wc(WaveletCo)
wc()所属R语言包：WaveletCo
Wavelet Coherence Analysis
小波相干分析
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The main function of this package; conducts the wavelet coherence analysis
这个包的主要功能进行了小波相干分析
用法----------Usage----------
wc(x, y, start = 0, dt = 10, dj = 1/20, method = "white.noise", lowerPeriod = 2 * dt, upperPeriod = floor(length(x)/3) * dt, no.bs = 100, plot = TRUE)
参数----------Arguments----------
参数：x
time series x
时间序列x
参数：y
time series y
时间序列y
参数：start
The start point of time axis
时间轴上的开始点
参数：dt
Sampling resolution on temporal field
时间领域的采样分辨率
参数：dj
Sampling resolution on frequency field
频域采样分辨率
参数：method
the method of generating surrogate time series
生成替代时间序列的方法
参数：lowerPeriod
Lower period of wavelet decomposition
下期小波分解
参数：upperPeriod
upper period of wavelet decomposition
上期小波分解
参数：no.bs
the number of bootstrap
的数目自举
参数：plot
if TRUE, then plot the result
如果为TRUE，则绘制的结果
（作者）----------Author(s)----------
Huidong Tian, Bernard Cazelles
实例----------Examples----------
x <- c(1.50* sin(seq(0, 30*pi, length.out = 70)) +
rnorm(70, sd = .25), rnorm(30, sd = 1.25)) +
sin(seq(0, 10*pi, length.out = 100))*2 +
seq(4, -4, length.out = 100)
y <- c(1.25* sin(seq(0, 30*pi, length.out = 70)+7*pi/8) +
rnorm(70, sd = .25), rnorm(30, sd = 1.25) ) +
sin(seq(0, 10*pi, length.out = 100))*2 +
seq(-4, 4, length.out = 100)
ns <- 200
WC <- wc(x, y, no.bs = ns, dt= 1, start = 1909, plot = FALSE)
##############################[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃]
f.n = 2
f.h = 25/25.4
m.t = 0.5;
m.b = 1.5
m.1 = 1;
m.2 = 3;
m.3 = 1;
m.4 = 2
W = 120/25.4
H = f.n*(f.h + (m.1+m.3)*4/30)+ (m.t + m.b)*4/30
tick0 = 0.025;
tick1 = tick0*f.h/(.15*W-m.4*4/30)
fig.h = (f.h + (m.1+m.3)*4/30)/H
fig.b = m.b*4/30/H
fig.w = list(); for(i in 1:f.n) fig.w[[i]] = c( 0,.8, fig.b + (f.n-i)*fig.h,fig.b + (f.n+1-i)*fig.h)
fig.p = list(); for(i in 1:f.n) fig.p[[i]] = c(.8,1, fig.b + (f.n-i)*fig.h,fig.b + (f.n+1-i)*fig.h)
#############################################[＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃＃]
x11(W,H,pointsize = 8)
## plot #1[＃图＃1]
par(fig = fig.w[[1]],mar = c(m.1,m.2,m.3,0))
plot (seq(1909, length.out= 100), x, type = "o", ylim = c(min(x, y), max(x, y)), pch = 19, axes = FALSE, xaxs = "i")
abline(lm(x~seq(1909, length.out= 100)), lwd = 2)
lines(seq(1909, length.out= 100), y, type = "o", pch = 21, col = "red")
abline(lm(y~seq(1909, length.out= 100)), lwd = 2, col = "red")
box(lwd = .25)
axis(1,lwd = .25,at = seq(1920, 2000, 20),label = NA,tck = tick0)
axis(2,lwd = .25,at = seq(-3, 3, 3),label = NA,tck = tick0)
mtext(seq(1920, 2000, 20),side = 1,at = seq(1920, 2000, 20),cex = 7/8)
mtext(seq(-3, 3, 3),side = 2,at = seq(-3, 3, 3),cex = 7/8,las = 1,line = .1)
mtext('(a)',side = 3,at = 1900,line = 0.1,font = 2,adj = 0)
mtext('Time series',side = 2,line = 1.5,font = 1)
## plot #2[＃2＃号图]
par(fig = fig.w[[2]],mar = c(m.1,m.2,m.3,0), new = TRUE)
wc.image(WC)
axis(1,lwd = .25,at = seq(1920, 2000, 20),label = NA,tck = tick0)
axis(2,lwd = .25,at = seq(1, 5),label = NA,tck = tick0)
mtext(seq(1920, 2000, 20),side = 1,at = seq(1920, 2000, 20),cex = 7/8)
mtext(2^seq(1, 5),side = 2,at = seq(1, 5),cex = 7/8,las = 1,line = .1)
mtext('(b)',side = 3,at = 1900,line = 0.1,font = 2,adj = 0)
mtext('Period',side = 2,line = 1.5,font = 1)
mtext('Year',side = 1,line = 1)
par(fig = fig.p[[2]],mar = c(m.1,0,m.3,m.4),new = TRUE)
wc.power(WC)
axis(1,lwd = .25,at = seq(.7, 1, .1),label = NA,tck = tick0)
axis(4,lwd = .25,at = seq(1, 5),label = NA,tck = tick0)
mtext(seq(.7, 1, .1),side = 1,at = seq(.7, 1, .1),cex = 7/8)
mtext(2^seq(1, 5),side = 4,at = seq(1, 5),cex = 7/8,las = 1,line = .1)
mtext('Power',side = 1,line = 1)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 16:56:12
