PosRidgeogram(SigWinR)
PosRidgeogram()所属R语言包：SigWinR
Significant window detector in non regularly sampled sequences
显着的窗口检测器在非定期采样序列
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Create a RIDGEOGRAM for a sequence of data of non-regularly sampled data. The function tests the input sequence against the null hypothesis that the value in the sequence does depend on position. The function produces p-values for the probability that the sequence is locally higher or lower than average for a certain position and window-size for a range of positions and window sizes. By default the p-value is corrected for multiple testing using the Benjamini-Hochberg correction.
创建一个RIDGEOGRAM非定期采样数据的数据序列。输入序列与功能测试的零假设的价值不依赖于序列中的位置。函数产生的概率，该序列是局部比平均水平更高或更低的范围内的位置和窗口大小为一定的位置和窗口尺寸的p-值。缺省情况下，p-值被校正多个测试使用Benjamini霍赫贝格校正。
用法----------Usage----------
PosRidgeogram(seq,pos,resolution=400,winrange=c(0,max(pos)-min(pos)),adjust.method="BH",circular=FALSE)
参数----------Arguments----------
参数：seq
sequence of samples
样本序列
参数：pos
sequence containing location of samples
样品的序列位置
参数：resolution
resolution of ridgeogram. Both the number of samples along the sequence for which a p-value has to be calculated and the number of widow sizes to be used between the minimum and maximum value specified in winrange.
分辨率的ridgeogram。这两个样本的数目沿该序列的p-值被计算和寡妇体积之间要使用的最小和最大中指定winrange的值的数目。
参数：winrange
minimum and maximum values for the range of window sizes for which the Ridgeogram has to be calculated. The resolution parameter determines the number of window sizes between the minimum and maximum will be calculated.
窗口大小的范围内，以计算该Ridgeogram的最小值和最大值。分辨率参数确定的窗口尺寸的数目之间的最小和最大的将被计算。
参数：adjust.method
method for multiple testing correction (see p.adjust for valid options)
多重检验校正方法（见p.adjust有效的选项）
参数：circular
assume the sequence is circular
假定序列是圆形的
Details
详细信息----------Details----------
For a PosRidgeogram the number of entries in the ridgeogram given a certain window-size is not fixed by the length of the sequence as it is in the standard ridgeogram. The window-size is linked to the position values in the input sequence and not to element number in the sequence. The sampling density must be specified seperately by the resolution parameter. This resolution is also independent the window-size for which ridges and anti-ridges will be calculated.
对于PosRidgeogram中的条目数的ridgeogram给予一定的窗口大小由该序列的长度是不固定的，因为它是在标准ridgeogram。的窗口大小的被链接到输入序列中的位置中的值，而不是在序列中的元素数目。必须指定单独的采样密度的分辨率参数。这项决议是独立的窗口大小的山脊和反垄计算。
值----------Value----------
A list containing <table summary="R valueblock"> <tr valign="top"><td>high</td> <td> Array containing Benjamini-Hochberg corrected p-values for the probability that the sequence is locally higher than average within the given window-size , the rows contain the windowsizes. starting from 3, the significance for all odd window sizes are calculated. The columns contain the p-values along the input sequence for a window size. If circular is FALSE the number of possible windows decreases with increasing window size and the output is centered within the rows. In this way the output can be rendered as an image (see RidgeogramPlot). </td></tr> <tr valign="top"><td>low</td> <td> Same as high for signifcantly lower regions.</td></tr> <tr valign="top"><td>sequence</td> <td> The input sequence used.</td></tr> <tr valign="top"><td>position</td> <td> The input location used.</td></tr> <tr valign="top"><td>range</td> <td> The location range used.</td></tr> <tr valign="top"><td>circular</td> <td> Circularity was assumed.</td></tr> </table>
一个列表，其中包含表summary="R valueblock"> <tr valign="top"> <TD> high </ TD> <TD>阵列含有Benjamini-霍赫贝格纠正的概率p值的序列局部高于平均水平，在给定的窗口大小，行包含windowsizes的。从3开始，其意义为所有奇窗口大小的计算方法。列中包含的p值沿的输入序列中的窗口的大小。如果是圆形的数目为FALSE可能的窗口的窗口大小的增加而减小，和输出的行内居中。以这种方式，输出可以被渲染作为图像（见RidgeogramPlot）。 </ TD> </ TR> <tr valign="top"> <TD>low </ TD> <TD>同highsignifcantly较低的区域。</ TD> </ TR > <tr valign="top"> <TD> sequence </ TD> <TD>使用的输入序列。</ TD> </ TR> <tr valign="top"> <TD> X> </ TD> <TD>输入使用的位置。</ TD> </ TR> <tr valign="top"> <TD>position </ TD> <TD>的位置范围内使用</ TD> </ TR> <tr valign="top"> <TD>range </ TD> <TD>圆假设。</ TD> </ TR> </ TABLE>
注意----------Note----------
The computation time is significantly higher than the Ridgeogram function.
计算时间显着更高比Ridgeogram函数。
（作者）----------Author(s)----------
w.c.deleeuw@uva.nl
参考文献----------References----------
enriched windows of genomic features related to DNA sequences Marcia A Inda, Marinus F van Batenburg, Marco Roos, Adam SZ Belloum, Dmitry Vasunin, Adianto Wibisono, Antoine HC van Kampen, and Timo M Breit
参见----------See Also----------
SigWin,RidgeogramPlot
SigWin，RidgeogramPlot
实例----------Examples----------
slen <- 200
pos <- 4*sort(rnorm(slen))
RidgeogramPlot(PosRidgeogram(sin(pos)+rnorm(slen,sd=0.3),pos,circular=TRUE))
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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发表于 2012-9-30 02:14:02
