probit(sampleSelection)
probit()所属R语言包：sampleSelection
Binary choice models.
二元选择模型。
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Binary Choice models. These models are estimated by binaryChoice, intended to be called by wrappers like probit.
二元选择模型。这些模型估计binaryChoice，被称为封装类，如probit。
用法----------Usage----------
probit(formula, ...)
binaryChoice(formula, subset, na.action, start = NULL, data = sys.frame(sys.parent()),
x=FALSE, y = FALSE, model = FALSE, method="ML",
userLogLik=NULL,
cdfLower, cdfUpper=function(x) 1 - cdfLower(x),
logCdfLower=NULL, logCdfUpper=NULL,
pdf, logPdf=NULL, gradPdf,
maxMethod="Newton-Raphson",
... )
参数----------Arguments----------
参数：formula
a symbolic description of the model to be fit, in the form response ~ explanatory variables (see also details).
一个象征性的模型来描述是合适的形式response ~ explanatory variables（见详情）。
参数：subset
an optional vector specifying a subset of observations to be used in the fitting process.
一个可选的矢量指定的装配过程中可以使用的观测值的一个子集。
参数：na.action
a function which indicates what should happen when the data contain 'NA's. The default is set by the 'na.action' setting of 'options', and is 'na.fail' if that is unset. The 'factory-fresh' default is 'na.omit'. Another possible value is 'NULL', no action. Value 'na.exclude' can be useful.
一个函数，它表示时会发生什么数据包含“NA”。默认设置是“na.action设置选项，na.fail”如果是没有设置的。的工厂新鲜“的默认值是”na.omit“。另一种可能的值是“NULL”，没有任何动作。价值“na.exclude”可能是有用的。
参数：start
inital value of parameters.
>初始值的参数。
参数：data
an optional data frame containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which probit is called.
一个可选的数据框包含在模型中的变量。如果未找到数据，变量取自环境（公式），典型的是从该probit被称为环境。
参数：x, y, model
logicals. If TRUE the corresponding components of the fit (the model matrix, the response, the model frame) are returned.
的逻辑。如果设置为TRUE，拟合的相应部件（模型矩阵，该响应，模型框架）返回。
参数：method
the method to use; for fitting, currently only method = "ML" (Maximum Likelihood) is supported; method = "model.frame" returns the model frame (the same as with model = TRUE, see below).
所使用的方法进行拟合，目前唯一的方法“ML（最大似然）支持的方法=”model.frame“的返回的模型框架（与模型相同= TRUE，见下文）。
参数：userLogLik
log-likelihood function. A function of the parameter to be estimated, which computes the log likelihood. If supplied, it will be used instead of cdfLower and similar parameters. This allows user to fine-tune the likelihood function such as introducing robust approximations. It might return the corresponding gradient and Hessian as approximations, see maxNR.
对数似然函数。一个函数的参数进行估计，计算对数似然。如果提供，它会被用来代替cdfLower和类似的参数。这允许用户微调的可能性的功能，如引入强大的近似。它可能会返回相应的梯度和Hessian作为近似值，请参阅maxNR。
参数：cdfLower, cdfUpper, pdf, gradPdf
function, lower and upper tail of the cumulative distribution function of the disturbance term, corresponding probability density function, and gradient of the density function. These functions must take a numeric vector as the argument, and return numeric vector of the probability/gradient values.
功能，下部和上部的尾部的干扰项，对应的概率密度函数，和梯度密度函数的累积分布函数。这些功能必须采取一个数值向量作为参数，并返回数字的概率向量/梯度值。
参数：logCdfLower, logCdfUpper, logPdf
logs of the corresponding functions. Providing these may improve precision in extreme tail. If not provided, simply logs are takes of the corresponding non-log values.
log的相应的功能。提供这些可以提高精度，在极端的尾巴。如果没有提供，只需登录都需要相应的非log值。
参数：maxMethod
character, a maximisation method supported by maxLik. This is only useful if using a user-supplied likelihood function.
字符，支持的maxLik最大化的方法。这仅仅是有用的，如果使用用户提供的似然函数。
参数：...
further arguments for binaryChoice and maxLik.
进一步的论据binaryChoice和maxLik。
Details
详细信息----------Details----------
The dependent variable for the binary choice models must have exactly two levels (e.g. '0' and '1', 'FALSE' and 'TRUE', or 'no' and 'yes'). Internally, the first level is always coded '0' ('failure') and the second level as '1' ('success'), no matter of the actual value. However, by default the levels are ordered alphabetically and this makes puts '1' after '0', 'TRUE' after 'FALSE' nad 'yes' after 'no'.
二元选择模型的因变量，必须刚好有两个级别（如：0和1，FALSE，TRUE，或没有，是）。在内部，该第一电平总是编码0（故障）和所述第二电平为1（成功）的实际值，不管。然而，在默认情况下的水平是按字母顺序排列，这使得使1，0后，TRUE后FALSE河畔是后不。
Via the distribution function parameters, binaryChoice supports generic latent linear index binary choice models with additive disturbance terms. It is intended to be called by wrappers like probit. However, it is also visible in the namespace as the user may want to implement her own models using another distribution of the disturbance term.
通过分布函数的参数，binaryChoice支持通用潜在的线性指标的二元选择模型与添加剂扰动项。它的目的是要调用的封装类，如probit。然而，它也显示在命名空间的用户可能希望实现自己的机型采用另一种分布的干扰项。
The model is estimated using Maximum Likelihood and Newton-Raphson optimizer.
该模型估计采用最大似然法和牛顿 - 拉夫逊优化。
probit implements an outlier-robust log-likelihood (Demidenko, 2001). In case of large outliers the analytic Hessian is singular while Fisher scoring approximation (used, for instance, by glm) is invertible. Those values are not reliable in case of outliers.
probit实现离群强劲的对数似然（杰米坚科，2001年）。在大的异常值的情况下，分析Hessian是单数，而费舍尔得分逼近（所用，例如，glm）是可逆的。这些值是不可靠的情况下，离群。
No attempt is made to establish the existence of the estimator.
没有尝试建立的估计存在。
值----------Value----------
An object of class "binaryChoice". It is a list with following components:
对象的类“binaryChoice”。这是一个与以下组件列表：
参数：LRT
Likelihood ration test. The full model is tested against H0: the parameters (besides constant) have no effect on the result. This is a list with components
似然比试验。全模型的测试是针对H0：参数（除了常数）具有对结果没有影响。这是与组件的列表
LRTThe LRT value
LRTThe LRT值
dfDegrees of freedom for LRT (= df of the model - 1)
dfDegrees自由LRT（= DF的模式 - 1）
LRT is distributed by chi2(df) under H0.
轻铁分布χ^ 2（DF）H0。
参数：param
A list with following background information:
列表下列资料：
nParamNumber of parameters of the model including constant
nParamNumber模型的参数，包括持续
nObsNumber of the observations
nObsNumber的意见
N1Number of observations with non-zero (true) response
具有非零（真）响应的观测N1Number
N0Number of observations with zero (false) response
0（假）的反应的观察与N0Number
参数：df
Number of free parameters
自由参数的数目
参数：x
if requested, the model matrix used.
如果有要求，使用的模型矩阵。
参数：y
if requested, the model response used. The response is represented internally as 0/1 integer vector.
如果有要求，使用的模型响应。响应表示内部0/1整数向量。
参数：model
the model frame, only if model = TRUE or method = "model.frame".
模型框架，只有model = TRUE或method = "model.frame"。
参数：na.action
information returned by model.frame on the special handling of NA s.
返回的信息model.frameNA S上的特殊处理。
Other components are inherited from maxLik.
其他组件都继承自maxLik。
probit adds class "probit" and following components to the "binaryChoice" object:
probit添加类“概率”的“binaryChoice”对象的以下组件：
参数：family
the family object used (binomial with link="probit")
家庭对象使用（binomiallink="probit"）
（作者）----------Author(s)----------
Ott Toomet <a href="mailto toomet@ut.ee">otoomet@ut.ee</a>
参考文献----------References----------
model”, Mathematical Communications 6, 233-247
参见----------See Also----------
maxLik for ready-packaged likelihood maximisation routines and methods, glm for generalised linear models, including probit, binomial.
maxLik准备打包的可能性最大化的过程和方法，glm广义线性模型，包括概率，binomial的。
实例----------Examples----------
## A simple MC trial: note probit assumes normal errors[MC＃一个简单的试验：请注意概率假设正常的错误]
x <- runif(100)
e <- 0.5*rnorm(100)
y <- x + e
summary(probit((y > 0) ~ x))
## female labour force participation probability[＃女性劳动人口参与概率]
data(Mroz87)
Mroz87$kids <- Mroz87$kids5 > 0 | Mroz87$kids618 > 0
Mroz87$age30.39 <- Mroz87$age < 40
Mroz87$age50.60 <- Mroz87$age >= 50
summary(probit(lfp ~ kids + age30.39 + age50.60 + educ + hushrs +
huseduc + huswage + mtr + motheduc, data=Mroz87))
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-9-29 21:36:48
