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R语言 wavethresh包 plot.wp()函数中文帮助文档(中英文对照)

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发表于 2012-10-1 17:29:35 | 显示全部楼层 |阅读模式
plot.wp(wavethresh)
plot.wp()所属R语言包:wavethresh

                                        Plot wavelet packet transform coefficients
                                         图小波包变换系数

                                         译者:生物统计家园网 机器人LoveR

描述----------Description----------

This function plots wavelet packet transform coefficients arising from a wp.object object.
此函数曲线小波包变换的系数产生一个wp.object对象。


用法----------Usage----------


## S3 method for class 'wp':
plot(x, nvwp = NULL, main = "Wavelet Packet Decomposition",
    sub, first.level = 5, scaling = "compensated", dotted.turn.on = 5,
    color.force = FALSE, WaveletColor = 2, NodeVecColor = 3,
    fast = FALSE, SmoothedLines = TRUE, ...)



参数----------Arguments----------

参数:x
The wp object whose coefficients you wish to plot.
你想绘制的WP对象,其系数。


参数:nvwp
An optional associated wavelet packet node vector class object of class nvwp. This object is a list of packets in the wavelet packet table. If this argument is specified then it is possible to highlight the packets in the nvwp objects in a different color using the NodeVecColor argument
一个可选的关联小波包节点向量类对象的类nvwp。此对象是列表中的小波包表中的数据包。如果指定该参数,则使用NodeVecColor参数,它是可能的,突出的数据包中的nvwp的对象以不同的颜色


参数:main
The main title of the plot.
主标题的图。


参数:sub
A subtitle for the plot.
字幕的图。


参数:first.level
The first resolution level to begin plotting at. This argument can be quite useful when you want to supress some of the coarser levels in the diagram.
第一项决议,开始图。当你想来说,拦截一些粗糙的水平在图中,此参数可以是非常有用的。


参数:scaling
How you want the coefficients to be scaled. The options are: global - one scale factor is chosen for the whole plot. The scale factor depends on the coefficient to be included on the plot that has the largest absolute value. The global option is useful when comparing coefficients that might appear anywhere in the plot; by.level - a scale factor is chosen for each resolution level in the plot. The scale factor for a level depends on the coefficient in that level that has the largest absolute value. The by.level option is useful when you wish to compare coefficients within a resolution level. The other option is compensated which is the same as global except for that finer scales' coefficients are scaled up by a factor of SQRT(2)
你怎么想的系数进行调整。选项有:global  - 选择一个比例因子是整个小区。规模因素取决于系数的绝对值最大的图,被列入。全局选项是很有用的比较系数时,可能出现在任何地方的图,“by.level - 选择比例因子为每个分辨率级别的图。的比例因子的水平依赖于在该级别的系数,具有最大绝对值。 by.level选项是有用的,当你想比较的分辨率级别内的系数。另一种选择是compensated这是相同的为全局的除外,更精细的尺度系数缩放由一个因素SQRT(2)

I don't know why compensated is the default option? That is probably silly!
我不知道为什么补偿是默认选项?这可能是太傻了!


参数:dotted.turn.on
The plot usually includes some dotted vertical bars that separate wavelet packets to make it clearer which packets are which. This option controls the coarsest resolution level at which dotted lines appear. All levels equal to and finer than this level will receive the vertical dotted lines.
图通常包括一些点缀的竖条,使其更清晰的数据包是不同的小波包。此选项可控制的粗分辨率的水平虚线出现。所有级别等于或小于这个水平将收到的垂直的虚线。


参数:color.force
If FALSE then some "clever" code in CanUseMoreThanOneColor tries to figure out how many colours can be used (THIS HAS NOT BEEN MADE TO WORK IN R)  and hence whether colour can be used to pick out wavelet packets or elements of a node vector. This option was designed to work with S. It doesn't work with R and so it is probably best to set color.force=T. In this way no interrogation is done and the lines/packets are plotted in the appropriate colours with no questions asked.
如果为FALSE,然后在CanUseMoreThanOneColor一些“聪明”的代码试图找出多少种颜色可以使用(这已不是MADE IN R),因此无论是颜色可以用来挑选出的小波包或元素的节点矢量。此选项的目的是要与S.,这是行不通的,R和设置color.force=T所以它可能是最好的。在这种方式中,没有进行审讯,并线/数据包被绘制在适当的颜色,不问任何问题。


参数:WaveletColor
A colour specification for the colour for wavelet coefficients. Wavelet coefficients are a component of wavelet packet coefficients and this option allows them to be drawn in a different color. In R you can use names like "red", "blue" to select the colors. In R you'll also need to set the color.force option to TRUE.
颜色规范的小波系数的颜色。小波系数小波包系数的一个组成部分,此选项使它们能够以不同的颜色绘制。在R中,你可以使用的名称,如“红”,“蓝”的颜色选择。在R,你也需要,设置color.force选项设置为true。


参数:NodeVecColor
If a nvwp object is supplied this option can force coefficients that are part of that nvwp to be drawn in the specified color. See the explanation for the WaveletColor option above about specification in R.
如果一个nvwp对象供给该选项可以强制系数,属于该nvwp的要在指定的颜色绘制。 WaveletColor选项的解释,请参阅上述有关规范R.


参数:fast
This option no longer does anything.
此选项不再做任何事情。


参数:SmoothedLines
If TRUE then the scaling function coefficients are drawn using lines (and look like mini versions of the original). If FALSE then the scaling function coefficients are drawn using the segments function and look like a coarser shadowy version of the original.
如果为true,则尺度函数的系数绘制使用行(看起来像迷你版的原)。如果为假则使用segments功能和外观像一个粗糙的朦胧的版本的原始尺度函数的系数被吸引。


参数:...
Other arguments to the plot command
绘图命令的其他参数


Details

详细信息----------Details----------

A wavelet packet object contains wavelet packet coefficients of a signal (usually obtained by the wp wavelet packet transform function). Given a wavelet packet object wp it possesses nlevels(wp) resolution levels. In WaveThresh the coarsest level is level 0 and the finest is level nlevels-1. For wavelet packets the number of packets at level j is 2^(nlevels-j).
一种小波包对象包含小波包系数的信号(通常通过wp小波包变换功能)。由于它拥有nlevels(wp)的分辨率级别的小波包对象WP。在WaveThresh最粗的级别为0和最优秀的是的水平NLEVELS-1。小波包在第j层的数据包的数量是2 ^(NLEVELS-J)。

This function plots the wavelet packet coefficients. At the bottom of the plot the original input function (if present) is plotted. Then levels above the original plot successively coarser wavelet packet coefficients. From the Mallat transform point of view smoothing goes up off the the left of the picture and detail to the right. The packets are indexed from 0 to the number of packets going from left to right within each resolution level.
此函数曲线的小波包系数。在底部的图的原始的输入功能(如果存在的话)绘制。然后以上的原创图的,在粗糙的小波包系数的水平。来自Mallat变换点来看平滑上升的折扣的左侧到右侧的图像和细节。的数据包的索引从0到的数据包的数目从左至右在每个分辨率等级。

The function has the ability to draw wavelet coefficients in a different color using the WaveletColor argument.
该函数有能力绘制小波系数在不同的颜色WaveletColor使用参数。

Optionally, if a node vector wavelet packet object is also supplied, which contains the specification of a basis selected from the packet table, then packets in that node vector can be highlighted in a another colour determined by the NodeVecColor.
任选地,如果一个节点的向量小波包对象也被提供,它包含选自分组表的基础的规格,然后在该节点的向量的数据包可以在另一种颜色由NodeVecColor确定高亮显示。

Packets are drawn on the plot and can be separated by vertical dotted lines. The resolution levels at which this happens can be controlled by the dotted.turn.on option. The coarsest resolution level to be drawn is controlled by the first.level option.
报文的图上绘制,并可以由垂直虚线分隔。分辨率级别在发生这种情况,可以通过dotted.turn.on选项。最粗糙的分辨率级要绘制控制由first.level选项。


值----------Value----------

Nothing



(作者)----------Author(s)----------


G P Nason



参见----------See Also----------

MaNoVe, wp, wp.object
MaNoVe,wp,wp.object


实例----------Examples----------


#[]
# Generate some test data[生成一些测试数据。]
#[]
v <- DJ.EX()$blocks
#[]
# Let's plot these to see what they look like[让我们来绘制这些看他们的样子]
#[]
## Not run: plot(v, type="l")[#未运行图(V,类型为“L”)]
#[]
# Do a wavelet packet transform[小波包变换]
#[]
vwp <- wp(v)
#[]
# And create a node vector[创建一个节点矢量]
#[]
vnv <- MaNoVe(vwp)
#[]
# Now plot the wavelet packets with the associated node vector[现在,绘制相关联的节点向量的小波包]
#[]
## Not run: plot(vwp, vnv, color.force=T, WaveletColor="red", dotted.turn.on=7)[#不运行:图(VWP,VNV,color.force = T,WaveletColor =“红色”,dotted.turn.on = 7)]
#[]
# The wavelet coefficients are plotted in red. Packets from the node vector[红色绘制的小波系数。数据包的节点矢量]
# are depicted in green. The node vector gets plotted after the wavelet[被描绘为绿色。得到的节点矢量绘制后的小波]
# coefficients so the green packets overlay the red (retry the plot command[所以覆盖的绿色报文红(重试绘图命令系数]
# but without the vnv object to see just the[但没有的VNV对象,看到的只是]
# wavelet coefficients). The vertical dotted lines start at resolution[小波系数)。垂直虚线开始在分辨率]
# level 7.[7级。]
#[]
#[]

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。
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