Description Details Author(s) Examples
We developed a robust missing value imputation approach for gene expression and metabolomics data analysis using minimum beta-divergence method. This approach capable of handling both missing values and outliers, simultaneously.
Package: | rMisbeta |
Type: | Package |
Version: | 1.0 |
Date: | 2020-10-03 |
License: | GPL (>=2.0) |
Package rMisbeta has the six following functions:
Sim2Group(): | This function generates the data from the one way ANOVA model for two groups. |
OutMisDat(): | This function returns the outliers and missing value incorporated data. |
CalcMeanVar(): | This function calculates the robust mean and variance from the the matrix in presence |
of outliers and missing values for function RobMeanVar(). | |
RobMeanVar(): | This function calculates the robust mean and variance from the the matrix in presence |
of outliers and missing values. The function RobMeanVar() also produces a | |
weight called beta-weights for each of the values to detect the outliers in the dataset. | |
remat(): | This function returns reconstructed data matrix by modifying the outliers and missing value |
using beta divergence method. | |
performance.eval(): | This is the performance evaluation function. Which calculates TPR,TNR,FPR,PNR,AUC |
etc. as a measure of performance index. | |
Md Shahjaman and Md. Nurul Haque Mollah; Maintainer: Md Shahjaman, shahjaman_brur@yahoo.com
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 | nG=1000
n1=n2=5
pde=0.1
Simdat=Sim2Group(nG,n1,n2,var0=0.1,pde=0.1)
xx=Simdat$outmat
TrueDE=Simdat$DEtrue
MisOutdat<-OutMisDat(xx,pctOut=0.1,pctMis=0.1)
misdat_zero<-MisOutdat
misdat_zero[is.na(misdat_zero)]<-0
cl=rep(c(1,2),each=n1)
res=remat(MisOutdat,cl)
up_mat<-res$remat
pTtest_zero<-pTtest_beta<-NULL
for (j1 in 1:dim(xx)[1])
{
DataYY <- data.frame(YY =misdat_zero[j1,], FactorLevels = factor(cl))
DataYY2 <- data.frame(YY2=up_mat[j1,], FactorLevels2 = factor(cl))
pTtest_zero[j1] <- t.test(YY~FactorLevels,data=DataYY, paired=FALSE)[[3]]
pTtest_beta[j1] <- t.test(YY2~FactorLevels2,data=DataYY2, paired=FALSE)[[3]]
}
TopDEn<-seq(nG*pde/10, pde*nG, length=10)
performance_zero<-performance.eval(pTtest_zero,TrueDE,TopDEn,decreasing=FALSE);
performance_beta<-performance.eval(pTtest_beta,TrueDE,TopDEn,decreasing=FALSE);
plot(performance_zero$FPR,performance_zero$TPR,type="o",
xlab="False Positive Rate",ylab="True Positive Rate",ylim=c(0,1))
points(performance_beta$FPR,performance_beta$TPR,type="o",col=2)
legend("bottomright", c('t_test_zero','t_test_rMisbeta'),lwd=1,cex=0.8,col=c(1,2))
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