amlbinomial(VGAM)
amlbinomial()所属R语言包：VGAM
Binomial Logistic Regression by Asymmetric Maximum Likelihood Estimation
不对称的最大似然估计的二项Logistic回归
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Binomial quantile regression estimated by maximizing an asymmetric likelihood function.
二项分位数回归估计不对称的似然函数最大化。
用法----------Usage----------
amlbinomial(w.aml = 1, parallel = FALSE, digw = 4,
link = "logit", earg = list())
参数----------Arguments----------
参数：w.aml
Numeric, a vector of positive constants controlling the percentiles. The larger the value the larger the fitted percentile value (the proportion of points below the “w-regression plane”). The default value of unity results in the ordinary maximum likelihood (MLE) solution.
数字，正数的向量控制的百分。该值越大，拟合的百分位数的值（以下的“w回归平面的点的比例”）越大。统一的默认值的查询结果在普通的最大似然（MLE）溶液。
参数：parallel
If w.aml has more than one value then this argument allows the quantile curves to differ by the same amount as a function of the covariates. Setting this to be TRUE should force the quantile curves to not cross (although they may not cross anyway). See CommonVGAMffArguments for more information.
如果w.aml有一个以上的值，则此参数允许不同的协变量的函数相同数额的位数曲线。设置TRUE应该迫使位数曲线不交叉（尽管他们可能不能跨越反正）。见CommonVGAMffArguments更多信息。
参数：digw
Passed into Round as the digits argument for the w.aml values; used cosmetically for labelling.
参数：link, earg
See binomialff.
见binomialff。
Details
详细信息----------Details----------
The general methodology behind this VGAM family function is given in Efron (1992) and full details can be obtained there. This model is essentially a logistic regression model (see binomialff) but the usual deviance is replaced by an asymmetric squared error loss function; it is multiplied by w.aml for positive residuals. The solution is the set of regression coefficients that minimize the sum of these deviance-type values over the data set, weighted by the weights argument (so that it can contain frequencies). Newton-Raphson estimation is used here.
这背后VGAM家庭功能的一般方法给出了埃弗龙（1992），在那里可以得到的全部细节。这种模式本质上是一个logistic回归模型（见binomialff），但一般的越轨行为被替换为一个非对称的平方误差损失函数，它是乘以w.aml残差为正的。该解决方案是一套，最大限度地减少这些偏差类型的值的总和以上的数据集，加权weights参数（以便它可以包含频率）的回归系数。这里使用Newton-Raphson法估计。
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 ----------
If w.aml has more than one value then the value returned by deviance is the sum of all the (weighted) deviances taken over all the w.aml values. See Equation (1.6) of Efron (1992).
如果w.aml有一个以上的值，然后返回的值deviance的所有（加权）deviances的接管所有w.aml值的总和。 ·埃夫隆（1992），见公式（1.6）。
注意----------Note----------
On fitting, the extra slot has list components "w.aml" and "percentile". The latter is the percent of observations below the “w-regression plane”, which is the fitted values. Also, the individual deviance values corresponding to each element of the argument w.aml is stored in the extra slot.
配件，extra插槽列表组件"w.aml"和"percentile"。后者是％以下的“w回归平面”，这是对于拟合值的观测。此外，个别越轨值对应于每个元素的参数w.aml被存储在extra插槽。
For amlbinomial objects, methods functions for the generic functions qtplot and cdf have not been written yet.
对于amlbinomial对象，方法，功能的通用功能qtplot和cdf还没有被写入尚未。
See amlpoisson about comments on the jargon, e.g., expectiles etc.
请参阅amlpoisson有关的术语的意见，例如，expectiles
In this documentation the word quantile can often be interchangeably replaced by expectile (things are informal here).
在本文档中字位数经常被交替更换expectile（这里的东西都是非正式的）。
（作者）----------Author(s)----------
Thomas W. Yee
参考文献----------References----------
Poisson overdispersion estimates based on the method of asymmetric maximum likelihood. Journal of the American Statistical Association, 87, 98–107.
参见----------See Also----------
amlpoisson, amlexponential, amlnormal, alaplace1, denorm.
amlpoisson，amlexponential，amlnormal，alaplace1，denorm。
实例----------Examples----------
# Example: binomial data with lots of trials per observation[例如：大量的实验中每次观测的的二项式数据与]
set.seed(1234)
sizevec = rep(100, length=(nn <- 200))
mydat = data.frame(x = sort(runif(nn)))
mydat = transform(mydat, prob = logit(-0+2.5*x+x^2, inverse = TRUE))
mydat = transform(mydat, y = rbinom(nn, size = sizevec, prob = prob))
(fit = vgam(cbind(y, sizevec - y) ~ s(x, df = 3),
amlbinomial(w = c(0.01, 0.2, 1, 5, 60)),
mydat, trace = TRUE))
fit@extra
## Not run: [＃不运行：]
par(mfrow=c(1,2))
# Quantile plot[分量图]
with(mydat, plot(x, jitter(y), col="blue", las=1, main=
paste(paste(round(fit@extra$percentile, dig=1), collapse=", "),
"percentile-expectile curves")))
with(mydat, matlines(x, 100 * fitted(fit), lwd=2, col="blue", lty=1))
# Compare the fitted expectiles with the quantiles[与分位数，比较合身的expectiles，]
with(mydat, plot(x, jitter(y), col="blue", las=1, main=
paste(paste(round(fit@extra$percentile, dig=1), collapse=", "),
"percentile curves are red")))
with(mydat, matlines(x, 100 * fitted(fit), lwd=2, col="blue", lty=1))
for(ii in fit@extra$percentile)
with(mydat, matlines(x, 100 *
qbinom(p=ii/100, size=sizevec, prob=prob) / sizevec,
col="red", lwd=2, lty=1))
## 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 15:24:48
