ASDABarPlot {accSDA}

barplot for ASDA objects

Description

This is a function to visualize the discriminant vector from the ASDA method. The plot is constructed as a ggplot barplot and the main purpose of it is to visually inspect the sparsity of the discriminant vectors. The main things to look for are how many parameters are non-zero and if there is any structure in the ones that are non-zero, but the structure is dependent on the order you specify your variables. For time-series data, this could mean that a chunk of variables are non-zero that are close in time, meaning that there is some particular event that is best for discriminating between the classes that you have.

Usage

ASDABarPlot(asdaObj, numDVs = 1, xlabel, ylabel, getList = FALSE, main, ...)

Arguments

asdaObj

Object from the ASDA function.

numDVs

Number of discriminant vectors (DVs) to plot. This is limited by the number of DVs outputted from the ASDA function or k-1 DVs where k is the number of classes. The first 1 to numDVs are plotted.

xlabel

Label to put under every plot

ylabel

Vector of y-axis labels for each plot, e.g. if there are three DVs, then ylab = c('Discriminant Vector 1', 'Discriminant Vector 2', 'Discriminant Vector 3') is a valid option.

getList

Logical value indicating whether the output should be a list of the plots or the plots stacked in one plot using the gridExtra package. By default the function produces a single plot combining all plots of the DVs.

main

Main title for the plots, this is not used if getList is set to TRUE.

...

Extra arguments to grid.arrange.

Value

barplot.ASDA returns either a single combined plot or a list of individual ggplot objects.

Note

This function is used as a quick diagnostics tool for the output from the ASDA function. Feel free to look at the code to customize the plots in any way you like.

See Also

ASDA

Examples

    # Generate and ASDA object with your data, e.g.
    # Prepare training and test set
    # This is a very small data set, I advise you to try it on something with more
    # variables, e.g. something from this source: http://www.cs.ucr.edu/~eamonn/time_series_data/
    # or possibly run this on the Gaussian data example from the ASDA function
    train <- c(1:40,51:90,101:140)
    Xtrain <- iris[train,1:4]
    nX <- normalize(Xtrain)
    Xtrain <- nX$Xc
    Ytrain <- iris[train,5]
    Xtest <- iris[-train,1:4]
    Xtest <- normalizetest(Xtest,nX)
    Ytest <- iris[-train,5]
    # Run the method
    resIris <- ASDA(Xtrain,Ytrain)

    # Look at the barplots of the DVs
    ASDABarPlot(resIris)