MethodOpt {MethodOpt}R Documentation

Method optimization GUI

Description

'MethodOpt()' runs the GUI.

Usage

MethodOpt()

Details

No arguments are needed to initiate the GUI. 'MethodOpt()' is split into three main tabs.

Value

No return value, opens and runs the GUI

Fractional factorial design

The first step in method optimization is to build an experimental design. Hence, the first tab of the GUI is dedicated to designing a fractional factorial experimental design. Parameters are input with their corresponding low and high values. Pressing "Generate FFD" will yield the experimental design. The user will run experiments according to each method.

ANOVA

The second step in method optimization is to run an ANOVA test. This is carried out under the "Analysis" tab. Raw experimental screening data is uploaded, and the spectra can be viewed. In subtabs, the spectra peaks must be identified (either by an uploaded retention time file or by a built-in identification algorithm), objectives must be selected, and the initial experimental design must be uploaded; then the ANOVA test may be run. Statistically significant parameters are indicated.

Box-Behnken design

The third step is to generate a three-level Box-Behnken experimental design with the significant parameters. Low, middle, and high values are input with their corresponding parameters. The design can be generated by pressing "Generate BBD."

Optimization

The final step is to run the optimization with the results of the Box-Behnken design. Similarly to the second step, raw data is uploaded in the "Analysis" tab. Spectra peaks must be identified, objectives must be selected, and the experimental design must be uploaded; then the optimal values can be calculated.

Examples

# Please see the vignette for the MethodOpt package for a full example of how
# to use the GUI launched by MethodOpt::MethodOpt().


[Package MethodOpt version 1.0.0 Index]