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| basic_theme | A generic basic theme for time courses. It extends ggplot2 theme_classic(). |
| check_exp_dataset | Check that the experimental data set exists. |
| combine_param_best_fits_stats | Combine the parameter best fits statistics. |
| combine_param_ple_stats | Combine the parameter PLE statistics. |
| compute_aic | Compute the Akaike Information Criterion. Assuming additive Gaussian measurement noise of width 1, the term -2ln(L(theta|y)) ~ SSR ~ Chi^2 |
| compute_aicc | Compute the corrected Akaike Information Criterion. Assuming additive Gaussian measurement noise of width 1, the term -2ln(L(theta|y)) ~ SSR ~ Chi^2 |
| compute_bic | Compute the Bayesian Information Criterion. Assuming additive Gaussian measurement noise of width 1, the term -2ln(L(theta|y)) ~ SSR ~ Chi^2 |
| compute_cl_objval | Compute the confidence level based on the minimum objective value. |
| compute_fratio_threshold | Compute the fratio threshold for the confidence level. |
| compute_sampled_ple_stats | Compute the table for the sampled PLE statistics. |
| gen_stats_table | Generate a table of statistics for each model readout. |
| get_param_names | Get parameter names |
| get_sorted_level_indexes | Return the indexes of the files as sorted by levels. |
| histogramplot | Plot a generic histogram |
| insulin_receptor_1 | A stochastic model simulation |
| insulin_receptor_2 | A stochastic model simulation |
| insulin_receptor_3 | A stochastic model simulation |
| insulin_receptor_all_fits | A parameter estimation data set including all the evaluated fits. |
| insulin_receptor_best_fits | A parameter estimation data set including only the best evaluated fits. |
| insulin_receptor_exp_dataset | Experimental data set for the insulin receptor beta phosphorylated at pY1146 as published in Dalle Pezze et al. Science Signaling 2012. |
| insulin_receptor_IR_beta_pY1146 | A stochastic simulation data set for the insulin receptor beta phosphorylated at pY1146. |
| insulin_receptor_ps1_l0 | A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 0. |
| insulin_receptor_ps1_l1 | A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 1. |
| insulin_receptor_ps1_l11 | A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 11. |
| insulin_receptor_ps1_l13 | A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 13. |
| insulin_receptor_ps1_l14 | A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 14. |
| insulin_receptor_ps1_l16 | A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 16. |
| insulin_receptor_ps1_l3 | A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 3. |
| insulin_receptor_ps1_l4 | A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 4. |
| insulin_receptor_ps1_l6 | A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 6. |
| insulin_receptor_ps1_l8 | A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 8. |
| insulin_receptor_ps1_l9 | A deterministic simulation of the insulin receptor model upon scanning of 1 model parameter. The initial amount of IR-beta is approx 9. |
| insulin_receptor_ps2_tp2 | A deterministic simulation of the insulin receptor model upon scanning of 2 model parameters. |
| kurtosis | Calculate the kurtosis of a numeric vector |
| leftCI | Return the left value of the parameter confidence interval. The provided dataset has two columns: ObjVal | ParamValue |
| load_exp_dataset | Load the experimental data set. |
| normalise_vec | Normalise a vector within 0 and 1 |
| objval.col | The name of the Objective Value column |
| objval_vs_iters_analysis | Analysis of the Objective values vs Iterations. |
| parameter_density_analysis | Parameter density analysis. |
| parameter_pca_analysis | PCA for the parameters. These plots rely on factoextra fviz functions. |
| pca_theme | A generic basic theme for pca. It extends ggplot2 theme_classic(). |
| pe_ds_preproc | 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. |
| plot_combined_tc | Plot repeated time courses in the same plot with mean, 1 standard deviation, and 95% confidence intervals. |
| plot_comb_sims | Plot the simulation time courses using a heatmap representation. |
| plot_double_param_scan_data | Plot model double parameter scan time courses. |
| plot_fits | Plot the number of iterations vs objective values in log10 scale. |
| plot_heatmap_tc | Plot time courses organised as data frame columns with a heatmap. |
| plot_objval_vs_iters | Plot the Objective values vs Iterations |
| plot_parameter_density | Plot parameter density. |
| plot_raw_dataset | Add experimental data points to a plot. The length of the experimental time course to plot is limited by the length of the simulated time course (=max_sim_tp). |
| plot_repeated_tc | Plot repeated time courses in the same plot separately. First column is Time. |
| plot_sampled_2d_ple | Plot 2D profile likelihood estimations. |
| plot_sampled_ple | Plot the sampled profile likelihood estimations (PLE). The table is made of two columns: ObjVal | Parameter |
| plot_sep_sims | Plot the simulations time course separately. |
| plot_single_param_scan_data | Plot model single parameter scan time courses |
| plot_single_param_scan_data_homogen | Plot model single parameter scan time courses using homogeneous lines. |
| replace_colnames | Rename data frame columns. 'ObjectiveValue' is renamed as 'ObjVal'. Substrings 'Values.' and '..InitialValue' are removed. |
| rightCI | Return the right value of the parameter confidence interval. The provided dataset has two columns: ObjVal | ParamValue |
| sampled_2d_ple_analysis | 2D profile likelihood estimation analysis. |
| sampled_ple_analysis | Run the profile likelihood estimation analysis. |
| sbpiper_pe | Main R function for SBpipe pipeline: parameter_estimation(). |
| sbpiper_ps1 | Main R function for SBpipe pipeline: parameter_scan1(). |
| sbpiper_ps2 | Main R function for SBpipe pipeline: parameter_scan2(). |
| sbpiper_sim | Main R function for SBpipe pipeline: simulate(). |
| scatterplot | Plot a generic scatter plot |
| scatterplot_log10 | Plot a generic scatter plot in log10 scale |
| scatterplot_ple | Plot a profile likelihood estimation (PLE) scatter plot |
| scatterplot_w_colour | Plot a scatter plot using a coloured palette |
| skewness | Calculate the skewness of a numeric vector |
| summarise_data | Summarise the model simulation repeats in a single file. |
| tc_theme | A theme for time courses. It extends ggplot2 theme_classic(). |