koenker(VGAM)
koenker()所属R语言包：VGAM
Koenker's Distribution Family Function
Koenker的分配家庭功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Estimates the location and scale parameters of Koenker's distribution by maximum likelihood estimation.
Koenker的分布位置与刻度参数估计的最大似然估计。
用法----------Usage----------
koenker(percentile = 50, llocation = "identity", lscale = "loge",
elocation = list(), escale = list(), ilocation = NULL,
iscale = NULL, imethod = 1, zero = 2)
参数----------Arguments----------
参数：percentile
A numerical vector containing values between 0 and 100, which are the quantiles and expectiles. They will be returned as "fitted values".
向量的数值介于0和100之间的值，这是位数和expectiles的。他们将返回“拟合值”。
参数：llocation, lscale, elocation, escale
See Links for more choices, and CommonVGAMffArguments.
见Links更多的选择，和CommonVGAMffArguments。
参数：ilocation, iscale, imethod, zero
See CommonVGAMffArguments for details.
见CommonVGAMffArguments的详细信息。
Details
详细信息----------Details----------
Koenker (1993) solved for the distribution whose quantiles are equal to its expectiles. This is called Koenker's distribution here. Its canonical form has mean and mode at 0 and has a heavy tail (in fact, its variance is infinite).
koenker（1993）的分布，其位数等于其expectiles解决。这就是所谓的Koenker的分布在这里。其典型形式在0均值和模式，并有一个沉重的尾巴（事实上，它的方差是无限的）。
The standard (“canonical”) form of Koenker's distribution can be endowed with a location and scale parameter. The standard form has a density that can be written as
标准（“规范”）的形式可以赋予Koenker的分布的位置和尺度参数。的标准形式具有的密度，可以写为
for real y. Then z = (y-a)/b for location and scale parameters a and b > 0. The mean of Y is a. By default, eta1=a and eta2=log(b). The expectiles/quantiles corresponding to percentile are returned as the fitted values; in particular, percentile = 50 corresponds to the mean (0.5 expectile) and median (0.5 quantile).
真正的y。然后z = (y-a)/b的位置和尺度参数a和b > 0。 Y的平均a。默认情况下，eta1=a和eta2=log(b)。的expectiles /分位数对应于percentile返回作为拟合的值，特别是，percentile = 50对应的平均（0.5 expectile）和中位数（0.5分位数）。
Note that if Y has a standard Koenker distribution then Y = sqrt(2) * T_2 where T_2 has a Student-t distribution with 2 degrees of freedom. The two parameters here can also be estimated using studentt2 by specifying df = 2 and making an adjustment for the scale parameter, however, this VGAM family function is more efficient since the EIM is known (Fisher scoring is implemented.)
请注意，如果Y有一个标准的Koenker分布，然后Y = sqrt(2) * T_2其中T_2有一个Student-t分布与2个自由度。然而，这里的两个参数也可使用studentt2通过指定df = 2和使调整为尺度参数估计，这VGAM家庭功能是更有效的，因为已知的EIM（费希尔得分实现。）
值----------Value----------
An object of class "vglmff" (see vglmff-class). The object is used by modelling functions such as vglm, rrvglm and vgam.
类的一个对象"vglmff"（见vglmff-class）。该对象被用于建模功能，如vglm，rrvglm和vgam。
（作者）----------Author(s)----------
T. W. Yee
参考文献----------References----------
When are expectiles percentiles? (solution) Econometric Theory, 9, 526–527.
参见----------See Also----------
dkoenker, studentt2.
dkoenker，studentt2。
实例----------Examples----------
set.seed(123); nn <- 1000
kdat <- data.frame(x2 = sort(runif(nn)))
kdat <- transform(kdat, mylocat = 1 + 3 * x2,
myscale = 1)
kdat <- transform(kdat, y = rkoenker(nn, loc = mylocat, scale = myscale))
fit <- vglm(y ~ x2, koenker(perc = c(1, 50, 99)), kdat, trace = TRUE)
fit2 <- vglm(y ~ x2, studentt2(df = 2), kdat, trace = TRUE) # 'same' as fit[作为适合的同]
coef(fit, matrix = TRUE)
head(fitted(fit))
head(predict(fit))
# Nice plot of the results[尼斯积的结果]
## Not run: plot(y ~ x2, kdat, col = "blue", las = 1,[＃未运行图（Y 2倍，KDAT，列=“蓝”，LAS = 1，]
sub = paste("n =", nn),
main = "Fitted quantiles/expectiles using Koenker's distribution")
matplot(with(kdat, x2), fitted(fit), add = TRUE, type = "l", lwd = 3)
legend("bottomright", lty = 1:3, lwd = 3, legend = colnames(fitted(fit)),
col = 1:3)
## End(Not run)[＃（不执行）]
fit@extra$percentile # Sample quantiles[采样位数]
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 15:40:02
