plot.LocallyWeightedPolynomial(SiZer)
plot.LocallyWeightedPolynomial()所属R语言包:SiZer
Plot a LocallyWeightedPolynomial object
绘制一个LocallyWeightedPolynomial对象
译者:生物统计家园网 机器人LoveR
描述----------Description----------
Creates a plot of an object created by locally.weighted.polynomial.
创建一个图的locally.weighted.polynomial创建的对象。
用法----------Usage----------
## S3 method for class 'LocallyWeightedPolynomial'
plot(x, derv = 0, CI.method = 2,
alpha = 0.05, use.ess = TRUE, draw.points = TRUE, ...)
参数----------Arguments----------
参数:x
LocallyWeightedPolynomial object
LocallyWeightedPolynomial对象
参数:derv
Derivative to be plotted. Default is 0 - which plots the smoothed function.
衍生以被绘制。默认值是0 - 绘制平滑的功能。
参数:CI.method
What method should be used to calculate the confidence interval about the estimated line. The methods are from Hannig and Marron (2006), where 1 is the point-wise estimate, and 2 is the row-wise estimate.
应使用什么样的方法有关的估计行计算置信区间。该方法是从Hannig和马伦(2006),其中1是逐点的估计,和图2是在行的估计。
参数:alpha
The CI has a 1-alpha/2 level of significance.
CI有显着性水平为1-α/ 2。
参数:use.ess
ESS stands for the estimated sample size. If at any point along the x-axis, the ESS is too small, then we will not plot unless use.ess=FALSE.
ESS代表估计的样本大小。如果在任何点沿x轴,ESS是太小的,那么我们就不会绘制,除非use.ess = FALSE。
参数:draw.points
Should the data points be included in the graph?
应被包括在图中的数据点?
参数:...
Additional arguments to be passed to the graphing functions.
其他的参数被传递到绘图功能。
(作者)----------Author(s)----------
Derek Sonderegger
参考文献----------References----------
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----------
locally.weighted.polynomial
locally.weighted.polynomial
实例----------Examples----------
data('Arkansas')
x <- Arkansas$year
y <- Arkansas$sqrt.mayflies
model <- locally.weighted.polynomial(x,y)
plot(model)
model <- locally.weighted.polynomial(x,y,degree=2)
plot(model, derv=1)
plot(model, derv=2)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注:
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