wle.lm(wle)
wle.lm()所属R语言包：wle
Fitting Linear Models using Weighted Likelihood
配件线性模型的加权似然
译者：生物统计家园网 机器人LoveR
描述----------Description----------
wle.lm is used to fit linear models via Weighted Likelihood, when the errors are iid from a normal distribution with null mean and unknown variance. The carriers are considered fixed. Note that this estimator is robust against the presence of bad leverage points too.
wle.lm通过加权似然，当误差是独立同分布的零均值和方差未知的正态分布，线性模型来拟合。被认为是固定运营商。需要注意的是这个估计是稳健的，对坏的平衡点的存在。
用法----------Usage----------
wle.lm(formula, data=list(), model=TRUE, x=FALSE,
y=FALSE, boot=30, group, num.sol=1, raf="HD",
smooth=0.031, tol=10^(-6), equal=10^(-3),
max.iter=500, 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.lm 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.lm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：boot
the number of starting points based on boostrap subsamples to use in the search of the roots.
基于自举子样本的起点，使用在搜索的根的数目。
参数：group
the dimension of the bootstap subsamples. The default value is max(round(size/4),var) where size is the number of observations and var is the number of variables.
的维度的bootstap子样本。默认值是max(round(size/4),var)size的一些意见和var是变量的数目。
参数：num.sol
maximum number of roots to be searched.
要搜索的最大根数。
参数：raf
type of Residual adjustment function to be used:
类型的残余调节功能被使用：
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：tol
the absolute accuracy to be used to achieve convergence of the algorithm.
要使用的绝对精度实现算法的收敛性。
参数：equal
the absolute value for which two roots are considered the same. (This parameter must be greater than tol).
绝对的值，两个根被认为是相同的。 （此参数必须大于tol）。
参数：max.iter
maximum number of iterations.
最大迭代次数。
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。
Details
详细信息----------Details----------
Models for wle.lm are specified symbolically. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first+second indicates all the terms in first together with all the terms in second with duplicates removed. A specification of the form first:second indicates the the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first+second+first:second.
模型wle.lm的符号。典型的模型形式response ~ terms其中response是响应向量（数字）和terms是一系列的条款，指定一个线性预测response。一个术语规范的形式first+second表示first一起在second重复删除的所有条款中的所有条款。一个规范的形式first:second的表示的术语集firstsecond的所有条款的相互作用的所有条款。规格first*second表明first和second交叉的。这是相同first+second+first:second。
值----------Value----------
wle.lm returns an object of class "wle.lm".
wle.lm返回一个对象的class"wle.lm"的。
The function summary is used to obtain and print a summary of the results. The generic accessor functions coefficients, residuals and fitted.values extract coefficients, residuals and fitted values returned by wle.lm.
函数summary用于获取和打印结果的摘要。一般的访问功能coefficients，residuals和fitted.values的提取系数，残差和拟合值返回wle.lm。
The object returned by wle.lm are:
对象返回wle.lm是：
<table summary="R valueblock"> <tr valign="top"><td>coefficients</td> <td> the parameters estimator, one row vector for each root found.</td></tr> <tr valign="top"><td>standard.error</td> <td> an estimation of the standard error of the parameters estimator, one row vector for each root found.</td></tr> <tr valign="top"><td>scale</td> <td> an estimation of the error scale, one value for each root found.</td></tr> <tr valign="top"><td>residuals</td> <td> the unweighted residuals from the estimated model, one column vector for each root found.</td></tr> <tr valign="top"><td>fitted.values</td> <td> the fitted values from the estimated model, one column vector for each root found.</td></tr> <tr valign="top"><td>tot.weights</td> <td> the sum of the weights divide by the number of observations, one value for each root found.</td></tr> <tr valign="top"><td>weights</td> <td> the weights associated to each observation, one column vector for each root found.</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>freq</td> <td> the number of starting points converging to the roots.</td></tr> <tr valign="top"><td>tot.sol</td> <td> the number of solutions found.</td></tr> <tr valign="top"><td>not.conv</td> <td> the number of starting points that does not converge after the max.iter iterations are reached.</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> <tr valign="top"><td>info</td> <td> not well working yet, if 0 no error occurred.</td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD> coefficients</ TD> <TD>的参数估计，每一根发现一个行向量。</ TD> </ TR> <tr valign="top"> <TD> standard.error </ TD> <TD>的参数估计的标准误差的估计，每一根发现一个行向量。</ TD> </ TR> <tr valign="top"> <TD> scale</ TD> <TD>的错误规模的估计，每根发现的一个值。</ TD> </ TR> <TR VALIGN =“”> <TD>residuals </ TD> <TD>未加权的估计模型的残差，每一根发现一个列向量。</ TD> </ TR> <TR VALIGN =“顶部“> <TD> fitted.values </ TD> <TD>估计模型的拟合值，每一根发现一个列向量。</ TD> </ TR> <tr valign="top"> < tot.weights TD> </ TD> <TD>的权重总和除以观测值的数量，每根发现的一个值。</ TD> </ TR> <tr valign="top"> < weights TD> </ TD> <TD>相关的权重给每个观察，每一根发现一个列向量。</ TD> </ TR> <tr valign="top"> <TD> X> </ TD> <TD>非参数密度估计。</ TD> </ TR> <tr valign="top"> <TD>f.density </ TD> <TD>的平滑模型。</ TD> </ TR> <tr valign="top"> <TD>m.density</ TD> <TD> Pearson残差。</ TD> </ TR> <TR VALIGN =“顶“<TD> delta </ TD> <TD>收敛的根源出发点的数量。</ TD> </ TR> <tr valign="top"> <TD><X > </ TD> <TD>找到解决方案的数量。</ TD> </ TR> <tr valign="top"> <TD>freq </ TD> <TD>数的开始点tot.sol迭代不收敛后到达。</ TD> </ TR> <tr valign="top"> <TD> not.conv</ TD> <TD>的比赛。叫（）。</ TD> </ TR> <tr valign="top"> <TD>max.iter </ TD> <TD> </ TD> </ TR> <TR VALIGN =“顶” > <TD> call </ TD> <TD> </ TD> </ TR> <tr valign="top"> <TD> contrasts</ TD> <TD>的模型框架。</ TD> </ TR> <tr valign="top"> <TD> xlevels </ TD> <TD>如果terms矩阵与第一列中的因变量和保持列的是整个模型的解释变量。</ TD> </ TR> <tr valign="top"> <TD> model </ TD> <TD>如果model=TRUE矩阵的解释的完整模型的变量。</ TD> </ TR> <tr valign="top"> <TD> x </ TD> <TD>如果x=TRUE与因变量的向量。 </ TD> </ TR> <tr valign="top"> <TD>y </ TD> <TD>还不能很好地工作，如果没有错误发生。</ TD> </ TR> </ TABLE>
（作者）----------Author(s)----------
Claudio Agostinelli
参考文献----------References----------
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.smooth一个算法来选择平滑参数的正常分布和正态分布内核。
实例----------Examples----------
library(wle)
# You can find this data set in:[你可以找到这个数据集：]
# Hawkins, D.M., Bradu, D., and Kass, G.V. (1984). [霍金斯位于Bradu，D.M.，，D.，卡斯，G.V.的（1984）。]
# Location of several outliers in multiple regression data using[在多元回归分析数据使用的几个离群的位置]
# elemental sets. Technometrics, 26, 197-208.[元素集。品质管理，26，197-208。]
#[]
data(artificial)
result <- wle.lm(y.artificial~x.artificial,boot=40,num.sol=3)
summary(result)
plot(result)
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注：
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发表于 2012-10-1 22:40:12
