Task 1 b completed

Easy/task1b.r

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+# Using binsegRcpp to compute binary segmentation models up to 5 segments
+# Plot segement means on top of data
+
+if(!require(binsegRcpp)) {
+    install.packages("binsegRcpp")
+}
+
+if(!require(neuroblastoma)) {
+    install.packages("neuroblastoma")
+}
+
+# import and load neuroblastoma dataset
+library('neuroblastoma')
+data('neuroblastoma')
+
+# using np as neuro.profiles for profile data
+np = neuroblastoma$profiles
+xy = np[np$profile.id == 4 & np$chromosome == 2, ]
+x = xy$position
+y = xy$logratio
+M = matrix(c(x,y), ncol=2)
+# computing binary segmentation models upto 5 segments using binsegRcpp
+model <- binsegRcpp::binseg_normal(y,6)
+
+# get changepoints and segment mean
+cm = coef(model,6:6)
+
+# create segment mean lines
+change.lines = x[as.vector(matrix(c(cm$start, cm$end), ncol=length(cm$start), nrow=2, byrow=TRUE))]
+segment.mean = as.vector(matrix(cm$mean, ncol=length(cm$mean), nrow=2, byrow=TRUE))
+
+# detected changepoints
+print(cm$end)
+
+# segment means
+print(cm$mean)
+
+# plot changepoint and segment mean over data
+svg('Easy/task1b.svg')
+plot(x,y,type='l', xaxt='n', xlab="Segments", ylab="logpoint ratios of the probe", main="Neuroblastoma changepoint detection using 'binsegRcpp'")
+axis(1, at=x[seq(1, 234, 50)], labels=seq(0, 200, 50))
+grid()
+legend(x=x[5], y=-0.5, legend=c("position vs logpoint data", "segment mean", "changepoint"), col=c("black","red","blue"), lty=c(1,1,NA), pch=c(NA,NA,'X'), cex=0.8, bg='lightblue')
+
+# add segment mean
+alter <- seq(1, 12, 2)
+segments(x0=change.lines[alter], y0=segment.mean[alter], x1=change.lines[alter+1], y1=segment.mean[alter+1], col='red')
+
+# add changepoints
+points(as.numeric(change.lines[alter+1])[1:5], as.numeric(segment.mean[alter])[1:5], col='blue', pch='X')
+
+dev.off()