studentt(VGAM)
studentt()所属R语言包：VGAM
Student t Distribution
学生t分布
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimation of parameters in a Student t distribution.
的学生t分布中的参数估计。
用法----------Usage----------
studentt(ldf = "loglog", edf = list(), idf = NULL, tol1 = 0.1,
imethod = 1)
studentt2(df = Inf,
llocation = "identity", elocation = list(),
lscale = "loge", escale = list(),
ilocation = NULL, iscale = NULL,
imethod = 1, zero = -2)
studentt3(llocation = "identity", elocation = list(),
lscale = "loge", escale = list(),
ldf = "loglog", edf = list(),
ilocation = NULL, iscale = NULL, idf = NULL,
imethod = 1, zero = -(2:3))
参数----------Arguments----------
参数：llocation, lscale, ldf
Parameter link functions for each parameter, e.g., for degrees of freedom nu. See Links for more choices. The defaults ensures the parameters are in range. A loglog link keeps the degrees of freedom greater than unity; see below.
参数链接功能，为每一个参数，例如，自由nu度。见Links更多的选择。默认值，确保参数范围内。 Aloglog链接保持自由的程度大于1，见下文。
参数：elocation, escale, edf
List. Extra arguments for the links. See earg in Links for general information.
列表。额外的参数的链接。见earg中Links的一般信息。
参数：ilocation, iscale, idf
Optional initial values. If given, the values must be in range. The default is to compute an initial value internally.
可选的初始值。如果给定的值必须在范围内。默认值是内部计算的初始值。
参数：tol1
A positive value, the tolerance for testing whether an initial value is 1. Best to leave this argument alone.
一个正的数值，用于测试是否一个初始值是1的公差。最好单独离开这个说法。
参数：df
Numeric, user-specified degrees of freedom. It may be of length equal to the number of columns of a response matrix.
数字用户指定的自由度。它可能是长度等于一个响应矩阵的列的数量。
参数：imethod, zero
See CommonVGAMffArguments.
见CommonVGAMffArguments。
Details
详细信息----------Details----------
The Student t density function is
学生t密度函数为：
for all real y. Then E(Y)=0 if nu>1 (returned as the fitted values), and Var(Y)= nu/(nu-2) for nu > 2. When nu=1 then the Student t-distribution corresponds to the standard Cauchy distribution, cauchy1. When nu=2 with a scale parameter of sqrt(2) then the Student t-distribution corresponds to the standard Koenker distribution, koenker. The degrees of freedom can be treated as a parameter to be estimated, and as a real and not an integer. The Student t distribution is used for a variety of reasons in statistics, including robust regression.
为所有真正y。然后E(Y)=0如果nu>1的拟合值（返回），和Var(Y)= nu/(nu-2)nu > 2。当nu=1然后学生t对应的标准柯西分布，cauchy1分配。当nu=2与尺度参数sqrt(2)然后学生t-分布对应于标准Koenker分布，koenker。可以被视为以进行估计的参数，并作为一个真正的而不是一个整数的自由度。学生t分布用于各种原因的统计，包括稳健回归。
Let Y = (T - mu) / sigma where mu and sigma are the location and scale parameters respectively. Then studentt3 estimates the location, scale and degrees of freedom parameters. And studentt2 estimates the location, scale parameters for a user-specified degrees of freedom, df. And studentt estimates the degrees of freedom parameter only. The fitted values are the location parameters. By default the linear/additive predictors are (mu, log(sigma), log log(nu))^T or subsets thereof.
让我们Y = (T - mu) / sigma其中mu和sigma的位置与刻度参数。 studentt3估计的位置，规模和程度的自由参数。和studentt2估计的位置，尺度参数为用户指定的自由度，df。和studentt估计仅适用于自由参数的程度。的拟合值的位置参数。默认情况下，线性/添加剂的预测是(mu, log(sigma), log log(nu))^T或其子集。
In general convergence can be slow, especially when there are covariates.
在一般的收敛速度较慢，尤其是当有协变量。
值----------Value----------
An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, and vgam.
类的一个对象"vglmff"（见vglmff-class）。该对象被用于建模功能，如vglm，vgam。
注意----------Note----------
studentt3 and studentt2 can handle multiple responses.
studentt3和studentt2可以处理多个响应。
Practical experience has shown reasonably good initial values are required. If convergence failure occurs try using arguments such as idf. Local solutions are also possible, especially when the degrees of freedom is close to unity or the scale parameter is close to zero.
实践经验表明，相当不错的初始值是必需的。如果出现收敛失败请尝试使用参数如idf。本地的解决方案也是可能的，特别是当接近统一或尺度参数的自由度是接近于零。
A standard normal distribution corresponds to a t distribution with infinite degrees of freedom. Consequently, if the data is close to normal, there may be convergence problems; best to use normal1 instead.
一个标准正态分布对应的分布与无限度的自由。因此，如果数据是接近正常，有可能是收敛的问题，最好使用normal1，而不是。
（作者）----------Author(s)----------
T. W. Yee
参考文献----------References----------
The probable error of a mean. Biometrika, 6, 1–25.
A generalized asymmetric Student-t distribution with application to financial econometrics. Journal of Econometrics, 157, 297–305.
参见----------See Also----------
normal1, cauchy1, logistic, huber, koenker, TDist.
normal1，cauchy1，logistic，huber，koenker，TDist。
实例----------Examples----------
tdata <- data.frame(x2 = runif(nn <- 1000))
tdata <- transform(tdata, y1 = rt(nn, df = exp(exp(0.5 - x2))),
y2 = rt(nn, df = exp(exp(0.5 - x2))))
fit1 <- vglm(y1 ~ x2, studentt, tdata, trace = TRUE)
coef(fit1, matrix = TRUE)
fit2 <- vglm(cbind(y1, y2) ~ x2, studentt3, tdata, trace = TRUE)
coef(fit2, matrix = TRUE)
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。
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发表于 2012-10-1 15:54:21
