r - 生成具有 `partykit:mob()` 对象的并排节点模型的表

Question

假设我使用 partykit:mob() 拟合模型.之后，我想生成一个包含所有节点的并排表(包括使用整个样本拟合的模型)。在这里，我尝试使用 stargazer() 来做到这一点。 ，但其他方式也非常受欢迎。
下面是一个示例并尝试获取该表。

library("partykit")
require("mlbench")
## Pima Indians diabetes data
data("PimaIndiansDiabetes", package = "mlbench")
## a simple basic fitting function (of type 1) for a logistic regression
logit <- function(y, x, start = NULL, weights = NULL, offset = NULL, ...) {
  glm(y ~ 0 + x, family = binomial, start = start, ...)
}
## set up a logistic regression tree
pid_tree <- mob(diabetes ~ glucose | pregnant + pressure + triceps + insulin +
                  mass + pedigree + age, data = PimaIndiansDiabetes, fit = logit)

pid_tree 
# Model-based recursive partitioning (logit)
# 
# Model formula:
#   diabetes ~ glucose | pregnant + pressure + triceps + insulin +
#   mass + pedigree + age
# 
# Fitted party:
#   [1] root
# |   [2] mass <= 26.3: n = 167
# |       x(Intercept)     xglucose
# |        -9.95150963   0.05870786
# |   [3] mass > 26.3
# |   |   [4] age <= 30: n = 304
# |   |       x(Intercept)     xglucose
# |   |        -6.70558554   0.04683748
# |   |   [5] age > 30: n = 297
# |   |       x(Intercept)     xglucose
# |   |        -2.77095386   0.02353582
# 
# Number of inner nodes:    2
# Number of terminal nodes: 3
# Number of parameters per node: 2
# Objective function: 355.4578

1.- 提取 summary(pid_tree, node = x) + stargazer() .

## I want to replicate this table extracting the the nodes from partykit object.   
library(stargazer)  
m.glm<-   glm(diabetes ~ glucose, family = binomial,data = PimaIndiansDiabetes)

typeof(m.glm)
## [1] "list"
class(m.glm)
## [1] "glm" "lm" 
stargazer(m.glm)
## ommited output.



## Extracting summary from each node
summ_full_data <- summary(pid_tree, node = 1)
summ_node_2    <- summary(pid_tree, node = 2)
summ_node_4    <- summary(pid_tree, node = 4)
summ_node_5    <- summary(pid_tree, node = 5)

## trying to create stargazer table with coefficients
stargazer(m.glm,
          summ_node_2, 
          summ_node_4,
          summ_node_5,title="MOB Results")
##Error: $ operator is invalid for atomic vectors

2.- 提取 pid_tree[x] + stargazer() .

## Second Attempt (extracting modelparty objects instead)
node_2    <- pid_tree[2]
node_4    <- pid_tree[4]
node_5    <- pid_tree[5]

class(node_5)
##[1] "modelparty" "party"     

stargazer(m.glm,
          node_2, 
          node_4,
          node_5,title="MOB Results")
# % Error: Unrecognized object type.
# % Error: Unrecognized object type.
# % Error: Unrecognized object type.

3.- 不是很优雅，我知道:强制类模拟 glm 对象。

## Force class of object to emulate glm one
class(m.glm)
class(summ_node_2) <- c("glm", "lm") 
stargazer(summ_node_2)
##Error in if (p > 0) { : argument is of length zero

一个相当实用的解决方案是重新拟合模型，恢复 partykit:mob() 找到的规则。然后使用 stargaze()在他们身上，但我肯定在这里遗漏了一些东西。提前致谢。

Answer 1

我的不好，这是使它起作用的一个小差异。这是一个解决方案，不确定是否是最好的方法，但它确实有效。-

library(stargazer)  
obj_node_full_sample<- pid_tree[1]$node$info$object
obj_node_2<- pid_tree[2]$node$info$object
obj_node_4<- pid_tree[4]$node$info$object
obj_node_5<- pid_tree[5]$node$info$object

stargazer(obj_node_full_sample,
          obj_node_2,
          obj_node_4,
          obj_node_5,title="Results", align=TRUE)