treethresh(treethresh)
treethresh()所属R语言包：treethresh
Compute optimal thresholding partition for a sequence, matrix, or array of data.
计算最优阈值分割序列，矩阵或数组中的数据。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function carries out the tree-based thresholding algorithm described in section 3 of Evers and Heaton (2009).
此功能进行Evers和希顿（2009年）第3条中描述的基于树的阈值算法。
用法----------Usage----------
treethresh(data, beta, criterion="score", control=list(),
rho=sys.frame(sys.parent()))
参数----------Arguments----------
参数：data
An array (or an object coercible to an array, i.e. a vector or matrix) containing the data. The data is assumed to have noise of unit variance, thus the data needs to be rescaled a priori (e.g. in the case of wavelet coefficients using function estimate.sdev).
包含数据的一个数组（或转换到一个数组中的一个目的，即一个向量或矩阵）。数据被认为有噪声的单位方差，因此，需要对数据进行重新调整先验的（例如，在小波系数的情况下，使用功能estimate.sdev）。
参数：beta
Instead of using the original data, one can call wtthresh using the beta_i instead of the observed data. These can be computed using beta.laplace.
而不是使用原始的data，可以调用“wtthresh使用beta_i的观测数据。这些可以计算使用beta.laplace。
参数：criterion
The criterion to be used. Can be "score" (default) for using the score test, "likelihood" for using the likelihood ratio test (slower), "heuristic" for using a heuristic criterion based on the original data, or a user-specified function that computes the goodness of a split. This function should take four arguments (which should be self-explanatory), left_data, left_betas, right_data, and right_betas.
要使用的标准。可以"score"（默认）使用似然比检验（更慢）的得分测试，"likelihood"的，"heuristic"使用上的原始数据基于启发式的标准的，或用户指定的函数，计算一个分裂的美好。这个函数应该采取四个参数（这应该是不言自明的），left_data，left_betas，right_data，right_betas。
参数：control
A list that allows the user to tweak the behaviour of treethresh. It can contain the following elements:
一个列表，允许用户调整的行为treethresh。它可以包含以下内容：
max.depthThe maximum depth of the tree. Defaults to 10.
max.depthThe树的最大深度。默认为10的。
minimum.widthThe minimum width of a region of the partitions. This setting avoids creating too small regions. Defaults to 3.
minimum.widthThe分区的区域的最小宽度。此设置可避免过小区域。默认为3的。
minimum.sizeThe minimum size of a region of the partitions. This setting avoids creating too small regions. Defaults to 5^d, where d is the dimension of the arras.
minimum.sizeThe的最小尺寸的区域的分区。此设置可避免过小区域。默认为5^d，d是阿拉斯的尺寸。
lr.signifIf the p-value of the corresponding likelihood ratio test is larger than 1-lr.signif a split will be discarded. Defaults to 0.5.
lr.signifIf相应的似然比检验的p-值是大于1-lr.signif拆分将被丢弃。默认为0.5的。
absolute.improvementThe minimum absolute improvement of the above criterion necessary such that a split is retained. Defaults to -Inf, i.e. deactivated.
absolute.improvementThe最小绝对改善的必要的上述标准，使得保留分裂。默认为-Inf，即停用。
relative.improvementThe minimum relative improvement of the above criterion necessary such that a split is retained. Defaults to -Inf, i.e. deactivated.
relative.improvementThe最小相对改善的必要的上述标准，使得分裂被保留。默认为-Inf，即停用。
absolute.criterionThe minimum value of the above criterion necessary such that a split is retained. Defaults to 0, i.e. deactivated.
absolute.criterionThe最小值的上述标准必要的，使得分裂被保留。默认为0，即停用。
aThe parameter a of the Laplace distribution gamma(mu) = const * exp(-a*mu) corresponding to the signal. Defaults to 0.5.
七成参数a的拉普拉斯分布gamma(mu) = const * exp(-a*mu)对应于该信号。默认为0.5的。
beta.maxThe maximum value of beta. Defaults to 1e5.
beta.maxThe最高值beta。默认为1e5的。
max.iterThe maximum number of iterations when computing the estimate of the weight w in a certain region. Defaults to 30.
max.iterThe最大迭代次数，当计算的估计的重量w在一定区域内。默认为30的。
tolerance.gradThe estimate of the weight w in a certain region is considered having converged, if the gradient of the likelihood is less than tolerance.grad. Defaults to 1e-8.
tolerance.gradThe估计的重量w在一定的区域被认为是具有融合，如果梯度的可能性小于tolerance.grad。默认为1e-8的。
toleranceThe estimate of the weight w in a certain region is considered having converged, if the estimates of the weight w change less than tolerance. Defaults to 1e-6.
toleranceThe估计的重量w在一定区域内的被认为是融合的，如果估计的重量w改变小于tolerance。默认为1e-6的。
参数：rho
The environment used to evaluate the user-speficied criterion function if one is supplied). (You want to change this argument only in very rare circumstances).
所使用的环境评估用户speficied的判别函数，如果一个被供给）。 （您想改变这种说法只有在极少数的情况下）。
值----------Value----------
treethresh returns an object of the class c("treethresh"), which is a list containing the following elements: <table summary="R valueblock"> <tr valign="top"><td>splits</td> <td> A table describing the detailed structure of the fitted tree together with the local loglikelihoods required for the pruning.</td></tr> <tr valign="top"><td>membership</td> <td> An array of the same dimension as data or beta indicating to which region each entry of the array of data belongs.</td></tr> <tr valign="top"><td>beta</td> <td> The values of beta for each observation / coefficient.</td></tr> <tr valign="top"><td>data</td> <td> The data used.</td></tr> <tr valign="top"><td>criterion</td> <td> The criterion used to decide on splits (see argument criterion).</td></tr> <tr valign="top"><td>control</td> <td> The control list of tuning options used (see argument control).</td></tr> </table>
treethresh返回一个对象类c("treethresh")，这是一个列表，其中包含以下元素：<table summary="R valueblock"> <tr valign="top"> <TD>splits </ TD> <td>一个表一起的修剪与需要当地loglikelihoods，，描述的详细结构的拟合树。</ TD> </ TR> <tr valign="top"> <TD><X > </ TD> <TD>数组的尺寸相同membership或data表明每个条目的数组中的数据属于哪个区域。</ TD> </ TR> <TR VALIGN =“”> <TD>beta </ TD> <TD>的值beta每个观察/系数。</ TD> </ TR> <TR VALIGN =“顶” > <TD> beta </ TD> <TD>使用的数据。</ TD> </ TR> <tr valign="top"> <TD> data</ TD> <TD使用的标准上分裂（见参数criterion）</ TD> </ TR> <tr valign="top"> <TD> criterion </ TD> <TD>的决定控制列表的调节选项（见参数control）</ TD> </ TR> </表>
参考文献----------References----------
Evers, L. and Heaton, T. (2009) Locally Adaptive Tree-Based Thresholding. Journal of Computational and Graphical Statistics. Dec 2009, Vol. 18, No. 4: 961-977.
实例----------Examples----------
# (1) Create a vector with the probabilities of a signal being present[（1）创建一个矢量信号存在的概率]
w.true <- c(rep(0.1,400),rep(0.7,300),rep(0.1,300))
# (2) Generate the signal[（2）生成的信号]
mu <- numeric(length(w.true))
non.zero.entry <- runif(length(mu))<w.true
num.non.zero.entries <- sum(non.zero.entry)
mu[non.zero.entry] <- rexp(num.non.zero.entries,rate=0.5)*sample(c(-1,1),num.non.zero.entries,replace=TRUE)
# (3) Split graphics device[（3）分割图形设备]
par(mfrow=c(2,2))
# (3) Draw the true signal (signal present in red)[（3）画出真正的信号（信号存在于红）]
plot(mu,col=non.zero.entry+1)
title("True signal")
# (4) Add noise to the signal[（4）噪声的信号]
x <- mu + rnorm(length(mu))
# (5) Plot the noisy signal (signal present in red)[（5）绘制噪声信号（信号存在于红）]
plot(x,col=non.zero.entry+1)
title("Noisy signal")
# (6) Carry out the tree-based thresholding[（6）开展基于树的阈值]
tt <- treethresh(x)
# (7) Prune the tree[（7）修剪树]
tt.pruned <- prune(tt)
# (8) Threshold the signal according to the pruned tree[（8）根据修剪树阈值的信号]
mu.hat <- thresh(tt.pruned)
# (9) Print the denoised signal[（9）打印降噪信号]
plot(mu.hat,col=non.zero.entry+1)
title("Denoised signal")
# (10) Add solid lines for splits (lines removed by the pruing are dashed)[（10）实线分割（由pruing的行删除虚线）]
abline(v=tt$split[,"pos"],lty=2)
abline(v=tt.pruned$split[,"pos"],lty=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。
|
发表于 2012-10-1 11:53:58
