DiscriminantPlots: Discriminant Plots

In lasandrall/SIDA: Sparse Integrative Discriminant Analysis for Multi-view Structured Data

Discriminant Plots

Description

Plots discriminant vectors for visualizing class separation

Usage

DiscriminantPlots(Xtestdata=Xtestdata,Ytest=Ytest,hatalpha=hatalpha)

Arguments

Xtestdata	A list with each entry containing views of size ntest \times p_d, where d =1,...,D. Rows are samples and columns are variables. Can use testing or training data.
Ytest	ntest \times 1 vector of class membership.
hatalpha	A list of estimated sparse discriminant vectors for each view.

Details

The function will return discriminant plots.

Value

NULL

References

Sandra E. Safo, Eun Jeong Min, and Lillian Haine (2019) , Sparse Linear Discriminant Analysis for Multi-view Structured Data, submitted

See Also

cvSIDA,sidatunerange, CorrelationPlots

Examples

library(SIDA)
##---- read in data
data(DataExample)

##---- call sida algorithm to estimate discriminant vectors, and predict on testing data

Xdata=DataExample[[1]]
Y=DataExample[[2]]
Xtestdata=DataExample[[3]]
Ytest=DataExample[[4]]


#call sidatunerange to get range of tuning paramater
ngrid=10
mytunerange=sidatunerange(Xdata,Y,ngrid,standardize=TRUE,weight=0.5,withCov=FALSE)

# an example with Tau set as the lower bound
Tau=c(mytunerange$Tauvec[[1]][1], mytunerange$Tauvec[[2]][1])

mysida=sida(Xdata,Y,Tau,withCov=FALSE,Xtestdata=Xtestdata,Ytest=Ytest,
            AssignClassMethod='Joint', plotIt=TRUE, standardize=TRUE,
            maxiteration=20,weight=0.5,thresh= 1e-03)

test.error=mysida$sidaerror

test.correlation=mysida$sidacorrelation

hatalpha=mysida$hatalpha

predictedClass=mysida$PredictedClass


##----plot discriminant and correlation plots
#---------Discriminant plot
mydisplot=DiscriminantPlots(Xtestdata,Ytest,mysida$hatalpha)

mycorrplot=CorrelationPlots(Xtestdata,Ytest,mysida$hatalpha)

lasandrall/SIDA documentation built on Oct. 19, 2022, 9:23 a.m.