clls(sensR)
clls()所属R语言包：sensR
Cumulative Link Location-Scale Models
累计链接位置比例模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
IMPORTANT: This function and its methods are no longer supported. The user is adviced to use clm() from package ordinal instead.
重要提示：不再支持此功能，其方法。用户谏，从包序号，而不是使用CLM（）。
Fits a cumulative link location-scale model to an ordered response variable. When the scale part is left unspecified, the model reduces to a cumulative link model assuming a constant scale. With the default logistic link function, the model reduces to the famous Proportional Odds Model. With the probit link and a single two-level factor in both location and scale parts, the model is known as the Binormal model in the Signal Detection Theory and the Psychometric literature.
适合一个链路累积的位置规模的模型的有序响应变量。当标尺部分是未指定的，模型简化为一个链路累积模型假设一个恒定的规模。使用默认的逻辑连接功能，模型简化为著名的比例优势模型。地点和规模部分的链接和一个两水平因子的概率，该模型被称为双正态模型在信号检测理论和心理的文学。
用法----------Usage----------
clls(location, scale, data, weights, start, ..., subset,
na.action, contrasts = NULL, Hess = FALSE, model = TRUE,
method = c("logistic", "probit", "cloglog", "cauchit"))
参数----------Arguments----------
参数：location
a formula expression as for regression models, of the form response ~ predictors. The response should be a factor (preferably an ordered factor), which will be interpreted as an ordinal response, with levels ordered as in the factor. The model must have an intercept: attempts to remove one will lead to a warning and be ignored. An offset may be used. See the documentation of formula for other details.
回归模型，公式表达的形式response ~ predictors。的反应应该是一个因素（优选一个有序的因子），这将被解释为一个序响应，与因子的水平排列的。该模型必须有一个拦截试图删除一个会导致警告，可以忽略。一个偏移量，也可使用。请参阅文档formula其他细节。
参数：scale
a optional formula expression as for the location part, of the form ~ predictors, ie. with an empty left hand side. If left unspecified, the model assumes a constant scale and reduces to the cumulative link model. An offset may be used. See the documentation of formula for other details.
的位置部分， ~ predictors的形式，即一个可选的公式表达。用一个空的左手侧。如果未指定，该模型假设恒定的规模，并降低链路累积模型。一个偏移量，也可使用。请参阅文档formula其他细节。
参数：data
an optional data frame in which to interpret the variables occurring in formula.
一个可选的数据框，其中解释的变量发生在formula。
参数：weights
optional case weights in fitting. Default to 1.
可选的情况下，权重装修。默认为1。
参数：start
initial values for the parameters. This is in the format c(beta, theta, sigma): see the Values section.
为参数的初始值。这是格式“c(beta, theta, sigma)：请参阅”值“部分。
参数：...
additional arguments to be passed to optim, most often a control argument.
其他参数被传递到的optim，最常见的是control参数。
参数：subset
expression saying which subset of the rows of the data should be used in the fit. All observations are included by default.
表达说应在适合使用的哪个子集的行的数据。默认情况下，所有的观测。
参数：na.action
a function to filter missing data.
一个函数来筛选丢失的数据。
参数：contrasts
a list of contrasts to be used for some or all of the factors appearing as variables in the model formula.
要用于出现的因素作为模型公式中的变量的一些或所有的列表对比。
参数：Hess
logical for whether the Hessian (the observed information matrix) should be returned. Use this if you intend to call summary or vcov on the fit.
符合逻辑的Hessian（观察到的信息矩阵）是否应该返回。如果你打算叫summary或vcov的拟合。
参数：model
logical for whether the model matrix should be returned.
逻辑，模型矩阵是否应该被返回。
参数：method
logistic or probit or complementary log-log or cauchit (corresponding to a Cauchy latent variable).
后勤或概率或互补的log的的log或cauchit（对应于一个柯西潜在变量）。
Details
详细信息----------Details----------
The implementation is highly inspired by polr in package MASS and should give compatible results, if scale is left unspecified.
实现高度的启发polr在包MASS应符合的结果，如果scale未指定的。
Note that standard errors are appropriate for tau = log sigma and not for sigma, because the profile likelihood is usually more symmetric for tau than for sigma. Therefore vcov will give the variance-covariance matrix of the parameters with tau rather than sigma and summary.clls will report standard errors for log sigma. Notice also that a relevant test for sigma is H_0: sigma = 1, so the relevant test for log sigma is H_0: log(sigma) = 0. This is reflected in the z value for sigma returned by summary.clls.
需要注意的是适当的标准误差tau=logsigma，而不是为sigma，通常是对称的tau比sigma，因为配置文件的可能性。因此vcov会给方差 - 协方差矩阵的参数tau而不是sigma和summary.clls报告标准错误logsigma。另请注意，相关的测试sigmaH_0: sigma = 1，所以相关的测试的logsigma是H_0: log(sigma) = 0。这反映在z值sigma返回summary.clls的。
There are methods for the standard model-fitting functions, including summary, vcov, anova, and an extractAIC method.
有一些方法的标准模型拟合功能，包括summary，vcov，anova和extractAIC方法。
值----------Value----------
A object of class "clls". This has components
一个对象类"clls"。这也有组件
<table summary="R valueblock"> <tr valign="top"><td>coefficients</td> <td> the coefficients of the location (beta), the intercepts (theta) and the scale (sigma).</td></tr> <tr valign="top"><td>beta</td> <td> the parameter estimates of the location part.</td></tr> <tr valign="top"><td>theta</td> <td> the intercepts/thresholds for the class boundaries.</td></tr> <tr valign="top"><td>sigma</td> <td> the parameter estimates of the scale part.</td></tr> <tr valign="top"><td>tau</td> <td> parameter estimates of the scale part on the log scale; ie. tau = log sigma.</td></tr> <tr valign="top"><td>deviance</td> <td> the residual deviance.</td></tr> <tr valign="top"><td>fitted.values</td> <td> a matrix, with a column for each level of the response with the fitted probabilities.</td></tr> <tr valign="top"><td>fitted.case</td> <td> a vector of same length as response, with the fitted probabilities on a case-by-case basis.</td></tr> <tr valign="top"><td>lev</td> <td> the names of the response levels.</td></tr> <tr valign="top"><td>terms.location</td> <td> a terms structure describing the location part.</td></tr> <tr valign="top"><td>terms.scale</td> <td> a terms structure describing the scale part.</td></tr> <tr valign="top"><td>df.residual</td> <td> the number of residual degrees of freedoms, calculated using the weights.</td></tr> <tr valign="top"><td>edf</td> <td> the (effective) number of degrees of freedom used by the model</td></tr> <tr valign="top"><td>n, nobs</td> <td> the (effective) number of observations, calculated using the weights.</td></tr> <tr valign="top"><td>call</td> <td> the matched call.</td></tr> <tr valign="top"><td>method</td> <td> the matched method used.</td></tr> <tr valign="top"><td>convergence</td> <td> the convergence code returned by optim.</td></tr> <tr valign="top"><td>niter</td> <td> the number of function and gradient evaluations used by optim.</td></tr> <tr valign="top"><td>Hessian</td> <td> if Hess is true, the observed Fisher information matrix.</td></tr> <tr valign="top"><td>location</td> <td> if model is true, the model.frame for the location part.</td></tr> <tr valign="top"><td>scale</td> <td> if model is true, the model.frame for the scale part.</td></tr> </table>
<table summary="R valueblock"> <tr valign="top"> <TD> coefficients</ TD> <TD>系数的位置（beta），截距（theta）和规模（sigma）</ TD> </ TR> <tr valign="top"> <TD>beta </ TD> <TD>的参数估计部分的位置。</ TD> </ TR> <tr valign="top"> <TD>theta </ TD> <TD>拦截/阈值的阶级界限。</ TD> </ TR> <tr valign="top"> <TD>sigma </ TD> <TD>参数估计的规模。</ TD> </ TR> <tr valign="top"> < tau TD> </ TD> <TD>参数估计规模上的log规模，即。 tau=logsigma。</ TD> </ TR> <tr valign="top"> <TD>deviance </ TD> <TD>的残余偏差。</ TD> </ TR> <tr valign="top"> <TD> fitted.values</ TD> <td>一个矩阵，一列每个级别的响应拟合概率。</ TD> </ TR> <tr valign="top"> <TD> fitted.case</ TD> <td>一个向量的长度相同，response，一情况件逐案的拟合概率基础。</ TD> </ TR> <tr valign="top"> <TD>lev</ TD> <TD>的响应级别的名称。</ TD> </ TR> <TR VALIGN =“”> <TD>terms.location </ TD> <td>一个terms结构描述位置的一部分。</ TD> </ TR> <tr valign="top"> < terms.scale TD> </ TD> <td>一个terms规模部分的结构来描述。</ TD> </ TR> <tr valign="top"> <TD>df.residual </ TD> <TD>剩余自由度的数量，来计算权重。</ TD> </ TR> <tr valign="top"> <TD>edf </ TD> <TD >（生效日期）模型所使用的自由度</ TD> </ TR> <tr valign="top"> <TD> n, nobs</ TD> <TD>的数（有效的）的观测，来计算权重。</ TD> </ TR> <tr valign="top"> <TD>call</ TD> <TD>匹配的呼叫。</ TD> </ TR > <tr valign="top"> <TD> method </ TD> <TD>匹配的方法。</ TD> </ TR> <tr valign="top"> <TD> X> </ TD> <TD>收敛代码返回convergence。</ TD> </ TR> <tr valign="top"> <TD> optim</ TD> < TD>的数量的功能和梯度评价niter。</ TD> </ TR> <tr valign="top"> <TD>optim </ TD> <TD>，如果 Hessian是真实的，所观察到的Fisher信息矩阵。</ TD> </ TR> <tr valign="top"> <TD> Hess </ TD> <TD>如果location是真实的，model的位置部分。</ TD> </ TR> <tr valign="top"> <TD>model.frame </ TD> <TD>如果scale “”是真的，model规模的一部分。</ TD> </ TR> </ TABLE>
参考文献----------References----------
Agresti, A. (2002) Categorical Data. Second edition. Wiley.
Christensen, R. H. B., Brockhoff, P. B. and Cleaver, G. (2008) Estimation and Inference in the A-Not A test with Sureness. Manuscript for Food Quality and Preference.
Venables, W. N. and Ripley, B. D. (2002) Modern Applied Statistics with S. Fourth edition. Springer.
参见----------See Also----------
polr, optim, glm, multinom.
polr，optim，glm，multinom。
实例----------Examples----------
options(contrasts = c("contr.treatment", "contr.poly"))
## Extend example from polr in package MASS:[在包MASS扩展的例子polr的：]
## Fit model from polr example:[合适的模型polr例如：]
data(housing, package = "MASS")
fm1 <- clls(Sat ~ Infl + Type + Cont, weights = Freq, data = housing)
fm1
summary(fm1)
## With probit link:[＃概率链接：]
summary(update(fm1, method = "probit"))
## Allow scale to depend on Cont-variable[＃允许规模取决于-cont变量]
summary(fm2 <- update(fm1, scale =~ Cont))
anova(fm1, fm2)
## which seems to improve the fit[＃这似乎是提高配合]
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。
|
发表于 2012-9-30 01:06:46
