estimate,Les-method(les)
estimate,Les-method()所属R语言包：les
estimate
估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
The 'estimate' method computes the fraction Lambda of probes with significant effect in the local surrounding of the genome.
“估算”的方法计算分数显着影响，在当地周围的基因组探针Lambda。
用法----------Usage----------
estimate(object, win, weighting = triangWeight, grenander = TRUE,
se = FALSE, minProbes = 3, method = "la", nCores = NULL, verbose =
FALSE, ...)
## S4 method for signature 'Les'
estimate(object, win, weighting = triangWeight,
grenander = TRUE, se = FALSE, minProbes = 3, method = "la", nCores =
NULL, verbose = FALSE, ...)
参数----------Arguments----------
参数：object
Object of class 'Les' containing experimental data, as returned by 'Les'.
对象类莱斯包含的实验数据，返回由“莱斯”。
参数：win
Integer specifying window size for the weighting function. This value is directly passed to the function specified by 'weighting'. For details see the description for the used window function 'weighting'.
加权函数的整数，指定窗口的大小。此值直接传递给指定的函数加权。有关详细信息，请参阅使用窗口函数加权的描述。
参数：weighting
Function specifying the shape of the weighting window. If not specified the supplied 'triangWeight' function with a triangular window will be used. For details on other window functions and how to specify own functions please see the 'Details' section.
功能指定加权窗口的形状。如果没有指定一个三角窗提供的triangWeight“的功能将被使用。对于其他窗口功能的详细信息，以及如何指定自己的功能，请参阅“详细资料”部分。
参数：grenander
Logical specifying if the Grenander correction for the cumulative density should be used (default: TRUE). For details see the 'Details' section.
逻辑指定累计密度Grenander校正应使用（默认：true）。有关详细信息，请参阅“详细资料”一节。
参数：se
Logical indicating whether the standard error (SE) from the final linear model should be computed and stored (default: FALSE). The standard error displays the goodness of fit for every probe, but is not needed for further computation. If computation time is a critical factor computation of the SE can be omitted to save some time.
逻辑显示标准的错误，从最后的线性模型（SE）是否应计算和存储（默认：false）。标准错误显示适合每个探针的美好，但并不需要作进一步的计算。如果计算时间是一个关键因素计算的SE可以省略，以节省一些时间。
参数：minProbes
Integer specifying the minimal number of unqiue p-values that must be present for each fit (default: 3). For very small number of p-values the cumulative density is not well defined and therefore estimation has a high uncertainty. If the number of unique p-values is smaller than 'minProbes' no estimation is performed for this probe and Lambda=NA is returned.
整数，指定的unqiue最少数量必须为每一个合适的（默认值：3）目前的P-值。对于非常小的p值累计密度没有明确界定，因此，估计有很高的不确定性。如果一些独特的p-值小于“minProbes没有估计这种探针和Lambda=娜是返回。
参数：method
Character string specifying the method used for linear regression (default: 'la'). Possible options are 'la' for a method based on linear algebra or 'qr' for a method based on qr decomposition. 'la' will be faster for few probes, 'qr' for many probes in a window. The best choice varies between data sets, parameters and machines. However this option only influences computation time but not the results.
字符串指定用于线性回归（默认：“拉”）的方法。可能的选项是“拉”的方法基于线性代数或“QR”基于QR分解的一种方法。 LA会更快几个探针，“QR”，在一个窗口中的许多探针。最好的选择不同的数据集，参数和机器。然而，这个选项只影响计算时间，但没有结果。
参数：nCores
Integer indicating the number of cores to use for computation. This feature requires the 'multicore' package which is only available for certain platforms. The package is used only if 'library(multicore)' has been called manually by the user before and if 'nCores' is an integer unequal NULL specifying the number of cores to use. The value is passed directly to 'mclapply' as argument 'n.cores'. For details and benefits please see the 'Details' section.
整数，指示内核数量计算使用。此功能要求的多核包这是只适用于某些平台。只有当库（多核）已要求用户手动“nCores”是一个整数的不平等NULL，指定使用的核心数量之前，如果使用的包。 “mclapply”参数“n.cores”的值被直接传递。请参阅“详细资料”部分的细节和福利。
参数：verbose
Logical indicating whether the progress of the computation should be printed on screen (default: FALSE).
逻辑表示是否计算的进展，应印在屏幕上（默认：false）。
参数：...
Further arguments passed to subsequent functions.
进一步的参数传递给后续的功能。
Details
详情----------Details----------
This function estimates Lambda(i) for all probes i. This is normally the first step in the analysis after storing the experimental data with 'Les'.
这个功能估计Lambda(i)所有探针i。这通常是在分析后存储的实验数据与LES的第一步。
The 'win' argument influences the number of neighboring probes taken into account by the weighting function. The value is passed to the function specified in 'weighting'. Larger values result in a smoother features. Details on a reasonable choice for this value can be found in the references.
双赢的说法，影响加权函数考虑到邻近的探针数量。通过“加权”在指定的函数值。值越大，导致更顺畅的功能。在此值的合理选择的详情，可参考文献中发现。
With the 'weighting' argument the applied weighting function can be specified from a predefined set or a custom function can be used. In the 'les' package the four functions 'triangWeight', 'rectangWeight', 'epWeight' and 'gaussWeight' are already supplied and offer a triangular, rectangular, Epanechnikov and Gaussian weighting function respectively. For details on the functions itself and how to use custom ones please see the documentation of the single functions or the vignette of this package.
应用加权函数与加权的说法可以从一组预定义或自定义功能，可以用来指定。在四大功能triangWeightLES包，rectangWeight，epWeight和gaussWeight“已经提供，并提供一个三角形，长方形，Epanechnikov和高斯加权函数。本身以及如何使用自定义的有关功能的详细信息，请参阅文件的单一功能或此包的小插曲。
The Grenander correction for the cumulative density includes the general knowledge about the concave shape of the cumulative density. This reduces the variance of the estimates and leads to a conservative estimation. Please note that using this feature may significantly increase computation time.
累计密度Grenander校正包括累计密度凹造型的一般知识。这降低了估计的方差，并导致一个保守的估计。请注意，使用此功能可能会显着增加计算时间。
The 'multicore' package can be used to spread the computation over several cores in a simple way. This can be useful on multi-core machines for large datasets. The 'multicore' package is not available on all platforms. To use multicore processing 'library(multicore)' has to be called beforehand and a number of cores to use has to be specified in 'nCores'. For details see the documentation of the 'multicore package.
多核包可以用来散布在多个内核在一个简单的方法计算。大型数据集的多核心的机器上，这可能是有用的。 多核包是不是所有平台上都可用。使用多核处理“图书馆（多核）”被称为事前的核心数使用，必须指定nCores。有关详情，请参阅“多核包的文档。
Please note that calling 'estimate' with an object returned by the methods 'ci' and 'regions' will overwrite data stored by these two methods. This ensures that no inconsistent data is stored.
请注意，调用方法的CI和区域返回一个对象的“估计”，将覆盖这两种方法存储的数据。这将确保没有不一致的数据存储。
值----------Value----------
Object of class 'Les' with additionally filled slots: lambda, win, weighting, grenander, nProbes, se (se only if computed)
对象类的莱斯此外填补插槽：拉姆达，共赢，加权，grenander，nProbes，硒（本身只有计算）
作者（S）----------Author(s)----------
Julian Gehring
Maintainer: Julian Gehring <julian.gehring@fdm.uni-freiburg.de>
参见----------See Also----------
Package: les-package
包装：les-package
Class: Les
类别：Les
Methods and functions: Les estimate threshold regions ci chi2 export plot weighting
方法和功能：Lesestimatethresholdregionscichi2exportplotweighting
举例----------Examples----------
data(spikeInStat)
x <- Les(pos, pval)
x <- estimate(x, win=200)
x
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 2012-2-25 23:06:19
