R语言 wle包 mle.stepwise()函数中文帮助文档(中英文对照)

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mle.stepwise(wle)
mle.stepwise()所属R语言包：wle

                                        Stepwise, Backward and Forward selection methods
                                         逐步，向后和向前的选择方法

                                         译者：生物统计家园网 机器人LoveR

描述----------Description----------

This function performs Stepwise, Forward and Backward model selection.
此功能进行逐步，前进和后退的模式选择。


----------Usage----------


mle.stepwise(formula, data=list(), model=TRUE, x=FALSE,
             y=FALSE, type="Forward", f.in=4.0, f.out=4.0,
             contransts=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 mle.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mle.stepwise被称为从从环境。


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：type
type="Stepwise": the stepwise methods is used,
type="Stepwise"：逐步的方法，用于

type="Forward": the forward methods is used,
type="Forward"：forward方法被使用时，

type="Backward": the backward method is used.
type="Backward"：使用落后的方法。


参数：f.in
the in value
价值


参数：f.out
the out value
超时值


参数：contransts
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 mle.stepwise 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mle.stepwise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----------

mle.stepwise returns an object of class "mle.stepwise".
mle.stepwise返回一个对象的class"mle.stepwise"的。

The function summary is used to obtain and print a summary of the results.
函数summary用于获取和打印结果的摘要。

The object returned by mle.stepwise are:
对象返回mle.stepwise是：

<table summary="R valueblock"> <tr valign="top"><td>step</td> <td> the selected models</td></tr> <tr valign="top"><td>type</td> <td> the type o model selection procedure was used.</td></tr> <tr valign="top"><td>f.in</td> <td> the value of f.in used.</td></tr> <tr valign="top"><td>f.out</td> <td> the value of f.out used.</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> step</ TD> <TD>选定的模型</ TD> </ TR> <TR VALIGN =“顶” > <TD> type </ TD> <TD> O型模型的选择过程。</ TD> </ TR> <tr valign="top"> <TD>f.in / TD <TD> f.in使用/ TD / TR> <tr valign="top"> <TD>f.out/ <TD> f值出来。</ TD> </ TR> <tr valign="top"> <TD>call </ TD> <TD> match.call（）。</ TD> </ TR> < TR VALIGN =“顶”> <TD>contrasts </ TD> <TD> </ TD> </ TR> <tr valign="top"> <TD> xlevels </ TD> <TD> </ TD> </ TR> <tr valign="top"> <TD>terms</ TD> <TD>模型框架。</ TD> </ TR> <TR VALIGN = “顶”> <TD> model </ TD> <TD> model=TRUE第一列因变量和列的完整模型的解释变量矩阵。</ TD> < / TR> <tr valign="top"> <TD> x </ TD> <TD>如果x=TRUE完整的模型的解释变量矩阵。</ TD> </ TR > <tr valign="top"> <TD> y </ TD> <TD>如果y=TRUE与因变量的向量。</ TD> </ TR> <TR VALIGN =“顶“<TD>info </ 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----------


Beale, E.M.L., Kendall, M.G., Mann, D.W., (1967) The discarding of variables in multivariate analysis, Biometrika, 54, 357-366.
Efroymson, (1960) Multiple regression analysis, in Mathematical Methods for Digital Computers, eds. A. Ralston and H.S. Wilf, 191-203, Wiley, New York.
Garside, M.J., (1965) The best sub-set in multiple regression analysis, Applied Statistics, 14, 196-200.
Goldberger, A.S, and Jochems, D.B., (1961) Note on stepwise least squares, Journal of the American Statistical Association, 56, 105-110.
Goldberger, A.S., (1961) Stepwise least squares: Residual analysis and specification error, Journal of the American Statistical Association, 56, 998-1000.


实例----------Examples----------



library(wle)

data(hald)

cor(hald)

result <- mle.stepwise(y.hald~x.hald)

summary(result)


转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。


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