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----
-title: "Manual for gmkin"
-output:
-  html_document:
-    css: gmkin_manual.css
-    toc: true
-    theme: united
----
-<!--
-%\VignetteEngine{knitr::rmarkdown}
-%\VignetteIndexEntry{Manual for gmkin}
--->
-
-
-## Introduction
-
-The R add-on package gmkin provides a browser based graphical interface for
-performing kinetic evaluations of degradation data using the mkin package.
-While the use of gmkin should be largely self-explanatory, this manual may serve
-as a functionality overview and reference.
-
-For system requirements and installation instructions, please refer to the 
-[gmkin homepage](http://kinfit.r-forge.r-project.org/gmkin_static)
-
-## Starting gmkin
-
-As gmkin is an R package, you need to start R and load the gmkin package before you can run gmkin. 
-This can be achieved by entering the command
-
-
-```r
-library(gmkin)
-```
-
-into the R console. This will also load the packages that gmkin depends on,
-most notably gWidgetsWWW2 and mkin.  Loading the package only has to be done
-once after you have started R. 
-
-Before you start gmkin, you should make sure that R is using the working
-directory that you would like to keep your gmkin project file(s) in. If you use
-the standard R application on windows, you can change the working directory
-from the File menu.
-
-Once you are sure that the working directory is what you want it to be, gmkin
-can be started by entering the R command
-
-
-```r
-gmkin()
-```
-
-This will cause the default browser to start up or, if it is already running, to
-pop up and open a new tab for displaying the gmkin user interface.
-
-In the R console, you should see some messages, telling you if the local R help
-server, which also serves the gmkin application, has been started, which port it is
-using and that it is starting an app called gmkin.
-
-Finally, it should give a message like
-
-
-```r
-Model cost at call 1: 2388.077
-```
-
-which means that the first kinetic evaluation has been configured for fitting.
-
-In the browser, you should see something like the screenshot below.
-
-![gmkin start](img/gmkin_start.png)
-
-The statusbar at the bottom of the gmkin window shows, among others, the
-working directory that gmkin uses.
-
-Note that the project file management area described below can be minimized by clicking on 
-the arrows on the right hand side of its title bar. This may be helpful if the vertical
-size of your browser window is restricted.
-
-## Project file management
-
-At startup, gmkin loads a project called "FOCUS\_2006\_gmkin" which is distributed
-with the gmkin package. A gmkin project contains datasets, kinetic models for
-fitting, and so-called fits, i.e. the results of fitting models to data. These
-gmkin projects can be saved and restored using the project file management area in the
-top left.
-
-![projects](img/projects.png)
-
-If you would like to save these items for reference or for the purpose of continuing 
-your work at a later time, you can modify the project name and press the button below it.
-The full name of the project file created and the working directory will be displayed 
-in the gmkin status bar.
-
-For restoring a previously saved project file, use the Browse button to locate
-it, and the "Upload" button to load its contents into gmkin.
-
-## Studies
-
-The "Studies" area directly below the "Project file management" area can be expanded by clicking
-on the arrows on the right hand side of its title bar. Studies in gmkin are
-simply a numbered list of sources for the datasets in a project. You can edit the titles 
-directly by clicking on them. If you would like to add a new data source, use the "Add"
-button above the table containing the list. If there are more than one studies in the list,
-you can also remove them using the "Remove" button.
-
-![studies](img/studies.png)
-
-Note that the user is responsible to keep the study list consistent with the numbers that are 
-used in the list of datasets described below.
-
-## Datasets and Models
-The project loaded at the start of gmkin contains two datasets and four kinetic models. These 
-are listed to the left under the heading "Datasets and Models", together with a button for 
-setting up fits as shown below.
-
-![datasets and models](img/datasetsnmodels.png)
-
-For editing, adding or removing datasets or models, you need to double-click on an
-entry in the respective list. 
-
-For setting up a fit of a specific model to a specific dataset, the model and
-the dataset should be selected by clicking on them. If they are compatible, clicking
-the button "Configure fit for selected dataset and model" will set up the fit and 
-open the "Plotting and Fitting" tab to the right.
-
-## Dataset editor
-
-The dataset editor allows for editing datasets, entering new datasets, uploading
-data from text files and deleting datasets.
-
-![dataset editor](img/dataseteditor.png)
-
-If you want to create (enter or load) a new dataset, it is wise to first edit
-the list of data sources in the "Studies" area as described above.
-
-### Entering data directly
-
-For entering new data manually, click on "New dataset", enter a title and select
-the study from which the dataset is taken. At this stage, you may already want
-to press "Keep changes", so the dataset appears in the list of datasets.
-
-In order to generate a table suitable for entering the data, enter a comma separated
-list of sampling times, optionally the time unit, and the number of replicate measurements
-at each sampling time. Also, add a comma separated list of short names of the
-relevant compounds in your dataset. A unit can be specified for the observed 
-values. An example of filling out the respective fields is shown below.
-
-![generate data grid](img/generatedatagrid.png)
-
-Once everyting is filled out to your satisfaction, press the button "Generate empty grid
-for kinetic data". In our example, this would result in the data grid shown below. You 
-can enter the observed data into the value column, as shown in the screenshot below.
-
-![data grid](img/datagrid.png)
-
-The column with title override serves to override data points from the original
-datasets, without loosing the information which value was originally reported.
-
-If everything is OK, press "Keep changes" to save the dataset in the current
-workspace. Note that you need to save the project file (see above) in order to
-be able to use the dataset that you created in a future gmkin session.
-
-### Importing data from text files
-
-In case you want to work with a larger dataset that is already available as a computer 
-file e.g. in a spreadsheet application, you can export these data as a tab separated
-or comma separated text file and import it using the "Browse" and "Upload" buttons in the
-dataset editor.
-
-As an example, we can create a text file from one of the datasets shipped with
-the mkin package using the following R command:
-
-
-```r
-write.table(schaefer07_complex_case, sep = ",", dec = ".", 
-            row.names = FALSE, quote = FALSE, 
-            file = "schaefer07.csv")
-```
-
-This produces a text file with comma separated values in the current working directory of R. 
-
-Loading this text file into gmkin using the "Browse" and "Upload" buttons results in 
-an import configuration area like this, with the uploaded text file displayed to the left,
-and the import options to the right.
-
-![upload area](img/uploadarea.png)
-
-In the import configuration area, the following options can be specified. In the field
-"Comment lines", the number of lines in the beginning of the file that should be ignored
-can be specified.
-
-The checkbox on the next line should be checked if the first line of the file contains
-the column names, i.e. the names of the observed variables when the data are in wide format.
-
-As "Separator", whitespace, semicolon or comma can be chosen. If whitespace is selected,
-files in which the values are separated by a fixed or varying number of whitespace characters
-should be read in correctly. As the tabulator counts as a whitespace character, this is 
-also the option to choose for tabulator separated values.
-
-As the "Decimal" separator, comma "," or period "." can be selected.
-
-In the next line, it can be specified if the data are in wide or in long format.
-If in wide format, the only option left to specify is the title of the column containing
-the sampling times. If the data is in long format, the column headings specifying the 
-columns containing the observed variables (default is "name"), the sampling times
-(default is "time"), the observed values (default is "value") and, if present in the data,
-the relative errors (default is "err") can be adapted. The default settings appearing if
-the long format is selected are shown below.
-
-![long](img/long.png)
-
-In our example we have data in the wide format, and after adapting the
-"Separator" to a comma, we can press the button "Import using options specified
-below", and the data should be imported.  If successful, the data editor should
-show the sampling times and the names of the observed variables, as well as the
-imported data in a grid for further editing or specifying overrides.
-
-After editing the title of the dataset and selecting the correct study as 
-the source of the data, the dataset editor should look like shown below.
-
-![successful upload](img/successfulupload.png)
-
-If everything is OK, press "Keep changes" to save the dataset in the current
-workspace. Again, you need to save the project file in order to be able to use
-the dataset that you created in a future gmkin session.
-
-## Model editor
-
-The following screenshot shows the model editor for the model number 4 in 
-the list of models that are in the initial workspace.
-
-![model editor](img/modeleditor.png)
-
-In the first line the name of the model can be edited. You can also specify "min" or
-"max" for minimum or maximum use of formation fractions. Maximum use of formation
-fractions means that the differential equations in the degradation model are formulated
-using formation fractions. When you specify "min", then formation fractions are only used
-for the parent compound when you use the FOMC, DFOP or the HS model for it.
-
-Pressing "Copy model" keeps the model name, so you should change it for the newly generated copy.
-Pressing "Add observed variable" adds a line in the array of state variable specifications below.
-The observed variables to be added are usually transformation products (usually termed metabolites),
-but can also be the parent compound in a different compartment (e.g. "parent\_sediment").
-
-Only observed variable names that occur in previously defined datasets can be selected. For any observed 
-variable other than the first one, only the SFO or the SFORB model can be selected. For each 
-observed variables, a comma separated list of target variables can be specified. In addition, a pathway
-to the sink compartment can be selected. If too many observed variables have been added, complete lines
-can be removed from the model definition by pressing the button "Remove observed variable".
-
-If the model definition is supposedly correct, press "Keep changes" to make it possible to select
-it for fitting in the listing of models to the left.
-
-## Plotting and fitting
-
-If the dataset(s) to be used in a project are created, and suitable kinetic models have been defined,
-kinetic evaluations can be configured by selecting one dataset and one model in the lists to the left,
-and the pressing the button "Configure fit for selected dataset and model" below these lists.
-
-This opens the "Plotting and fitting" tab area to the right, consisting of a graphical window
-showing the data points in the selected dataset and the model, evaluated with the initial parameters
-defined by calling `mkinfit` without defining starting parameters. The value of the objective function
-to be minimized for these default parameters can be seen in the R console, e.g. as
-
-
-```r
-Model cost at call 1: 15156.12
-```
-
-for the example shown below, where the FOCUS example dataset D and the model SFO\_SFO were selected.
-
-![plotting and fitting](img/plottingnfitting.png)
-
-### Parameters
-
-In the right hand area, initially the tab with the parameter list is displayed. While 
-name and type of the parameters should not be edited, their initial values can be edited
-by clicking on a row. Also, it can be specified if the parameters should be fixed
-in the optimisation process. 
-
-If the initial values for the parameters were changed, the resulting model solution
-can be visually checked by pressing the button "Show initial". This will update the 
-plot of the model and the data using the specified initial parameter values.
-
-If a similar model with a partially overlapping model definition has already be fitted, 
-initial values for parameters with the same name in both models can also be retrieved
-from previous fits by selecting the fit and pressing the button "Get initials
-from". This facilitates stepwise fitting of more complex degradation pathways.
-
-After the model has been successfully fitted by pressing the "Run" button, the optimised
-parameter values are added to the parameter table.
-
-### Fit options
-
-The most important fit options of the `mkinfit` function can be set via the 
-"Fit option" tab shown below. If the "plot" checkbox is checked, an R graphics device
-started via the R console shows the fitting progress, i.e. the change of the model
-solution together with the data during the optimisation.
-
-![fit options](img/fitoptions.png)
-
-The "solution\_type" can either be "auto", which means that the most effective solution
-method is chosen for the model, in the order of "analytical" (for parent only degradation
-data), "eigen" (for differential equation models with only linear terms, i.e. without 
-FOMC, DFOP or HS submodels) or "deSolve", which can handle all model definitions generated
-by the `mkin` package.
-
-The parameters "atol" and "rtol" are only effective if the solution type is "deSolve". They
-control the precision of the iterative numerical solution of the differential equation model.
-
-The checkboxes "transform\_rates" and "transform\_fractions" control if the parameters are fitted
-as defined in the model, or if they are internally transformed during the fitting process in 
-order to improve the estimation of standard errors and confidence intervals which are based 
-on a linear approximation at the optimum found by the fitting algorithm.
-
-If fitting with transformed fractions leads to a suboptimal fit, doing a first
-run without transforming fractions may help. A final run using the optimised
-parameters from the previous run as starting values (see comment on "Get
-initials from" above) can then be performed with transformed fractions.
-
-The dropdown box "weight" specifies if and how the observed values should be weighted 
-in the fitting process. If "manual" is chosen, the values in the "err" column of the 
-dataset are used, which are set to unity by default. Setting these to higher values 
-gives lower weight and vice versa. If "none" is chosen, observed
-values are not weighted. Please refer to the documentation of the `modFit` function from
-the `FME` package for the meaning of options "std" and "mean".
-
-The options "reweight.method", "reweight.tol" and "reweight.max.iter" enable the use of
-iteratively reweighted least squares fitting, if the reweighting method is set to "obs". Please
-refer to the `mkinfit` [documentation](http://kinfit.r-forge.r-project.org/mkin_static/mkinfit.html) 
-for more details.
-
-The drop down box "method.modFit" makes it possible to choose between the optimisation
-algorithms "Port" (the default in mkin versions > 0.9-33, a local optimisation
-algorithm using a model/trust region approach), "Marq" (the former default in
-mkin, a Levenberg-Marquardt variant from the R package `minpack.lm`), 
-and "SANN" (the simulated annealing method - robust but inefficient and without
-a convergence criterion).
-
-Finally, the maximum number of iterations for the optimisation can be adapted using the
-"maxit.modFit" field.
-
-### Fitting the model
-
-In many cases the starting parameters and the fit options do not need to be modified
-and the model fitting process can simply be started by pressing the "Run" button.
-In the R console, the progressive reduction in the model cost can be monitored and will 
-be displayed like this:
-
-
-```r
-Model cost at call  1 :  15156.12 
-Model cost at call  3 :  15156.12 
-Model cost at call  7 :  14220.79 
-Model cost at call  8 :  14220.79 
-Model cost at call  11 :  14220.79 
-Model cost at call  12 :  3349.268 
-Model cost at call  15 :  3349.268 
-Model cost at call  17 :  788.6367 
-Model cost at call  18 :  788.6366 
-Model cost at call  22 :  374.0575 
-Model cost at call  23 :  374.0575 
-Model cost at call  27 :  371.2135 
-Model cost at call  28 :  371.2135 
-Model cost at call  32 :  371.2134 
-Model cost at call  36 :  371.2134 
-Model cost at call  37 :  371.2134 
-```
-
-If plotting of the fitting progress was selecte in the "Fit options" tab, a
-new separate graphics window should either pop up, or a graphics window previously
-started for this purpose will be reused.
-
-### Summary
-
-Once a fit has successfully been performed by pressing the "Run" button, the summary
-as displayed below can be accessed via the "Summary" tab.
-
-![summary](img/summary.png)
-
-The complete summary can be saved into a text file by specifying a suitable file name
-and pressing the button "Save summary".
-
-### Plot options
-
-In the tab "Plot options", the file format can be chosen, the legend can be
-turned off, and the observed variables for which the data and the model fit
-should be plotted can be selected as shown below.
-
-![plot options](img/plotoptions.png)
-
-On systems running the Windows operating system, the windows metafile (wmf) format
-can be additionally chosen. Chaning the file format for plotting will also change 
-the extension of the proposed filename for saving the plot.
-
-### Confidence interval plots
-
-Whenever a new fit has been configured or a run of a fit has been completed, the plotting
-area is updated with the abovementioned plot of the data and the current model solution.
-
-In addition, a confidence interval plot is shown below this conventional plot. In case
-a fit has been run and confidence intervals were successfully calculated for the fit (i.e. 
-if the model was not overparameterised and no other problems occurred), the 
-confidence intervals are graphically displayed as bars as shown below.
-
-![confidence](img/confidence.png)
-
-<!-- vim: set foldmethod=syntax ts=2 sw=2 expandtab: -->