hatvalues(VGAM)
hatvalues()所属R语言包：VGAM
Hat Values and Regression Deletion Diagnostics
红帽值和回归删除诊断
译者：生物统计家园网 机器人LoveR
描述----------Description----------
When complete, a suite of functions that can be used to compute some of the regression (leave-one-out deletion) diagnostics, for the VGLM class.
完成后，一套可用于计算的回归（留一出删除）诊断，的VGLM类的功能。
用法----------Usage----------
hatvalues(model, ...)
hatvaluesvlm(model, type = c("diagonal", "matrix", "centralBlocks"), ...)
hatplot(model, ...)
hatplot.vlm(model, multiplier = c(2, 3), lty = "dashed",
xlab = "Observation", ylab = "Hat values", ylim = NULL, ...)
dfbetavlm(model, maxit.new = 1,
trace.new = FALSE,
smallno = 1.0e-8, ...)
参数----------Arguments----------
参数：model
an R object, typically returned by vglm.
R对象，通常返回vglm。
参数：type
Character. The default is the first choice, which is a nM x nM matrix. If type = "matrix" then the entire hat matrix is returned. If type = "centralBlocks" then n central M x M block matrices, in matrix-band format.
字符。默认是第一选择，这是一个nM x nM矩阵。如果type = "matrix"然后整个帽子矩阵返回。如果type = "centralBlocks"然后n中央M x M的分块矩阵，在矩阵带格式。
参数：multiplier
Numeric, the multiplier. The usual rule-of-thumb is that values greater than two or three times the average leverage (at least for the linear model) should be checked.
数字，乘数。通常的经验规则是大于两次或三次的平均杠杆（至少对于线性模型）的值应该被检查。
参数：lty, xlab, ylab, ylim
Graphical parameters, see par etc. The default of ylim is c(0, max(hatvalues(model))) which means that if the horizontal dashed lines cannot be seen then there are no particularly influential observations.
图形参数，请参阅par等ylim默认的是c(0, max(hatvalues(model)))这意味着，如果无法看到的水平虚线则没有特别有影响力的意见。
参数：maxit.new, trace.new, smallno
Having maxit.new = 1 will give a one IRLS step approximation from the ordinary solution (and no warnings!). Else having maxit.new = 10, say, should usually mean convergence will occur for all observations when they are removed one-at-a-time. Else having maxit.new = 2, say, should usually mean some lack of convergence will occur when observations are removed one-at-a-time. Setting trace.new = TRUE will produce some running output at each IRLS iteration and for each individual row of the model matrix. The argument smallno multiplies each value of the original prior weight (often unity); setting it identically to zero will result in an error, but setting a very small value effectively removes that observation.
maxit.new = 1会给一个IRLS一步逼近普通的解决方案（警告！）。否则在maxit.new = 10，说，通常指的融合将出现的所有观测时，被删除的时间。其他maxit.new = 2，说，通常是指一些缺乏衔接的观察时，会发生被删除的时间。设置trace.new = TRUE会产生一些运行在每个IRLS迭代输出和模型矩阵各行。参数smallno每个值乘以原来的先验的权重（通常是统一的），设置它等同于零，将导致错误，但设置一个很小的值，有效地消除观察。
参数：...
further arguments, for example, graphical parameters for hatplot.vlm().
进一步的论据，例如，图形参数hatplot.vlm()。
Details
详细信息----------Details----------
The invocation hatvalues(vglmObject) should return a n x M matrix of the diagonal elements of the hat (projection) matrix of a vglm object. To do this, the QR decomposition of the object is retrieved or reconstructed, and then straightforward calculations are performed.
调用hatvalues(vglmObject)应该返回一个n x Mvglm对象的帽子（投影）矩阵的对角线元素的矩阵。要做到这一点，该对象的QR分解检索或重建，然后进行简单的计算。
The invocation hatplot(vglmObject) should plot the diagonal of the hat matrix for each of the M linear/additive predictors. By default, two horizontal dashed lines are added; hat values higher than these ought to be checked.
调用hatplot(vglmObject)绘制的帽子矩阵对角线的M线性/添加剂的预测。缺省情况下，两个水平的虚线被添加;帽子值高于这些应该以进行检查。
注意----------Note----------
It is hoped, soon, that the full suite of functions described at influence.measures will be written for VGLMs. This will enable general regression deletion diagnostics to be available for the entire VGLM class.
我们希望，不久，influence.measures将被写入为VGLMs的全套功能。这将使一般回归删除诊断程序，可的整个VGLM类。
（作者）----------Author(s)----------
T. W. Yee.
参见----------See Also----------
vglm, cumulative, influence.measures.
vglm，cumulative，influence.measures。
实例----------Examples----------
# Proportional odds model, p.179, in McCullagh and Nelder (1989)[比例优势模型，第179页，在McCullagh和Nelder（1989）]
pneumo <- transform(pneumo, let = log(exposure.time))
fit <- vglm(cbind(normal, mild, severe) ~ let, cumulative, data = pneumo)
hatvalues(fit) # n x M matrix, with positive values[N×M矩阵，具有正的值]
all.equal(sum(hatvalues(fit)), fit@rank) # Should be TRUE[应该是真实的]
## Not run: par(mfrow = c(1, 2))[＃不运行：PAR（mfrow = C（1，2））]
hatplot(fit, ylim = c(0, 1), las = 1, col = "blue")
## 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。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
|
发表于 2012-10-1 15:38:01
