[英]Can't get partykit package's mob function to do univariate MLE fit
I can't get the partykit package's mob function to do univariate MLE fit. 我无法获得partykit软件包的mob函数来进行单变量MLE拟合。
# Trying to convert vignette example here https://cran.r-project.org/web/packages/partykit/vignettes/mob.pdf on page 7 to do univariate MLE gamma fits.
data("PimaIndiansDiabetes", package = "mlbench")
library("partykit")
library("fitdistrplus")
# Generating some fake data to replace the example data.
op <- options(digits = 3)
set.seed(123)
x <- rgamma(nrow(PimaIndiansDiabetes), shape = 5, rate = 0.1)
PimaIndiansDiabetes$diabetes<-x
PimaIndiansDiabetes$glucose<-x
#Hopefully this change to the formula means fit a gamma to just the diabetes vector of values!
pid_formula <- diabetes ~ 1 | pregnant + pressure + triceps + insulin + mass + pedigree + age
#Defining my own, negative of log likelihood since mob will minimize it.
estfun<-function(z) {-logLik(z)}
#replacing the call to glm that is successful in the vignette example.
class(fitdistr) <- append(class(fitdistr),estfun)
logit <- function(y, x, start = NULL, weights = NULL, offset = NULL, ...) {
fitdistr(y, "gamma")
}
#fail! The mob() function still does not see my artificially created estfun().
pid_tree <- mob(pid_formula, data = PimaIndiansDiabetes, fit = logit)
Error in UseMethod("estfun") : no applicable method for 'estfun' applied to an object of class "fitdistr" The above error message does not appear when glm is used instead of fitdistr UseMethod(“ estfun”)中的错误:没有适用于“ estfun”的适用方法应用于“ fitdistr”类的对象当使用glm而不是fitdistr时,不会出现上述错误消息
# estfun runs OK outside of call to mob!
estfun(logit(PimaIndiansDiabetes$diabetes,PimaIndiansDiabetes$glucose))
In principle, it is feasible to use mob()
for what you want to do but there is a misunderstanding of what the estfun()
method is supposed to do and how it is being called. 原则上,将mob()
用于您想做的事情是可行的,但是对estfun()
方法应该做的事情以及如何调用它存在误解。
mob()
needs the following pieces of information from a model object to carry out the construction of the tree: mob()
需要来自模型对象的以下信息来进行树的构造:
coef(object)
. 估计的参数,通常由coef(object)
提取。 logLik(object)
. 优化的目标函数,通常由logLik(object)
提取。 estfun(object)
. 估计函数又称为梯度贡献,通常由estfun(object)
提取。 See vignette("sandwich-OOP", package = "sandwich")
for an introduction. 有关简介,请参见vignette("sandwich-OOP", package = "sandwich")
。 For objects of class "fitdistr"
the former two are available but the latter is not: 对于"fitdistr"
类的对象,前两个可用,但后者不可用:
methods(class = "fitdistr")
## [1] coef logLik print vcov
## see '?methods' for accessing help and source code
Hence: 因此:
f <- fitdistr(x, "gamma")
coef(f)
## shape rate
## 5.022 0.103
logLik(f)
## 'log Lik.' -3404 (df=2)
sandwich::estfun(f)
## Error in UseMethod("estfun") :
## no applicable method for 'estfun' applied to an object of class "fitdistr"
The estfun()
function you have defined does not work for the following two reasons: (1) It is not a method estfun.fitdistr()
that could be called by the generic function sandwich::estfun()
that is used through the package's NAMESPACE
. 您定义的estfun()
函数由于以下两个原因而无法正常工作:(1)并非该estfun.fitdistr()
方法可以通过包的通用函数sandwich::estfun()
调用NAMESPACE
。 (2) It does not compute the right quantity: it's the log-likelihood but we need the derivative of the log-density with respect to both parameters and evaluated at each observation. (2)它没有计算正确的数量:这是对数似然,但我们需要关于两个参数的对数密度的导数,并在每次观察时进行评估。 The latter would be an nx 2 matrix. 后者将是nx 2矩阵。
I think it shouldn't be too hard to compute the score function of the gamma distribution by hand. 我认为手动计算伽玛分布的得分函数应该不难。 But this should also be available in some R package already, possibly gamlss.dist
or also other packages. 但这也应该已经在某些R包中可用,可能是gamlss.dist
或其他包中。
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