bkde(KernSmooth)
bkde()所属R语言包：KernSmooth
Compute a Binned Kernel Density Estimate
计算一个分级的核密度估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Returns x and y coordinates of the binned kernel density estimate of the probability density of the data.
返回x和y坐标的分级内核密度估计的概率密度的数据。
用法----------Usage----------
bkde(x, kernel = "normal", canonical = FALSE, bandwidth,
gridsize = 401L, range.x, truncate = TRUE)
参数----------Arguments----------
参数：x
vector of observations from the distribution whose density is to be estimated. Missing values are not allowed.
从分布的密度估计的观测向量。遗漏值是不允许的。
参数：bandwidth
the kernel bandwidth smoothing parameter. Larger values of bandwidth make smoother estimates, smaller values of bandwidth make less smooth estimates. The default is a bandwidth computed from the variance of x, specifically the "oversmoothed bandwidth selector" of Wand and Jones (1995, page 61).
内核带宽平滑参数。 bandwidth值越大使平滑的估计，小bandwidth不太顺利的估计值。默认是从x，特别是“oversmoothed带宽选择”魔杖和琼斯（1995年，第61页）的方差计算带宽。
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，规范扩展的内核使用。
参数：gridsize
the number of equally spaced points at which to estimate the density.
等距点估计的密度。
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, extended by the support of the kernel.
向量x在计算估计的最小值和最大值。默认是最大和最小的数据值，由内核支持延长。
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----------
This is the binned approximation to the ordinary kernel density estimate. Linear binning is used to obtain the bin counts. For each x value in the sample, the kernel is centered on that x and the heights of the kernel at each datapoint are summed. This sum, after a normalization, is the corresponding y value in the output.
这是普通的内核密度估计的分级逼近。线性分级用于获取的bin计数。对于每x样品中的价值，核心是围绕x“内核在每个数据点的高度概括。这笔款项，归一化后，相应的y产值。
值----------Value----------
a list containing the following components:
一个列表，包含以下组件：
参数：x
vector of sorted x values at which the estimate was computed.
向量排序x估计是计算值。
参数：y
vector of density estimates at the corresponding x. </table>
密度矢量估计，在相应的x。 </ TABLE>
背景----------Background----------
Density estimation is a smoothing operation. Inevitably there is a trade-off between bias in the estimate and the estimate's variability: large bandwidths will produce smooth estimates that may hide local features of the density; small bandwidths may introduce spurious bumps into the estimate.
密度估计是一个平滑的操作。必然是有偏见的估计和估计的变异之间的取舍：大带宽将产生平滑的估计，可能隐藏着地方特色的密度小的带宽估计可能引入的杂散颠簸。
参考文献----------References----------
Kernel Smoothing. Chapman and Hall, London.
参见----------See Also----------
density, dpik, hist, ksmooth.
density，dpik，hist，ksmooth。
举例----------Examples----------
data(geyser, package="MASS")
x <- geyser$duration
est <- bkde(x, bandwidth=0.25)
plot(est, type="l")
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 2012-2-16 20:41:44
