bkde2D(KernSmooth)
bkde2D()所属R语言包：KernSmooth
Compute a 2D Binned Kernel Density Estimate
计算二维分级内核的密度估计
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Returns the set of grid points in each coordinate direction, and the matrix of density estimates over the mesh induced by the grid points. The kernel is the standard bivariate normal density.
返回在每个坐标方向的网格点，网格点诱导的网格密度估计矩阵。内核是标准的二元正常密度。
用法----------Usage----------
bkde2D(x, bandwidth, gridsize = c(51L, 51L), range.x, truncate = TRUE)
参数----------Arguments----------
参数：x
a two-column matrix containing the observations from the distribution whose density is to be estimated. Missing values are not allowed.
一个两列的矩阵包含的意见，从分布的密度估计。遗漏值是不允许的。
参数：bandwidth
vector containing the bandwidth to be used in each coordinate direction.
向量使用的带宽将在每个坐标方向。
参数：gridsize
vector containing the number of equally spaced points in each direction over which the density is to be estimated.
向量在每个方向上等距点的密度是估计数。
参数：range.x
a list containing two vectors, where each vector contains the minimum and maximum values of x at which to compute the estimate for each direction. The default minimum in each direction is minimum data value minus 1.5 times the bandwidth for that direction. The default maximum is the maximum data value plus 1.5 times the bandwidth for that direction
一个列表，其中包含两个向量，每个向量都包含的最低和最高值x在计算每个方向的估计。在每个方向上的默认最小是最低的数据值减去1.5倍，这一方向的带宽。默认的最大的最大数据值加1.5倍的带宽，方向
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：如果为TRUE，用x值range.x被忽略指定的范围之外的数据。 </ TABLE>
值----------Value----------
a list containing the following components:
一个列表，包含以下组件：
参数：x1
vector of values of the grid points in the first coordinate direction at which the estimate was computed.
矢量估计计算的第一个坐标方向的格点值。
参数：x2
vector of values of the grid points in the second coordinate direction at which the estimate was computed.
在第二个坐标方向估计计算的网格点值的向量。
参数：fhat
matrix of density estimates over the mesh induced by x1 and x2. </table>
估计比x1和x2诱导网格密度矩阵。 </ TABLE>
Details
详情----------Details----------
This is the binned approximation to the 2D kernel density estimate. Linear binning is used to obtain the bin counts and the Fast Fourier Transform is used to perform the discrete convolutions. For each x1,x2 pair the bivariate Gaussian kernel is centered on that location and the heights of the kernel, scaled by the bandwidths, at each datapoint are summed. This sum, after a normalization, is the corresponding fhat value in the output.
这是2D内核密度估计的分级逼近。线性分级用于获取的bin计数和快速傅立叶变换用于执行离散卷积。对于每个x1，x2对二元高斯核中心的内核，位置和高度缩放的带宽，在每个数据点，总结。这笔款项，归一化后，相应的fhat产值。
参考文献----------References----------
Fast Computation of Multivariate Kernel Estimators. Journal of Computational and Graphical Statistics, 3, 433-445.
Kernel Smoothing. Chapman and Hall, London.
参见----------See Also----------
bkde, density, hist.
bkde，density，hist。
举例----------Examples----------
data(geyser, package="MASS")
x <- cbind(geyser$duration, geyser$waiting)
est <- bkde2D(x, bandwidth=c(0.7, 7))
contour(est$x1, est$x2, est$fhat)
persp(est$fhat)
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 21:16:55
