predict.smooth.Pspline(pspline)
predict.smooth.Pspline()所属R语言包：pspline
Smoothing Spline of Arbitrary Order at New Data
在新的数据任意阶样条函数
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Uses an object of class "smooth.Pspline" to evaluate a polynomial smoothing spline of arbitrary order or one of its derivatives at new argument values.
使用对象的类"smooth.Pspline"任意命令或它的衍生新的参数值来评估一个多项式样条。
用法----------Usage----------
## S3 method for class 'smooth.Pspline':
predict(object, xarg, nderiv = 0, ...)
参数----------Arguments----------
参数：object
a fitted smooth.Pspline object.
一个装有smooth.Pspline对象。
参数：xarg
the argument values at which the spline or its derivative is to be evaluated.
的参数值，该样条曲线或其衍生物以进行评价。
参数：nderiv
the order of the derivative required – the default is 0, the function itself.
衍生工具所需的顺序 - 默认值是0，函数本身。
参数：...
further arguments passed to or from other methods.
进一步的参数传递给其他方法。
Details
详细信息----------Details----------
The method produces results similar to function the predict method for smooth.spline, but the smoothing function is a natural smoothing spline rather than a B-spline smooth, and the order of the spline can be chosen freely, where order in this case means the order of the derivative that is penalized. smooth.spline penalizes the second derivative, and consequently only derivatives or order 0 or 1 are useful, but because smooth.Pspline penalizes a derivative of order m, derivatives up to order m-1 are useful. The general recommendation is to penalize the derivative two beyond the highest order derivative to be evaluated.
类似功能的predict smooth.spline方法的方法产生的结果，但平滑函数是一个自然的，而不是一个B样条光滑样条，样条曲线的顺序可以自由选择，其中为了在这种情况下，是指被处罚的衍生工具的顺序。 smooth.spline惩罚的二阶导数，因此只衍生工具或命令0或1是有用的，但是，因为“smooth.Pspline惩罚m阶衍生工具，衍生工具订购M-1是有用的。一般的建议是惩罚衍生超越最高阶导数进行评估。
值----------Value----------
A list with components xarg and dy; the xarg component is identical to the input xarg sequence, the dy component is the evaluated derivative of order deriv.
与组件的列表xarg和dy;xarg组分是相同的输入xarg序列，dy组分是评价衍生物为了deriv。
参考文献----------References----------
Heckman, N. and Ramsay, J. O. (1996) Spline smoothing with model based penalties. McGill University, unpublished manuscript.
参见----------See Also----------
smooth.Pspline
smooth.Pspline
实例----------Examples----------
example(smooth.Pspline)
## smoother line is given by[＃平滑线由下式给出]
xx <- seq(4, 25, length=100)
lines(xx, predict(sm.spline(speed, dist, df=5), xx), col = "red")
## add plots of derivatives[＃添加图的衍生工具。]
lines(xx, 10*predict(sm.spline(speed, dist), xx, 1), col = "blue")
lines(xx, 100*predict(sm.spline(speed, dist), xx, 2), col = "green")
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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-9-24 19:49:22
