pe_ds_preproc: Parameter estimation pre-processing. It renames the data set...
In sbpiper: Data Analysis Functions for 'SBpipe' Package

Parameter estimation pre-processing. It renames the data set columns, and applies a log10 transformation if logspace is TRUE. If all.fits is true, it also computes the confidence levels.

1
2
3

pe_ds_preproc(filename, param.names = c(), logspace = TRUE,
  all.fits = FALSE, data_point_num = 0,
  fileout_param_estim_summary = "param_estim_summary.csv")

`filename`	the dataset filename containing the fits sequence
`param.names`	the list of estimated parameter names
`logspace`	true if the data set shoud be log10-transformed.
`all.fits`	true if filename contains all fits, false otherwise
`data_point_num`	the number of data points used for parameterise the model. Ignored if all.fits is false
`fileout_param_estim_summary`	the name of the file containing the summary for the parameter estimation. Ignored if all.fits is false

dir.create(file.path("pe_datasets"))
data(insulin_receptor_all_fits)
write.table(insulin_receptor_all_fits, 
            file=file.path("pe_datasets", "all_fits.csv"), 
            row.names=FALSE)
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"))
data(insulin_receptor_best_fits)
write.table(insulin_receptor_best_fits, 
            file=file.path("pe_datasets", "best_fits.csv"), 
            row.names=FALSE)
pe_ds_preproc(filename=file.path("pe_datasets", "best_fits.csv"), 
              param.names=c('k1', 'k2', 'k3'), 
              logspace=TRUE, 
              all.fits=FALSE)