Description Usage Arguments Examples
Main R function for SBpipe pipeline: parameter_estimation().
1 2 3 4 5 6 7 8 | 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)
|
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. |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | 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)
|
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