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<h1 class="documentFirstHeading">Lab9.R</h1>
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<p>###################################################
### chunk number 1: loadpacks
###################################################
library(wavethresh)
library(Rwave)
library(ts)
library(Milan)</p>
<p>###################################################
### chunk number 2: plotwavelets
###################################################
op = par(mfrow=c(5,2), oma = c(0,0, 4, 0),
mgp = c(1.2, .8, 0), mar = .1 + c(4,4, .5,1))
for(fn in 1:10) {
draw.default(filter.number= fn, col = "blue", main = NULL, xlab= "")
abline(h=0,v=0, lty=3, lwd=.5, col = "gray")
}
mtext(paste("draw.default(*, family = <code>",formals(draw.default)$family[[2]],"</code>)"),
side = 3, line = 1, outer = TRUE,
cex = par("cex.main"), font = par("font.main"))
par(op)</p>
<p>###################################################
### chunk number 3: Example1
###################################################
test.data1=example.1()$y
ts.plot(test.data1,ylab="y")
title("A function with a jump")</p>
<p>###################################################
### chunk number 4: DWT1
###################################################
wds1 <- wd(test.data1)
plot(wds1)</p>
<p>###################################################
### chunk number 5: tree1
###################################################
par(mfrow=c(4,2))
plot.ts(accessC(wds1, level=8))
plot.ts(accessD(wds1, level=8))
plot.ts(accessC(wds1, level=7))
plot.ts(accessD(wds1, level=7))
plot.ts(accessC(wds1, level=6))
plot.ts(accessD(wds1, level=6))
plot.ts(accessC(wds1, level=5))
plot.ts(accessD(wds1, level=5))</p>
<p>###################################################
### chunk number 6: Example2
###################################################
par(mfrow=c(1,1))
test.data2<-achirp(512)$y
ts.plot(test.data2,ylab="y")
title("A linear chirp")</p>
<p>###################################################
### chunk number 7: DWT2
###################################################
wds2 <- wd(test.data2)
plot(wds2)</p>
<p>###################################################
### chunk number 8: tree2
###################################################
par(mfrow=c(4,2))
plot.ts(accessC(wds2, level=8))
plot.ts(accessD(wds2, level=8))
plot.ts(accessC(wds2, level=7))
plot.ts(accessD(wds2, level=7))
plot.ts(accessC(wds2, level=6))
plot.ts(accessD(wds2, level=6))
plot.ts(accessC(wds2, level=5))
plot.ts(accessD(wds2, level=5))</p>
<p>###################################################
### chunk number 9: CWT
###################################################
par(mfrow=c(3,1))
plot.ts(test.data2)
title("A chirp")
cwtchirp<-cwt(test.data2,5,12)
image(Arg(cwtchirp))</p>
<p>###################################################
### chunk number 10: Approx1
###################################################
levels<-3:(wds1$nlevels - 1)
nthresh <- length(levels)
d <- NULL
dz <- 0
for (i in 1:nthresh) {
d <- accessD(wds1, level = levels<a href="#refi">[i]</a>)
d[abs(d) <= 0.06] = 0
dz=dz+sum(d==0)
cat("Level: ", levels<a href="Lab9.R#refi">[i]</a>, " there are ", sum(d == 0), " zeroes\n")
wds2 <- putD(wds1, level = levels<a href="#refi">[i]</a>, v = d)
}
cat("there are in total", dz, " zeroes\n")</p>
<p>###################################################
### chunk number 11: Approx2
###################################################
par(mfrow=c(2,1))
ts.plot(wr(wds2))
# look at the difference
ts.plot(test.data1-wr(wds2))</p>
<p>###################################################
### chunk number 12: Denoise1
###################################################
par(mfcol = c(3, 2), mar = c(2, 2, 2, 1), mgp = c(5, 0.4, 0))<p> x <- (1:128)/128
f1 <- 2 <em> sin(2 </em> pi * x)
f2 <- c(rep(0, 32), rep(4, 64), rep(0, 32))
y1 <- f1 + rnorm(128)
# performing the dwt of y1
y1wd <- wd(y1, filter.number = 5)
plot(x, y1, cex = 0.55)
lines(x, wr(threshold(y1wd, policy = "manual", type = "hard", levels =
0:6, value = 1.75)))
mtext("Threshold = 1.75", side = 3, line = 0.1, cex = 0.6)
plot(x, y1, cex = 0.55)
lines(x, wr(threshold(y1wd, policy = "manual", type = "hard", levels =
0:6, value = 2.75)))
mtext("Threshold = 2.75", side = 3, line = 0.1, cex = 0.6)
plot(x, y1, cex = 0.55)
lines(x, wr(threshold(y1wd, policy = "manual", type = "hard", levels =
0:6, value = 3.75)))
mtext("Threshold = 3.75", side = 3, line = 0.1, cex = 0.6)</p>
<p> y2 <- f2 + rnorm(128)
y2wd <- wd(y2, filter.number = 5)
plot(x, y2, cex = 0.55)
lines(x, wr(threshold(y2wd, policy = "manual", type = "hard", levels =
0:6, value = 1.75)))
mtext("Threshold = 1.75", side = 3, line = 0.1, cex = 0.6)
plot(x, y2, cex = 0.55)
lines(x, wr(threshold(y2wd, policy = "manual", type = "hard", levels =
0:6, value = 2.75)))
mtext("Threshold = 2.75", side = 3, line = 0.1, cex = 0.6)
plot(x, y2, cex = 0.55)
lines(x, wr(threshold(y2wd, policy = "manual", type = "hard", levels =
0:6, value = 3.75)))
mtext("Threshold = 3.75", side = 3, line = 0.1, cex = 0.6)</p>
</p>
<p>###################################################
### chunk number 13: Denoise2
###################################################
par(mfrow = c(2, 2), mar = c(1.5, 1.5, 1.5, 0.5), mgp = c(5, 0.4, 0))
x <- (1:2048)/2048
f <- fblocks(x)
ssig <- sqrt(var(f))
f <- ((f - mean(f)) * 5)/ssig
fnoise <- f + rnorm(2048)
fwd <- wd(fnoise, filter.number = 5)
plot(x, f, type = "l")
mtext(side = 3, line = 0.1, "True function")
plot(x, fnoise, type = "l")
mtext(side = 3, line = 0.1, "Noisy observations")
fwdsure <- threshsure(fwd)
plot(x, wr(fwdsure), type = "l")
mtext(side = 3, line = 0.1, "SURE estimator")
fwdhyb <- threshhyb(fwd, lowlev = 2, seed = 0)
plot(x, wr(fwdhyb), type = "l")
mtext(side = 3, line = 0.1, "SURE hybrid estimator")</p>
<p>###################################################
### chunk number 14: TI1
###################################################
# k1 and k2 are translation amounts
k1 <- 32
k2 <- 42
par(mfrow = c(3, 1), mar = c(1.5, 1.5, 1.5, 0), mgp = c(5, 0.4, 0))
x <- (1:128)/128
unshft <- c(rep(1, 64), rep(-1, 64))
noise <- rnorm(128)
# ORIGINAL ESTIMATOR
y <- 2 * unshft + noise
plot(x, y, type = "l")
yest <- wr(threshold(wd(y, filter.number = 1), levels = 0:6))
lines(x, yest)
# FIRST TRANSLATED ESTIMATOR
if(k1 == 0) y1 <- y else y1 <- c(y[(1 + k1):128], y<a href="#ref1:k1">[1:k1]</a>)
if(k1 == 0) shft1 <- unshft else shft2 <- c(unshft[(1 + k1):128], unshft<a href="#ref1:k1">[1:k1]</a>)
plot(x, y1, type = "l")
y1est <- wr(threshold(wd(y1, filter.number = 1), levels = 0:6))
lines(x, y1est)
# SECOND TRANSLATED ESTIMATOR
if(k2 == 0) y2 <- y else y2 <- c(y[(1 + k2):128], y<a href="#ref1:k2">[1:k2]</a>)
if(k2 == 0) shft2 <- unshft else shft2 <- c(unshft[(1 + k2):128], unshft<a href="#ref1:k2">[1:k2]</a>)
plot(x, y2, type = "l")
y2est <- wr(threshold(wd(y2, filter.number = 1), levels = 0:6))
lines(x, y2est)</p>
<p>###################################################
### chunk number 15: TI2
###################################################
par(mfrow=c(1,1))
par(mar = c(1.5, 1.5, 1.5, 0.5), mgp = c(5, 0.4, 0))
x <- (1:128)/128
y <- 2 * c(rep(1, 64), rep(-1, 64)) + rnorm(128)
plot(x, y, type = "l")
yest <- spincyclen(y, filter.number = 1)
lines(x, yest)
yest1 <- spincyclen(y, filter.number = 3)
lines(x, yest1,col=<code>red</code>)</p>
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<a href="../../../../News/2009-10-28-Release-2.5"
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BioC 2.5, consisting of 352 packages and designed to work with R 2.10.z, was released today.
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R, the open source platform used by Bioconductor, featured in a series of articles in the New York Times.
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