dpik(KernSmooth)
dpik()所属R语言包：KernSmooth
Select a Bandwidth for Kernel Density Estimation
选择带宽核密度估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Use direct plug-in methodology to select the bandwidth of a kernel density estimate.
使用直接插在方法选择的内核密度估计的带宽。
用法----------Usage----------
dpik(x, scalest = "minim", level = 2L, kernel = "normal",
canonical = FALSE, gridsize = 401L, range.x = range(x),
truncate = TRUE)
参数----------Arguments----------
参数：x
vector containing the sample on which the kernel density estimate is to be constructed.
向量的内核密度估计是要构建的样本。
参数：scalest
estimate of scale. "stdev" - standard deviation is used. "iqr" - inter-quartile range divided by 1.349 is used. "minim" - minimum of "stdev" and "iqr" is used.
规模的估计。 "stdev" - 标准偏差。 "iqr" - 间四分范围由1.349分。 "minim" - "stdev"和"iqr"用于最低。
参数：level
number of levels of functional estimation used in the plug-in rule.
数量的插件在规则中使用的功能估计的水平。
参数：kernel
character string which determines the smoothing kernel. kernel can be: "normal" - the Gaussian density function (the default). "box" - a rectangular box. "epanech" - the centred beta(2,2) density. "biweight" - the centred beta(3,3) density. "triweight" - the centred beta(4,4) density. This can be abbreviated to any unique abbreviation.
字符串，这就决定了平滑的内核。 kernel能"normal" - 高斯密度函数（默认）。 "box" - 一个矩形框。 "epanech" - 中心的β（2,2）的密度。 "biweight" - 中心的β（3,3）的密度。 "triweight" - 中心的β（4,4）的密度。这可以是任何独特的缩写简称。
参数：canonical
logical flag: if TRUE, canonically scaled kernels are used
逻辑标志：如果TRUE，规范缩放内核是使用
参数：gridsize
the number of equally-spaced points over which binning is performed to obtain kernel functional approximation.
同样间隔点进行分级是获取内核函数逼近。
参数：range.x
vector containing the minimum and maximum values of x at which to compute the estimate. The default is the minimum and maximum data values.
向量x在计算估计的最小值和最大值。默认的最小和最大的数据值。
参数：truncate
logical flag: if TRUE, data with x values outside the range specified by range.x are ignored. </table>
逻辑标志：如果TRUE，x值range.x指定的范围之外的数据将被忽略。 </ TABLE>
Details
详情----------Details----------
The direct plug-in approach, where unknown functionals that appear in expressions for the asymptotically optimal bandwidths are replaced by kernel estimates, is used. The normal distribution is used to provide an initial estimate.
直接插入的方法，替换内核估计的渐近最优带宽的表达式中出现的未知函，用于。正态分布是用来提供一个初步估计。
值----------Value----------
the selected bandwidth.
选定的带宽。
背景----------Background----------
This method for selecting the bandwidth of a kernel density estimate was proposed by Sheather and Jones (1991) and is described in Section 3.6 of Wand and Jones (1995).
这种核密度估计的带宽选择方法由Sheather和琼斯（1991）提出，并在魔杖和琼斯（1995）3.6节所述。
参考文献----------References----------
A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society, Series B, 53, 683–690.
Kernel Smoothing. Chapman and Hall, London.
参见----------See Also----------
bkde, density, ksmooth
bkde，density，ksmooth
举例----------Examples----------
data(geyser, package="MASS")
x <- geyser$duration
h <- dpik(x)
est <- bkde(x, bandwidth=h)
plot(est,type="l")
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-2-16 21:36:36
