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diff --git a/vignettes/gmkin_manual.md b/vignettes/gmkin_manual.md deleted file mode 100644 index bab6770..0000000 --- a/vignettes/gmkin_manual.md +++ /dev/null @@ -1,408 +0,0 @@ ---- -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. - - - -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. - - - -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. - - - -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. - - - -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. - - - -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. - - - -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. - - - -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. - - - -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. - - - -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. - - - -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. - - - -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. - - - -### 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. - - - -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. - - - -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. - - - -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. - - - -<!-- vim: set foldmethod=syntax ts=2 sw=2 expandtab: --> |