bandwidth(stats)
bandwidth()所属R语言包：stats
Bandwidth Selectors for Kernel Density Estimation
核密度估计的带宽选择器
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Bandwidth selectors for Gaussian kernels in density.
density高斯内核的带宽选择。
用法----------Usage----------
bw.nrd0(x)
bw.nrd(x)
bw.ucv(x, nb = 1000, lower = 0.1 * hmax, upper = hmax, tol = 0.1 * lower)
bw.bcv(x, nb = 1000, lower = 0.1 * hmax, upper = hmax, tol = 0.1 * lower)
bw.SJ(x, nb = 1000, lower = 0.1 * hmax, upper = hmax,
method = c("ste", "dpi"), tol = 0.1 * lower)
参数----------Arguments----------
参数：x
numeric vector.
数字向量。
参数：nb
number of bins to use.
使用的箱数。
参数：lower, upper
range over which to minimize. The default is almost always satisfactory. hmax is calculated internally from a normal reference bandwidth.
射程超过，以尽量减少。默认是几乎总是令人满意。 hmax内部计算从一个正常参考带宽。
参数：method
either "ste" ("solve-the-equation") or "dpi" ("direct plug-in").
要么"ste"（“解决的方程”）或"dpi"（“直接插入式”）。
参数：tol
for method "ste", the convergence tolerance for uniroot. The default leads to bandwidth estimates with only slightly more than one digit accuracy, which is sufficient for practical density estimation, but possibly not for theoretical simulation studies.
方法"ste"，uniroot收敛公差。默认情况下会导致带宽估计仅略高于一个数字的准确性，实际密度估计是足够的，但可能不为理论模拟研究。
Details
详情----------Details----------
bw.nrd0 implements a rule-of-thumb for choosing the bandwidth of a Gaussian kernel density estimator. It defaults to 0.9 times the minimum of the standard deviation and the interquartile range divided by 1.34 times the sample size to the negative one-fifth power (= Silverman's "rule of thumb", Silverman (1986, page 48, eqn (3.31)) unless the quartiles coincide when a positive result will be guaranteed.
bw.nrd0实现选择高斯核密度估计的带宽规则的拇指。它默认为0.9倍，最低的标准差和四分位距除以1.34倍的样本大小负五分之一的电力（=西尔弗曼的“拇指规则”，西尔弗曼（1986年，第48页，式（3.31） ），除非四分相吻合时一个积极的结果将得到保证。
bw.nrd is the more common variation given by Scott (1992), using factor 1.06.
bw.nrd是较常见的斯科特（1992年）的变化，利用系数1.06。
bw.ucv and bw.bcv implement unbiased and biased cross-validation respectively.
bw.ucv和bw.bcv实现公正的和有偏见的交叉验证。
bw.SJ implements the methods of Sheather & Jones (1991) to select the bandwidth using pilot estimation of derivatives.<br> The algorithm for method "ste" solves an equation (via uniroot) and because of that, enlarges the interval c(lower,upper) when the boundaries were not user-specified and do not bracket the root.
bw.SJ实现的Sheather - 琼斯（1991）的方法来选择使用衍生工具的试点估计的带宽参考方法"ste"算法解决了一个等式（通过uniroot）正因为如此，扩大间隔c(lower,upper)当边界不是用户指定的，并没有支架的根。
值----------Value----------
A bandwidth on a scale suitable for the bw argument of density.
一个规模为bwdensity参数适合的带宽。
参考文献----------References----------
Multivariate Density Estimation: Theory, Practice, and Visualization. Wiley.
A reliable data-based bandwidth selection method for kernel density estimation. Journal of the Royal Statistical Society series B, 53, 683–690.
Density Estimation. London: Chapman and Hall.
Modern Applied Statistics with S. Springer.
参见----------See Also----------
density.
density。
bandwidth.nrd, ucv, bcv and width.SJ in package MASS, which are all scaled to the width argument of density and so give answers four times as large.
bandwidth.nrd，ucv，bcv和width.SJ包MASS，这是所有缩放widthdensity参数所以给出答案的4倍。
举例----------Examples----------
require(graphics)
plot(density(precip, n = 1000))
rug(precip)
lines(density(precip, bw="nrd"), col = 2)
lines(density(precip, bw="ucv"), col = 3)
lines(density(precip, bw="bcv"), col = 4)
lines(density(precip, bw="SJ-ste"), col = 5)
lines(density(precip, bw="SJ-dpi"), col = 6)
legend(55, 0.035,
legend = c("nrd0", "nrd", "ucv", "bcv", "SJ-ste", "SJ-dpi"),
col = 1:6, lty = 1)
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 19:09:29
