pwPleInit

 0. Adjust default values specified in the configuration.
     config.analyses.PLE.*

 1. Load the desired model(s) and data-set(s) into PottersWheel and calibrate the
    parameters to best possible values. To get an overview about the parameters
    incorporated in the model, execute pwInfo at the command line. PLE will
    consider all fitted parameters. If necessary, use the command pwFixParameters
    to free or fix parameters. Use either integrator 14 (CVODES) with
    active Jacobian calculation for integration and optimization or
    integrator 1 (RADAU5) with the fast integration scheme (pwFastIntegration(true)).

 2. Initialise PLE using pwPleInit or open the PLE GUI using pwPLEgui.
    All computed results will be stored in the subfolder PLE-yyyymmddTHHMMSS.
    Continue within the GUI or apply the following commands.

 3. To estimate the profile likelihood for all free parameters run pwPleCalculate
    For a specific parameter run pwPleCalculate(i) where i refers to the number
    of the parameter as listed by pwInfo.

    Further arguments to pwPleCalculate(i, a, b, c, d, e) are:

 a) maximum number of steps in increasing and decreasing direction, default = 100
 b) aspired chi-square increase between two sampling steps,
    measured in percentage of delta-alpha, default = 0.1
 c) minimum step size in %, default = 0.01% of the parameter value
 d) maximum step size in %, default = 50%   of the parameter value
 e) flag to stop the estimation, if the sampled parameter is increased or
    decreased above or below its parameter bounds, default = true

 4. Execute pwPlePrint to get a summary of the results, including an automatically
    generated flag IDflag stating the type of identifiability. LowerPL and UpperPL
    indicate likelihood based confidence intervals sigma^{pm, PL}, LowerHes and UpperHes
    confidence intervals approximated by the inverse of the Hessian matrix sigma^{pm, Hes}
    and Rel.PL and Rel.Hes the relative size of the confidence intervals (sigma^+ - sigma^-)/(2�
    theta_i).

 5. Execute pwPlePlot(i) to generate a figure showing the profile likelihood Chi^2_{PL}
    PL(theta_i) and the corresponding changes in the other parameters.

 6. Execute pwPlePlotMulti to generate a figure showing an overview of all so far
    calculated profile likelihoods.

 7. Execute pwPlePlotRelations(js) where js is a vector of parameter indices, to
    generate a scatterplot of parameters to reveal functional relations. This is most
    convenient for two or three indices. One index generates a histogram and if js
    is omitted, a matrix plot is produced.

 8. Execute pwPleTrajectories(i) to plot trajectories corresponding to parameter
    values sampled for the profile likelihood Chi^2_{PL}(theta_i). This depicts model
    variability due to uncertainties in this parameter.