inst/doc/rescaling_data.R

In rrscale: Robust Re-Scaling to Better Recover Latent Effects in Data

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set.seed(919)
u1 = rlnorm(10)
v1 = rlnorm(10)
Y1 = u1%*%t(v1)

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u2 = rlnorm(10)
v2 = rlnorm(10)
Y2 = .5+u2%*%t(v2)

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Y_nn = rbind(Y1,Y2)
Y = Y_nn + array(rlnorm(prod(dim(Y_nn)),0,.05),dim(Y_nn))

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library('reshape2')
library('ggplot2')
group = factor(rep(c(1,2),each=nrow(Y)/2))
levels(group) = c("group1","group2")
mY = melt(data.frame(Y,group),id.vars="group")
ggplot(data=mY,mapping=aes(x=value,fill=group))+geom_histogram(bins=100)+geom_vline(data=aggregate(value~group,data=mY,mean),mapping=aes(xintercept=value,linetype=group),size=1.5)

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t.test(rowMeans(Y)[group=="group1"],rowMeans(Y)[group=="group2"])

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library('rrscale')
scl = rrscale(Y)

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scl$T_name

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scl$par_hat

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trans_Y = scl$RR

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trans_Y2 = scl$rr_fn(Y)
all(trans_Y2==trans_Y,na.rm=TRUE)

## ---- out.width='100%', fig.asp=.5, dpi=300-----------------------------------
tmY = melt(data.frame(trans_Y,group),id.vars="group")
ggplot(data=tmY,mapping=aes(x=value,fill=group))+geom_histogram(bins=100)+geom_vline(data=aggregate(value~group,data=tmY,mean),mapping=aes(xintercept=value,linetype=group),size=1.5)

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t.test(rowMeans(trans_Y)[group=="group1"],rowMeans(trans_Y)[group=="group2"])

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plot(svdc(Y)$u[,1:2],col=group,xlab="PC1",ylab="PC2")

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plot(svdc(trans_Y)$u[,1:2],col=group,xlab="PC1",ylab="PC2")

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cancor(model.matrix(~1+group),svdc(Y)$u[,1:2])

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cancor(model.matrix(~1+group),svdc(trans_Y)$u[,1:2])