sbpiper (version 1.9.0)

sbpiper_pe: Main R function for SBpipe pipeline: parameter_estimation().

Description

Main R function for SBpipe pipeline: parameter_estimation().

Usage

sbpiper_pe(model, finalfits_filenamein, allfits_filenamein, plots_dir,
  data_point_num,
  fileout_param_estim_best_fits_details = "param_estim_best_fits_details.csv",
  fileout_param_estim_details = "param_estim_details.csv",
  fileout_param_estim_summary = "param_estim_summary.csv",
  best_fits_percent = 50, plot_2d_66cl_corr = TRUE,
  plot_2d_95cl_corr = TRUE, plot_2d_99cl_corr = TRUE, logspace = TRUE,
  scientific_notation = TRUE)

Arguments

model

the name of the model

finalfits_filenamein

the dataset containing the best parameter fits

allfits_filenamein

the dataset containing all the parameter fits

plots_dir

the directory to save the generated plots.

data_point_num

the number of data points used for parameterise the model.

fileout_param_estim_best_fits_details

the name of the file for the statistics of the parameters best fits.

fileout_param_estim_details

the name of the file containing the detailed statistics for the estimated parameters.

fileout_param_estim_summary

the name of the file containing the summary for the parameter estimation.

best_fits_percent

the percent of best fits to analyse.

plot_2d_66cl_corr

true if the 2D parameter correlation plots for 66% confidence intervals should be plotted.

plot_2d_95cl_corr

true if the 2D parameter correlation plots for 95% confidence intervals should be plotted.

plot_2d_99cl_corr

true if the 2D parameter correlation plots for 99% confidence intervals should be plotted.

logspace

true if parameters should be plotted in logspace.

scientific_notation

true if 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_best_fits)
write.table(insulin_receptor_best_fits, 
            file=file.path("pe_datasets", "best_fits.csv"), 
            row.names=FALSE)
data(insulin_receptor_all_fits)
write.table(insulin_receptor_all_fits, 
            file=file.path("pe_datasets", "all_fits.csv"), 
            row.names=FALSE)
sbpiper_pe(model="ir_beta", 
           finalfits_filenamein=file.path("pe_datasets", "best_fits.csv"), 
           allfits_filenamein=file.path("pe_datasets", "all_fits.csv"), 
           plots_dir="pe_plots", 
           data_point_num=33, 
           fileout_param_estim_best_fits_details=file.path("pe_datasets", 
                                                 "param_estim_best_fits_details.csv"), 
           fileout_param_estim_details=file.path("pe_datasets", 
                                                 "param_estim_details.csv"), 
           fileout_param_estim_summary=file.path("pe_datasets", 
                                                 "param_estim_summary.csv"), 
           best_fits_percent=50, 
           plot_2d_66cl_corr=TRUE, 
           plot_2d_95cl_corr=TRUE, 
           plot_2d_99cl_corr=TRUE, 
           logspace=TRUE, 
           scientific_notation=TRUE)
# }