margeff(VGAM)
margeff()所属R语言包：VGAM
Marginal effects for the multinomial logit and cumulative models
多项logit的边际效应和累积模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Marginal effects for the multinomial logit model and cumulative logit/probit/... models: the derivative of the fitted probabilities with respect to each explanatory variable.
边际效应的多项Logit模型和累积罗吉特/机率/ ...机型：拟合的概率相对于每个解释变量的衍生物。
用法----------Usage----------
margeff(object, subset = NULL)
参数----------Arguments----------
参数：object
A vglm multinomial or cumulative object.
Avglmmultinomial或cumulative对象。
参数：subset
Numerical or logical vector, denoting the required observation(s). Recycling is used if possible. The default means all observations.
数值或逻辑向量，表示所需的观察（S）。如果可能的话使用回收。默认值是指所有观测。
Details
详细信息----------Details----------
Computes the derivative of the fitted probabilities of a multinomial logit model or cumulative logit/probit/... model with respect to each explanatory variable.
计算衍生工具的拟合概率多项式Logit模型或累积罗吉特/机率/ ...相对于每个解释变量的模型。
值----------Value----------
A p by M+1 by n array, where p is the number of explanatory variables and the (hopefully) nominal response has M+1 levels, and there are n observations.
ApM+1n阵列，其中p是解释变量的数量，（希望）标称响应M+1水平，并有n观察。
If is.numeric(subset) and length(subset) == 1 then a p by M+1 matrix is returned.
如果is.numeric(subset)和length(subset) == 1然后pM+1矩阵返回。
警告----------Warning ----------
Care is needed in interpretation, e.g., the change is not universally accurate for a unit change in each explanatory variable because eventually the "new" probabilities may become negative or greater than unity. Also, the "new" probabilities will not sum to one.
需要谨慎解释，例如，普遍的变化是不准确的各解释变量的单位变化在新的可能性，因为最终可能成为负或大于1。此外，“新”的概率不会是1。
This function is not applicable for models with data-dependent terms such as bs and poly. Also the function should not be applied to models with any terms that have generated more than one column of the LM model matrix, such as bs and poly. For such try using numerical methods such as finite-differences. The formula in object should comprise of simple terms of the form ~ x2 + x3 + x4, etc.
此功能是不适用的模型与数据相关的条款，如bs和poly。此外，功能不应该被应用到模型的任何条款产生一个以上的LM模型矩阵的列，如bs和poly。对于这样的尝试利用数值方法，如有限差分。 formula object应该包括简单的形式 ~ x2 + x3 + x4，等
注意----------Note----------
For multinomial this function should handle any value of refLevel and also any constraint matrices. However, it does not currently handle the xij or form2 arguments, nor vgam objects.
multinomial此功能应该处理任何价值refLevel和任何约束矩阵。然而，目前尚未处理的xij或form2参数，也没有vgam对象。
For multinomial if subset is numeric then the function uses a for loop over the observations (slow). The default computations use vectorization; this uses more memory than a for loop but is faster.
对于multinomial如果subset是数字，那么该函数使用了一种for循环的意见（慢）。默认的计算使用矢量化，这比for循环使用更多的内存，但速度更快。
（作者）----------Author(s)----------
T. W. Yee
参见----------See Also----------
multinomial, cumulative, vglm.
multinomial，cumulative，vglm。
实例----------Examples----------
# Not a good example for multinomial() because the response is ordinal!![没有一个很好的例子多项式（），因为响应顺序！]
ii = 3; hh = 1/100
pneumo = transform(pneumo, let = log(exposure.time))
fit = vglm(cbind(normal, mild, severe) ~ let, multinomial, pneumo)
fit = vglm(cbind(normal, mild, severe) ~ let,
cumulative(reverse = TRUE, parallel = TRUE),
data = pneumo)
fitted(fit)[ii,]
mynewdata = with(pneumo, data.frame(let = let[ii]+hh))
(newp <- predict(fit, newdata = mynewdata, type = "response"))
# Compare the difference. Should be the same as hh --> 0.[的差异进行比较。应该是相同的为hh - > 0。]
round(dig = 3, (newp-fitted(fit)[ii,])/hh) # Finite-difference approximation[有限差分近似]
round(dig = 3, margeff(fit, subset = ii)["let",])
# Other examples[其他的例子]
round(dig = 3, margeff(fit))
round(dig = 3, margeff(fit, subset = 2)["let",])
round(dig = 3, margeff(fit, subset = c(FALSE,TRUE))["let",,]) # recycling[回收]
round(dig = 3, margeff(fit, subset = c(2,4,6,8))["let",,])
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 15:43:41
