wle.onestep(wle)
wle.onestep()所属R语言包：wle
A One-Step Weighted Likelihood Estimator for Linear model
一个单步的加权似然估计的线性模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function evaluate the One-step weighted likelihood estimator for the regression and scale parameters.
此功能评估的一步加权似然估计的回归和比例参数。
用法----------Usage----------
wle.onestep(formula, data=list(), model=TRUE, x=FALSE,
y=FALSE, ini.param, ini.scale, raf="HD",
smooth=0.031, num.step=1,
contrasts=NULL, verbose=FALSE)
参数----------Arguments----------
参数：formula
a symbolic description of the model to be fit. The details of model specification are given below.
一个象征性的模型来描述是合适的。模型规范的细节在下面给出。
参数：data
an optional data frame containing the variables in the model. By default the variables are taken from the environment which wle.stepwise is called from.
一个可选的数据框包含在模型中的变量。默认情况下，变量是从wle.stepwise被称为从环境。
参数：model, x, y
logicals. If TRUE the corresponding components of the fit (the model frame, the model matrix, the response.)
的逻辑。如果TRUE拟合的相应部件（模型框架，模型矩阵，响应。）
参数：ini.param
starting values for the coefficients.
开始的系数的值。
参数：ini.scale
starting values for the scale parameters.
开始的规模参数的值。
参数：raf
type of Residual adjustment function to be use:
类型的残余调节功能，可以使用：
raf="HD": Hellinger Distance RAF,
raf="HD"：Hellinger距离RAF，
raf="NED": Negative Exponential Disparity RAF,
raf="NED"：负指数差异RAF，
raf="SCHI2": Symmetric Chi-Squared Disparity RAF.
raf="SCHI2"：对称卡方差异皇家空军。
参数：smooth
the value of the smoothing parameter.
的平滑化参数的值。
参数：num.step
number of the steps.
的步骤数目。
参数：contrasts
an optional list. See the contrasts.arg of model.matrix.default.
可选列表。请参阅contrasts.argmodel.matrix.default。
参数：verbose
if TRUE warnings are printed.
如果TRUE警告被打印出来。
值----------Value----------
wle.onestep returns an object of class "wle.onestep".
wle.onestep返回一个对象的class"wle.onestep"的。
Only print method is implemented for this class.
只打印的方法来实现这个类。
The object returned by wle.onestep are:
对象返回wle.onestep是：
<table summary="R valueblock"> <tr valign="top"><td>coefficients</td> <td> the parameters estimator.</td></tr> <tr valign="top"><td>standard.error</td> <td> an estimation of the standard error of the parameters estimator.</td></tr> <tr valign="top"><td>scale</td> <td> an estimation of the error scale.</td></tr> <tr valign="top"><td>residuals</td> <td> the unweighted residuals from the estimated model.</td></tr> <tr valign="top"><td>fitted.values</td> <td> the fitted values from the estimated model.</td></tr> <tr valign="top"><td>tot.weights</td> <td> the sum of the weights divide by the number of observations.</td></tr> <tr valign="top"><td>weights</td> <td> the weights associated to each observation.</td></tr> <tr valign="top"><td>f.density</td> <td> the non-parametric density estimation.</td></tr> <tr valign="top"><td>m.density</td> <td> the smoothed model.</td></tr> <tr valign="top"><td>delta</td> <td> the Pearson residuals.</td></tr> <tr valign="top"><td>call</td> <td> the match.call().</td></tr> <tr valign="top"><td>contrasts</td> <td> </td></tr> <tr valign="top"><td>xlevels</td> <td> </td></tr> <tr valign="top"><td>terms</td> <td> the model frame.</td></tr> <tr valign="top"><td>model</td> <td> if model=TRUE a matrix with first column the dependent variable and the remain column the explanatory variables for the full model.</td></tr> <tr valign="top"><td>x</td> <td> if x=TRUE a matrix with the explanatory variables for the full model.</td></tr> <tr valign="top"><td>y</td> <td> if y=TRUE a vector with the dependent variable.</td></tr>
<table summary="R valueblock"> <tr valign="top"> <TD> coefficients</ TD> <TD>的参数估计。</ TD> </ TR> <TR VALIGN =“顶部“> <TD> standard.error </ TD> <TD>参数估计的标准误差的估计。</ TD> </ TR> <tr valign="top"> <TD><X > </ TD> <TD>的错误规模的估计。</ TD> </ TR> <tr valign="top"> <TD> scale</ TD> <TD>的未加权残差从模型的估计。</ TD> </ TR> <tr valign="top"> <TD> residuals</ TD> <TD>估计模型的拟合值。</ TD> </ TR> <tr valign="top"> <TD> fitted.values</ TD> <TD>的权重总和除以观测值的数量。</ TD> </ TR> <TR VALIGN =“顶“<TD> tot.weights </ TD> <TD>相关的权重给每个观察。</ TD> </ TR> <tr valign="top"> <TD>weights / TD> <TD>非参数密度估计。</ TD> </ TR> <tr valign="top"> <TD>f.density </ TD> <TD>平滑模型。</ TD> </ TR> <tr valign="top"> <TD>m.density </ TD> <TD> Pearson残差。</ TD> </ TR> <tr valign="top"> < delta TD> </ TD> <TD> match.call（）。</ TD> </ TR> <tr valign="top"> <TD> call</ TD> < TD> </ TD> </ TR> <tr valign="top"> <TD>contrasts </ TD> <TD> </ TD> </ TR> <tr valign="top"> < xlevels TD> </ TD> <TD>的模型框架。</ TD> </ TR> <tr valign="top"> <TD> terms</ TD> <td>如果model与第一列中的因变量和列的完整模型的解释变量矩阵。</ TD> </ TR> <tr valign="top"> <TD>model=TRUE / TD> <TD>如果x矩阵的完整模型的解释变量。</ TD> </ TR> <tr valign="top"> <TD>x=TRUE</ TD > <TD>如果y与因变量的向量。</ TD> </ TR>
</table>
</ TABLE>
（作者）----------Author(s)----------
Claudio Agostinelli
参考文献----------References----------
Agostinelli, C., (1997) A one-step robust estimator based on the weighted likelihood methodology, Working Paper n. 1997.16, Department of Statistics, University of Padova.
Agostinelli, C., (1998) Inferenza statistica robusta basata sulla funzione di verosimiglianza pesata: alcuni sviluppi, Ph.D Thesis, Department of Statistics, University of Padova.
Agostinelli, C., Markatou, M., (1998) A one-step robust estimator for regression based on the weighted likelihood reweighting scheme, Statistics \& Probability Letters, Vol. 37, n. 4, 341-350.
Agostinelli, C., (1998) Verosimiglianza pesata nel modello di regressione lineare, XXXIX Riunione scientifica della Societ\'a Italiana di Statistica, Sorrento 1998.
参见----------See Also----------
wle.smooth an algorithm to choose the smoothing parameter for normal distribution and normal kernel, wle.lm a function for estimating linear models with normal distribution error and normal kernel.
wle.smooth算法选择平滑参数正常分布和正态分布的内核，wle.lm估计线性模型与正常分配错误和正常的内核的功能。
实例----------Examples----------
#library(wle)[库（WLE）]
#library(lqs)[库（LQS）]
#data(artificial)[数据（人工）]
#result.lts <- lqs(y.artificial~x.artificial, [result.lts < - 的LQS（y.artificial~x.artificial，]
# method = "lts")[方法=“LTS”）]
#result.wle <- wle.onestep(y.artificial~x.artificial,[result.wle < - wle.onestep（y.artificial~x.artificial：]
# ini.param=result.lts$coefficients,[ini.param = result.lts系数，]
# ini.scale=result.lts$scale[1])[ini.scale = result.lts规模[1]）]
#result.wle[result.wle]
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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 2012-10-1 22:41:32
