h.select(sm)
h.select()所属R语言包：sm
Selection of the smoothing parameter
平滑参数的选择
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function selects a smoothing parameter for density estimation in one or two dimensions and for nonparametric regression with one or two covariates. Several methods of selection are available.
该功能选择平滑参数密度估计在一维或二维和非参数回归与一个或两个协变量。选择的几种方法是可用的。
用法----------Usage----------
h.select(x, y = NA, weights = NA, group = NA, ...)
参数----------Arguments----------
参数：x
a vector, or two-column matrix.
的向量，或两列的矩阵。
参数：y
a vector of reponses, in regression case.
反应变量的矢量，在回归情况下。
参数：weights
a vector of integers representing frequencies of individual observations. Use of this parameter is incompatible with binning; hence nbins must then be set to 0 or left at its default value NA.
一个向量整数，代表个人的意见的频率。使用此参数是不符合binning，，因此nbins然后被设置为0或留在它的默认值NA。
参数：group
a vector of groups indicators (numeric or character values) or a factor
组的指标（数字或字符的值）或一个因素的矢量
参数：...
other optional parameters are passed to the sm.options function, through a mechanism which limits their effect only to this call of the function. There are three which are relevant for this function, namely method, which specifies the method of smoothing parameter selection, df, which specifies the approximate degrees of freedom associated with the selected smoothing parameter, and structure.2d which determines the form of the smoothing parameters in the two-dimensional case. A full description of these arguments are given in the documentation of sm.options. </table>
其他可选的参数传递给sm.options功能，通过一种机制，限制了它们的作用仅此呼叫的功能。这是相关的这个函数有三个，即method，它指定的平滑参数选择方法，df，其中指定的近似与所选的平滑参数相关的自由度，和<X >在两维的情况下确定的平滑化参数的形式。这些参数的完整描述给定的中的文档structure.2d。 </ TABLE>
Details
详细信息----------Details----------
see the two references below for discussion of the methods of smoothing parameter selection.
看到两个参考下面讨论的平滑参数选择的方法。
If the sample size is large, binning will be employed. In the case of method = "cv" the answer will therefore be different from that obtained through the function hcv where binning is not used.
如果样本量较大时，将采用分级。在箱子method = "cv"的答案将因此从该通过函数hcv不使用其中像素合并得到的是不同的。
When the group argument is set, the chosen method of smoothing parameter selection is applied to each group and the value returned is the geometric mean of these. This is intended for use in sm.density.compare and sm.ancova, where the same smoothing parameter is used for all groups so that the principal bias terms cancel when the estimates are compared.
当group参数设置平滑参数的选择，选择的方法是适用于每个组和这些返回值的几何平均值。这样做的目的，用于sm.density.compare和sm.ancova，其中用于所有组的主要偏置项取消时，估计是比较相同的平滑参数。
值----------Value----------
the value of the selected smoothing parameter.
选定的平滑参数的值。
副作用----------Side Effects----------
none
没有
参考文献----------References----------
Bowman, A.W. and Azzalini, A. (1997). Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford University Press, Oxford.
Hurvich, C.M., Simonoff, J.S. and Tsai, C.-L. (1998). Smoothing parameter selection in nonparametric regression using an improved Akaike information criterion. J. R. Statistic. Soc., Series B, 60, 271-293.
参见----------See Also----------
sm, hcv, hsj, hnorm
sm，hcv，hsj，hnorm
实例----------Examples----------
x <- rnorm(50)
h.select(x)
h.select(x, method = "sj")
x <- matrix(rnorm(100), ncol = 2)
h.select(x)
sm.density(x, method = "cv")
x <- rnorm(50)
y <- x^2 + rnorm(50)
h.select(x, y)
sm.regression(x, y, method = "aicc")
x <- matrix(rnorm(100), ncol = 2)
y <- x[,1]^2 + x[,2]^2 + rnorm(50)
h.select(x, y, method = "cv", structure.2d = "common")
sm.regression(x, y, df = 8)
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。
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发表于 2012-9-30 10:44:21
