huber(VGAM)
huber()所属R语言包：VGAM
Huber's least favourable distribution family function
Huber的最有利的分配家庭功能
译者：生物统计家园网 机器人LoveR
描述----------Description----------
M-estimation of the two parameters of Huber's least favourable distribution. The one parameter case is also implemented.
M-Huber的最有利分布的两个参数的估计。一个参数的情况下也可以实现。
用法----------Usage----------
huber1(llocation = "identity", elocation = list(),
k = 0.862, imethod = 1)
huber(llocation = "identity", lscale = "loge", elocation = list(),
escale = list(), k = 0.862, imethod = 1, zero = 2)
参数----------Arguments----------
参数：llocation, lscale
Link functions applied to the location and scale parameters. See Links for more choices.
链接功能应用到的位置和尺度参数。见Links更多的选择。
参数：elocation, escale
List. Extra argument 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：k
Tuning constant. See rhuber for more information.
时间常数。见rhuber更多信息。
参数：imethod, zero
See CommonVGAMffArguments for information. The default value of zero means the scale parameter is modelled as an intercept-only.
见CommonVGAMffArguments的信息。仅截距模型的尺度参数的默认值zero。
Details
详细信息----------Details----------
Huber's least favourable distribution family function is popular for resistant/robust regression. The center of the distribution is normal and its tails are double exponential.
Huber的最有利的分布族是受欢迎的功能/防水稳健回归。该中心的分布是正常的，它的尾巴是双指数。
By default, the mean is the first linear/additive predictor (returned as the fitted values; this is the location parameter), and the log of the scale parameter is the second linear/additive predictor. The Fisher information matrix is diagonal; Fisher scoring is implemented.
缺省情况下，平均值是第一线性/添加剂预测器（作为拟合值返回，这是位置参数），和尺度参数的log是第二线性/添加剂预测器。 Fisher信息矩阵是对角线费舍尔得分的实现。
The VGAM family function huber1() estimates only the location parameter. It assumes a scale parameter of unit value.
VGAM家庭功能huber1()估计的位置参数。它假定单位价值尺度参数。
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----------
Warning: actually, huber() may be erroneous since the first derivative is not continuous when there are two parameters to estimate. huber1() is fine in this respect.
警告：实际上，huber()可能是错误的，因为一阶导数是不连续的时，有两个参数来估计。 huber1()是在这方面的罚款。
The response should be univariate.
应该是单变量的响应。
（作者）----------Author(s)----------
T. W. Yee. Help was given by Arash Ardalan.
参考文献----------References----------
Robust Statistics, 2nd ed. New York: Wiley.
参见----------See Also----------
rhuber, normal1, gaussianff, laplace, CommonVGAMffArguments.
rhuber，normal1，gaussianff，laplace，CommonVGAMffArguments。
实例----------Examples----------
set.seed(1231); NN = 30; coef1 = 1; coef2 = 10
hdata = data.frame(x2 = sort(runif(NN)))
hdata = transform(hdata, y = rhuber(NN, mu = coef1 + coef2 * x2))
hdata$x2[1] = 0.0 # Add an outlier[添加一个局外人]
hdata$y[1] = 10
fit.huber <- vglm(y ~ x2, huber (imethod = 3), hdata, trace = TRUE)
fit.huber1 <- vglm(y ~ x2, huber1(imethod = 3), hdata, trace = TRUE)
coef(fit.huber, matrix = TRUE)
summary(fit.huber)
## Not run: # Plot the results[＃不运行：＃绘制的结果]
plot(y ~ x2, hdata, col = "blue", las = 1)
lines(fitted(fit.huber) ~ x2, hdata, col = "darkgreen", lwd = 2)
fit.lm <- lm(y ~ x2, hdata) # Compare to a LM:[比较一个LM：]
lines(fitted(fit.lm) ~ x2, hdata, col = "lavender", lwd = 3)
# Compare to truth:[比较真相：]
lines(coef1 + coef2 * x2 ~ x2, hdata, col = "orange", lwd = 2, lty = "dashed")
legend("bottomright", legend = c("truth", "huber", "lm"),
col = c("orange", "darkgreen", "lavender"),
lty = c("dashed", "solid", "solid"), lwd = c(2, 2, 3))
## End(Not run)[＃（不执行）]
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 15:38:12
