假设我使用以下方法拟合模型partykit:mob()。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()，但其他方式也非常受欢迎。
下面是一个例子并尝试获取表格。
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.- 提取物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.- 提取物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.- 我知道，这并不是很优雅：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
一个相当务实的解决方案就是重新拟合模型，恢复发现的规则partykit:mob()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。
