SiZer(SiZer)
SiZer()所属R语言包：SiZer
Calculate SiZer Map
计算SIZER图
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Calculates the SiZer map from a given set of X and Y variables.
从一个给定的一组X和Y变量计算SIZER图。
用法----------Usage----------
SiZer(x, y, h=NA, x.grid=NA, degree=NA, derv=1, grid.length=41)
参数----------Arguments----------
参数：x
data vector for the independent axis
独立轴矢量数据
参数：y
data vector for the dependent axis
依赖轴矢量数据
参数：h
An integer representing how many bandwidths should be considered, or vector of length 2 representing the upper and lower limits h should take, or a vector of length greater than two indicating which bandwidths to examine.
一个整数，代表被认为多少带宽，或向量，长度为2代表h应采取的上限和下限，或者一个向量的长度更大的比2表示检查的带宽。
参数：x.grid
An integer representing how many bins to use along the x-axis, or a vector of length 2 representing the upper and lower limits the x-axis should take, or a vector of length greater than two indicating which x-values the derivative should be evaluated at.
一个整数，代表使用沿着x轴，或一个矢量代表的x-轴应该采取的上限和下限，或比2表示的x值的衍生物应该是一个向量的长度更大的长度为2的多少箱评估。
参数：grid.length
The default length of the h.grid or x.grid if the length of either is not given.
默认长度为h.grid或x.grid要么是给定的，如果长度。
参数：derv
The order of derivative for which to make the SiZer map.
秩序衍生工具使SIZER的图。
参数：degree
The degree of the local weighted polynomial used to smooth the data. This must be greater than or equal to derv.
用于平滑的数据的局部加权多项式的程度。这必须是大于或等于derv。
Details
详细信息----------Details----------
SiZer stands for the Significant Zero crossings of the derivative. There are two dominate approaches in smoothing bivariate data: locally weighted regression or penalized splines. Both approaches require the use of a 'bandwidth' parameter that controls how much smoothing should be done. Unfortunately there is no uniformly best bandwidth selection procedure. SiZer (Chaudhuri and Marron, 1999) is a procedure that looks across a range of bandwidths and classifies the p-th derivative of the smoother into one of three states: significantly increasing (blue), possibly zero (purple), or significantly negative (red).
SIZER代表了显着的零交叉点的导数。二元数据：局部加权回归或惩罚样条平滑有两个占主导地位的方法。这两种方法都需要使用“带宽”参数，控制多少平滑应该做的。不幸的是，没有统一的最佳选择带宽程序。 SIZER（Chaudhuri和马伦，1999年）是一种程序，看起来横跨带宽的范围内，并分类成三种状态之一的第p个衍生物的平滑：显著增加（蓝色），可能为零（紫色），或显著负（红色）。
值----------Value----------
Returns an SiZer object which has the following components: <table summary="R valueblock"> <tr valign="top"><td>x.grid</td> <td> Vector of x-values at which the derivative was evaluated.</td></tr> <tr valign="top"><td>h.grid</td> <td> Vector of bandwidth values for which a smoothing function was calculated.</td></tr> <tr valign="top"><td>slopes</td> <td> Matrix of what category a particular x-value and bandwidth falls into (Increasing=1, Possibly Zero=0, Decreasing=-1, Not Enough Data=2).</td></tr> </table>
返回一个的SIZER对象具有以下组件：<table summary="R valueblock"> <tr valign="top"> <TD>x.grid </ TD> <TD>向量的x值在其中衍生性进行了评估。</ TD> </ TR> <tr valign="top"> <TD>h.grid </ TD> <TD>向量的带宽值的平滑函数的计算结果。</ TD > </ TR> <tr valign="top"> <TD> slopes</ TD> <TD>矩阵的哪一类特定的x值和带宽分为（增加= 1，可能是零= 0 ，减少= -1，没有足够的数据，= 2）。</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Derek Sonderegger
参考文献----------References----------
Chaudhuri, P., and J. S. Marron. 1999. SiZer for exploration of structures in curves. Journal of the American Statistical Association 94:807-823.
Hannig, J., and J. S. Marron. 2006. Advanced distribution theory for SiZer. Journal of the American Statistical Association 101:484-499.
Sonderegger, D.L., Wang, H., Clements, W.H., and Noon, B.R. 2009. Using SiZer to detect thresholds in ecological data. Frontiers in Ecology and the Environment 7:190-195.
参见----------See Also----------
plot.SiZer, locally.weighted.polynomial
plot.SiZer，locally.weighted.polynomial
实例----------Examples----------
data('Arkansas')
x <- Arkansas$year
y <- Arkansas$sqrt.mayflies
plot(x,y)
# Calculate the SiZer map for the first derivative[计算的一阶导数的图为SIZER]
SiZer.1 <- SiZer(x, y, h=c(.5,10), degree=1, derv=1)
plot(SiZer.1)
# Calculate the SiZer map for the second derivative[计算SIZER图的二阶导数]
SiZer.2 <- SiZer(x, y, h=c(.5,10), degree=2, derv=2);
plot(SiZer.2)
# By setting the grid.length larger, we get a more detailed SiZer[通过设置grid.length大，我们获得更详细的SIZER]
# map but it takes longer to compute. [图，但它需要更长的时间来计算。]
#[]
# SiZer.3 <- SiZer(x, y, h=c(.5,10), grid.length=100, degree=1, derv=1)[SiZer.3 < - SIZER（的x，y，H = C（.5,10），grid.length = 100度= 1，柴油= 1）]
# plot(SiZer.3)[图（SiZer.3）]
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 09:48:44
