Dataset is a superclass. All classess for specialized data inherit all these methods. When needed, they will override these basic methods with specialized ones (see description of the specific subclasses).
This document is an index of Dataset methods. They are losely grouped in general categories according to purpose and briefly annotated. For details on method algorithms and parameter lists see Dataset.m
Dataset(...) - Default constructor. Called by constructors of subclasses.
make_a_copy(...) - a copier of the Dataset object. NOTE: Dataset is a handle object therefore a special copier is provided.
set_name() - setting a new name for the Dataset object.
set_X(...) - Set X data
simulate_data(...) - Compute ideal Y values
simulate_noisy_data(...) - Produce new Y values perturbed with random noise
plot_simulation(...) - Plot model and simulated (noisy) data
trim_data(...) - remove unwanted tails
set_XSE(...) - Set standard errors of X
X_min_max() - Report min and max values of X variable
list_models() - Report on existing models for the object
set_active_model(...) - This method sets a new model to active and resets previous fitting results (because the numbers become meaningless). It also sets solver and its options for calculation of the model functions. The solver may by 'analytical' if model is represented as a closed-form expression or any of the numeric routines ('ode45', 'fmincon', etc.)---see the code of a particular model to find that out. A good practice is to include information about available solvers into model description when model is included with the specific data class.
show_active_model() - Report on active model
set_parameters(...) - Set values of parameters for viewing initial conditions and performing individual fitting
set_ranges_and_limits(...) - Set parameter ranges for Monte-Carlo and parameter limits for fitting
show_parameter_ranges_limits(...) - Return parameter values, confidence intervals, Monte-Carlo ranges and parameter limits as a formatted text
model(...) - Calculate values of the model function (active model of the dataset)
SS(...) - Calculate weighed sum of squares of residuals.
R_square(...) - calculate R-square (measure of goodness of fit) for current model parameters.
simple_fit(...) - Fit data without determination of uncertainties
fit(...) - Fitting with detemination of parameter uncertanties
show_fit_statistics(...) - Display fitting statistics
assign_best_fit_parameters(...) - Assign best-fit parameters determined by external algorithm (TotalFit).
set_XY_names(...) - Set X and Y axis names for plotting
set_XY_ranges(...) - Set X and Y ranges. Use empty array [] for automatic scaling.
plot_data(...) - Create a figure with experimental data
plot_model(...) - Create a smooth curve of a model function for given parameters
plot_all(...) - Plot both model with current parameters and the experimental data
plot_fit(...) - Plot data and the best-fit model curve (complains if fitting was not done)
set_ideal_XY(...) - Compute and set ideal Y values
generate_perturbed_XY(...) - Calculate and set perturbed X and Y data
restore_original_XY(...) - Restore original data
report_error(...) - Report error and abort execution
verify_simulation_mode(...) and verify_fitting_mode(...) - verify the dataset mode for mode-specific methods
show_model(...) - Display of one of the dataset's models
plot_parameter_correlations(...) - Plotting of parameter correlation maps (one parameter versus another) to reveal mutual correlations.
add_model(..) - Add a new model function: used in a subclass constructor.