with the xlsx package. If it is not installed it gives back a warning message and converts it to csv.
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#' @param silent If you set it TRUE all off the modells output to the screen will be suppressed. It can be usefull, because it increases the model-speed.
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#' @param aggressive It deletes every possible modell-outputs from the previous modell runs.
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#' @param variable column number of the variable which should be plottedor "all" if you have less than 10 variables. In this case it will plot everything in a matrix layout
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#' @param leapYear Should the function do a leapyear correction on the outputdata? If TRUE, then the 31.12 day will be doubled.
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#' @param logfilename If you want to set a specific name for your logfiles you can set this via logfile parameter
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#' @param plotType There are two options now: continious time series("cts") or disctrete time series("dts")
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#' @param skipSpinup If TRUE, calibMuso wont do spinup simulation
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#' @return It depends on the export parameter. The function returns with a matrix with the modell output, or writes this in a file, which is given previously
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#' @author Roland HOLLOS, Dora HIDY
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#' @param settings RBBGCMuso uses variables that define the entire simulation environment. Those environment variables include the name of the INI files, the name of the meteorology files, the path to the model executable and its file name, the entire output list, the entire output variable matrix, the dependency rules for the EPC parameters etc. Using the runMuso function RBBGCMuso can automatically create those environment variables by inspecting the files in the working directory (this happens through the setupMuso function). It means that by default model setup is performed automatically in the background and the user has nothing to do. With this settings parameter we can force runMuso to skip automatic environment setup as we provide the environment settings to runMuso. In a typical situation the user can skip this option.
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#' @param timee The required timesteps in the model output. It can be "d", if it is daily, "m", if it is monthly, "y" if it is yearly. It is recommended to use daily data, as the yearly and monthly data is not well-tested yet.
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#' @param debugging If debugging is set to TRUE, after model execution the function copies the Biome-BGCMuSo log file into a LOG directory and stores it for further processing. If debugging is set to STAMPLOG instead of TRUE, it concatenates a number before the logfile, which is one plus the maximum of those present in the LOG directory. In each case the log files will be saved.
|
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#' @param keepEpc If keepEpc is set to TRUE, the function keeps the EPC file and stamps it, and then copies it to the EPCS directory. If debugging is set to TRUE, it copies the wrong EPC files to the wrong epc directory.
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#' @param export If it is set to YES or you define a filename here, the function converts the output to the specific file format. For example, if you set export to "example.csv", it converts the output to "csv". If you set it to "example.xls" it converts the output to example.xls with the xlsx package. If the Excel converter package is not installed it gives back a warning message and converts the results to csv.
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#' @param silent If you set the silent parameter to TRUE, all of the model's output normally written to the screen will be suppressed. This option can be useful to increase the speed of the model execution.
|
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#' @param aggressive It deletes all previous model-outputs from previous model runs.
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#' @param variable Column number of the output variable which should be plotted, or "all" if you have less than 10 variables. In this case the function will plot everything in a matrix layout.
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#' @param leapYear Should the function do a leapyear correction on the output data? If TRUE, then the result for 31 December will be doubled in leap years which means that the results for the leap year will cover all 366 days. See the model's User's Guide for notes on leap years.
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#' @param logfilename If you would like to set a specific name for your logfiles you can set this via the logfile parameter.
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#' @param plotType There are two options implemented by now: continuous time series ("cts") or disctrete time series ("dts")
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#' @param skipSpinup If TRUE, the function won't perform the spinup simulation. In this case the endpoint file must exist that provides initial conditions for the run.
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#' @return It depends on the export parameter. The function returns with a matrix with the model output, or writes this into a file, which is defined previously
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#' @usage plotMuso(settings, variable,
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#' timee="d", silent=TRUE,
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#' debugging=FALSE, keepEpc=FALSE,
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@ -239,19 +239,19 @@ plotMuso <- function(settings=NULL,
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plotName = plotName)
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}
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#'plot the BBGCMuso output with data
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#'plot the Biome-BGCMuSo model output with observation data
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#'
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#' This function runs the BBGC-MuSo model and reads in its outputfile in a very structured way, and after that plot the results automaticly along with a given measurement
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#' This function runs the Biome-BGCMuSo model and reads its output file in a well structured way, and after that it plots the results automatically along with a given measurement dataset provided by the user. plotMusoWithData is a convenient and quick method to create nice graphs from Biome-BGCMuSo output which is quite painful in other environments.
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#'
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#' @author Roland Hollos, Dora Hidy
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#' @param settings You have to run the setupMuso function before rungetMuso. It is its output which contains all of the necessary system variables. It sets the whole environment
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#' @param sep This is the separator used in the measurement file
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#' @param savePlot It it is specified, the plot will be saved in a format specified with the immanent extension
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#' @author Roland HOLLOS, Dora HIDY
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#' @param settings RBBGCMuso uses variables that define the entire simulation environment. Those environment variables include the name of the INI files, the name of the meteorology files, the path to the model executable and its file name, the entire output list, the entire output variable matrix, the dependency rules for the EPC parameters etc. Using the runMuso function RBBGCMuso can automatically create those environment variables by inspecting the files in the working directory (this happens through the setupMuso function). It means that by default model setup is performed automatically in the background and the user has nothing to do. With this settings parameter we can force runMuso to skip automatic environment setup as we provide the environment settings to runMuso. In a typical situation the user can skip this option.
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#' @param sep This is the separator symbol used in the measurement file (that is supposed to be a delimited text file)
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#' @param savePlot It it is specified, the plot will be saved in a graphical format specified by the immanent extension. For example, it the savePlot is set to image01.png then a PNG graphics file will be created.
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#' @param variable The name of the output variable to plot
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#' @param NACHAR This is not implemented yet
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#' @param csvFile The file of the measurement. It must contain a header.
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#' @param calibrationPar documentation in setupMuso()
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#' @param parameters documentation in calibMuso()
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#' @param csvFile This specifies the filename of the measurements. It must contain a header. Typically this is a CSV file.
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#' @param calibrationPar You might want to change some parameters in your EPC file before running the model. The function offers possibility for this without editing the EPC file. In this situation you have to select the appropirate model parameters first. You can refer to these parameters with the number of the line in the EPC file. Indexing of lines start from one. You should use a vector for this referencing like c(1,5,8)
|
||||
#' @param parameters Using the function it is possible to change some of the EPC parameters prior to model execution. This can be achieved with this option. In the parameters variable you have set the row indices of the variables that you wish to change. In this parameters you can give an exact value for them in a vector form like c(1,2,3,4).
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#' @usage plotMuso(settings, variable,
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#' timee="d", silent=TRUE,
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#' debugging=FALSE, keepEpc=FALSE,
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@ -300,14 +300,14 @@ plotMusoWithData <- function(csvFile, variable, NACHAR=NA, settings=NULL, sep=",
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#' compareMuso
|
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#'
|
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#' This function runs the modell, change one of it's input, and plot both in one plot.
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#' This function runs the model, then changes one of its input data, runs it again, and plots both results in one graph.
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#'
|
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#' @author Roland Hollos
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#' @param settings You have to run the setupMuso function before rungetMuso. It is its output which contains all of the necessary system variables. It sets the whole environment
|
||||
#' @param parameters In the settings variable you have set the row indexes of the variables, you wish to change. In this parameter you can give an exact value for them in a vector like: c(1,2,3,4)
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#' @author Roland HOLLOS
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||||
#' @param settings RBBGCMuso uses variables that define the entire simulation environment. Those environment variables include the name of the INI files, the name of the meteorology files, the path to the model executable and its file name, the entire output list, the entire output variable matrix, the dependency rules for the EPC parameters etc. Using the runMuso function RBBGCMuso can automatically create those environment variables by inspecting the files in the working directory (this happens through the setupMuso function). It means that by default model setup is performed automatically in the background and the user has nothing to do. With this settings parameter we can force runMuso to skip automatic environment setup as we provide the environment settings to runMuso. In a typical situation the user can skip this option.
|
||||
#' @param parameters Using this function it is possible to change some of the EPC parameters prior to model execution. This can be achieved with this option. In the parameters variable you have set the row indices of the variables that you wish to change. In this parameters you can give an exact value for them in a vector form like c(1,2,3,4).
|
||||
#' @param variable The name of the output variable to plot
|
||||
#' @param calibrationPar in the help of setupMuso function.
|
||||
#' @param fileToChange You can change any line of the epc or the ini file, you just have to specify with this variable which file you van a change. Two options possible: "epc", "ini", "both"
|
||||
#' @param calibrationPar You might want to change some parameters in your EPC file before running the model. This function offers possibility for this without editing the EPC file. In this situation you have to select the appropirate model parameters first. You can refer to these parameters with the number of the line in the EPC file. Indexing of lines start from one. You should use a vector for this referencing like c(1,5,8)
|
||||
#' @param fileToChange You can change any line of the EPC or the INI file. Please choose "EPC", "INI" or "BOTH". This file will be used for the analysis, and the original parameter values will be changed according to the choice of the user.
|
||||
#' @import ggplot2
|
||||
#' @export
|
||||
compareMuso <- function(settings=NULL,parameters, variable=1, calibrationPar=NULL, fileToChange="epc", skipSpinup=TRUE, timeFrame="day"){
|
||||
|
||||
@ -3,7 +3,7 @@
|
||||
#' This function runs the Biome-BGCMuSo model in spinup phase.
|
||||
#'
|
||||
#' @author Roland HOLLOS
|
||||
#' @param settings IRBBGCMuso uses variables that define the entire simulation environment. Those environment variables include the name of the INI files, the name of the meteorology files, the path to the model executable and its file name, the entire output list, the entire output variable matrix, the dependency rules for the EPC parameters etc. Using the runMuso function RBBGCMuso can automatically create those environment variables by inspecting the files in the working directory (this happens through the setupMuso function). It means that by default model setup is performed automatically in the background and the user has nothing to do. With this settings parameter we can force runMuso to skip automatic environment setup as we provide the environment settings to runMuso. In a typical situation the user can skip this option.
|
||||
#' @param settings RBBGCMuso uses variables that define the entire simulation environment. Those environment variables include the name of the INI files, the name of the meteorology files, the path to the model executable and its file name, the entire output list, the entire output variable matrix, the dependency rules for the EPC parameters etc. Using the runMuso function RBBGCMuso can automatically create those environment variables by inspecting the files in the working directory (this happens through the setupMuso function). It means that by default model setup is performed automatically in the background and the user has nothing to do. With this settings parameter we can force runMuso to skip automatic environment setup as we provide the environment settings to runMuso. In a typical situation the user can skip this option.
|
||||
#' @param debugging If debugging is set to TRUE, after model execution the function copies the Biome-BGCMuSo log file into a LOG directory to stores it for further processing. If debugging is set to STAMPLOG instead of TRUE, it concatenates a number before the logfile, which is one plus the maximum of those present in the LOG directory. In each case the log files will be saved.
|
||||
#' @param keepEpc If keepEpc is set to TRUE, the function keeps the EPC file and stamps it, and then copies it to the EPCS directory. If debugging is set to TRUE, it copies the wrong EPC files to the wrong epc directory.
|
||||
#' @param silent If you set the silent parameter to TRUE, all of the model's output normally written to the screen will be suppressed. This option can be useful to increase the speed of the model execution.
|
||||
|
||||
@ -4,7 +4,11 @@
|
||||
|
||||
* The RBBGCMuso Package
|
||||
#+AUTHOR: Roland HOLLÓS, Dóra HIDY, Zoltán BARCZA
|
||||
<<<<<<< HEAD
|
||||
*Current version: 0.6.1.3*
|
||||
=======
|
||||
*Current version: 0.6.1*
|
||||
>>>>>>> Documentation
|
||||
|
||||
RBBGCMuso is an R package which supports the easy but powerful application of the [[http://agromo.agrar.mta.hu/bbgc/][Biome-BGCMuSo]] biogeochemical model in R environment. It also provides some additional tools for the model such as Biome-BGCMuSo optimized Monte-Carlo simulation and global sensitivity analysis. If you would like to use the framework, please read the following description.
|
||||
|
||||
|
||||
Loading…
Reference in New Issue
Block a user