sbpiper (version 1.9.0)

parameter_density_analysis: Parameter density analysis.

Description

Parameter density analysis.

Usage

parameter_density_analysis(model, filename, parameter, plots_dir,
  thres = "BestFits", best_fits_percent = 50,
  fileout_param_estim_summary = "", logspace = TRUE,
  scientific_notation = TRUE)

Arguments

model

the model name

filename

the filename containing the fits sequence

parameter

the name of the parameter to plot the density

plots_dir

the directory for storing the plots

thres

the threshold used to filter the dataset. Values: "BestFits", "CL66", "CL95", "CL99", "All".

best_fits_percent

the percent of best fits to analyse. Only used if thres="BestFits".

fileout_param_estim_summary

the name of the file containing the summary for the parameter estimation. Only used if thres!="BestFits".

logspace

true if the parameters should be plotted in logspace

scientific_notation

true if the axis labels should be plotted in scientific notation

Examples

# NOT RUN {
dir.create(file.path("pe_datasets"))
dir.create(file.path("pe_plots"))
data(insulin_receptor_all_fits)
write.table(insulin_receptor_all_fits, 
            file=file.path("pe_datasets", "all_fits.csv"), 
            row.names=FALSE)
# generate the global statistics for the parameter estimation
pe_ds_preproc(filename=file.path("pe_datasets", "all_fits.csv"), 
              param.names=c('k1', 'k2', 'k3'), 
              logspace=TRUE, 
              all.fits=TRUE, 
              data_point_num=33, 
              fileout_param_estim_summary=file.path("pe_datasets", "param_estim_summary.csv"))
parameter_density_analysis(model="ir_beta", 
                           filename=file.path("pe_datasets", "all_fits_log10.csv"), 
                           parameter="k1", 
                           plots_dir="pe_plots", 
                           thres="CL95",
                           fileout_param_estim_summary=file.path("pe_datasets", 
                                                                 "param_estim_summary.csv"),
                           logspace=TRUE)
                           
data(insulin_receptor_best_fits)
write.table(insulin_receptor_best_fits, 
            file=file.path("pe_datasets", "best_fits.csv"), 
            row.names=FALSE)
# generate the global statistics for the parameter estimation
pe_ds_preproc(filename=file.path("pe_datasets", "best_fits.csv"), 
              param.names=c('k1', 'k2', 'k3'), 
              logspace=TRUE, 
              all.fits=FALSE)
parameter_density_analysis(model="ir_beta", 
                           filename=file.path("pe_datasets", "best_fits_log10.csv"), 
                           parameter="k1", 
                           plots_dir="pe_plots", 
                           thres="BestFits",
                           best_fits_percent=50,
                           logspace=TRUE)
# }