cgumbel(VGAM)
cgumbel()所属R语言包：VGAM
Censored Gumbel Distribution
删失Gumbel分布
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Maximum likelihood estimation of the 2-parameter Gumbel distribution when there are censored observations. A matrix response is not allowed.
最大似然估计的参数Gumbel分布时被审查的意见。矩阵的反应是不允许的。
用法----------Usage----------
cgumbel(llocation = "identity", lscale = "loge", elocation = list(),
escale = list(), iscale=NULL, mean=TRUE, percentiles=NULL, zero=2)
参数----------Arguments----------
参数：llocation, lscale
Character. Parameter link functions for the location and (positive) scale parameters. See Links for more choices.
字符。参数链接的位置和功能（正）scale参数。见Links更多的选择。
参数：elocation, escale
Extra argument for the respective links. See earg in Links for general information.
额外的参数，相应的链接。见earg中Links的一般信息。
参数：iscale
Numeric and positive. Initial value for scale. Recycled to the appropriate length. In general, a larger value is better than a smaller value. The default is to choose the value internally.
数字和积极的。初始值scale。再循环到适当的长度。在一般情况下，一个较大的值优于一个较小的值。默认情况下是选择内部的价值。
参数：mean
Logical. Return the mean? If TRUE then the mean is returned, otherwise percentiles given by the percentiles argument.
逻辑。返回的意思吗？如果TRUE然后返回，否则的平均百分percentiles参数。
参数：percentiles
Numeric with values between 0 and 100. If mean=FALSE then the fitted values are percentiles which must be specified by this argument.
数字0和100之间的值。如果mean=FALSE然后拟合值是该参数所的百分必须指定。
参数：zero
An integer-valued vector specifying which linear/additive predictors are modelled as intercepts only. The value (possibly values) must be from the set {1,2} corresponding respectively to location and scale. If zero=NULL then all linear/additive predictors are modelled as a linear combination of the explanatory variables. The default is to fit the shape parameter as an intercept only.
指定一个整数值向量线性/添加剂的预测模型仅作为拦截。值（可能值），必须从集合{1,2}分别对应location和scale。如果zero=NULL然后所有的线性/添加剂预测因子建模作为解释变量的线性组合。默认情况下是适合的形状参数仅作为拦截。
Details
详细信息----------Details----------
This VGAM family function is like gumbel but handles observations that are left-censored (so that the true value would be less than the observed value) else right-censored (so that the true value would be greater than the observed value). To indicate which type of censoring, input extra = list(leftcensored = vec1, rightcensored = vec2) where vec1 and vec2 are logical vectors the same length as the response. If the two components of this list are missing then the logical values are taken to be FALSE. The fitted object has these two components stored in the extra slot.
这VGAM家庭功能就像是gumbel但处理意见是左审查（这样的真正价值将小于观测值），否则右删失（这样的真正价值将更大比实测值）。为了表明哪种类型的审查，输入extra = list(leftcensored = vec1, rightcensored = vec2)其中vec1和vec2是逻辑响应向量的长度相同。如果此列表中的两个组成部分丢失，则对应的逻辑值分别取为FALSE。设备齐全的对象有这两个组件存储在extra插槽。
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和vgam。
警告----------Warning----------
Numerical problems may occur if the amount of censoring is excessive.
数值可能会出现问题，如果是过度的审查。
注意----------Note----------
See gumbel for details about the Gumbel distribution. The initial values are based on assuming all uncensored observations, therefore could be improved upon.
见gumbel Gumbel分布的详细信息。的初始值是根据于假设所有未经审查观测，因此，可以改进。
（作者）----------Author(s)----------
T. W. Yee
参考文献----------References----------
An Introduction to Statistical Modeling of Extreme Values. London: Springer-Verlag.
参见----------See Also----------
gumbel, egumbel, rgumbel, guplot, gev, venice.
gumbel，egumbel，rgumbel，guplot，gev，venice。
实例----------Examples----------
# Example 1[例1]
ystar = venice[["r1"]] # Use the first order statistic as the response[使用第一个次序统计量作为响应]
n = length(ystar)
L = runif(n, 100, 104) # Lower censoring points[下审查]
U = runif(n, 130, 135) # Upper censoring points[上审查点]
y = pmax(L, ystar) # Left censored[左审查]
y = pmin(U, y) # Right censored[右删失]
extra = list(leftcensored = ystar < L, rightcensored = ystar > U)
fit = vglm(y ~ scale(year), data=venice, trace=TRUE, extra=extra,
cgumbel(mean=FALSE, perc=c(5,25,50,75,95)))
coef(fit, matrix=TRUE)
head(fitted(fit))
fit@extra
# Example 2: simulated data[实施例2：模拟数据]
n = 1000
ystar = rgumbel(n, loc=1, scale=exp(0.5)) # The uncensored data[未经审查的数据]
L = runif(n, -1, 1) # Lower censoring points[下审查]
U = runif(n, 2, 5) # Upper censoring points[上审查点]
y = pmax(L, ystar) # Left censored[左审查]
y = pmin(U, y) # Right censored[]
## Not run: par(mfrow=c(1,2)); hist(ystar); hist(y);[＃不运行：PAR（mfrow = C（1,2）），历史（ystar），历史（Y）;]
extra = list(leftcensored = ystar < L, rightcensored = ystar > U)
fit = vglm(y ~ 1, trace=TRUE, extra=extra, cgumbel)
coef(fit, 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 15:29:34
