R 有没有办法通过子集循环回归？

对于回归项目，我使用kmeans通过nbclust创建了数据集群。该项目目前有4个集群，但如果成功，该项目可能涉及分析多个美国城市。虽然我可以手动构建每个回归，但我更愿意最小化代码量，从而减少编码错误的机会

当前模型使用具有多个子集和10-15个不同自变量的glm。我目前正在手工构建每个集群回归

Tulsa$Cluster <- Tulsa$Best.partition
#This creates a vector of clusters; there are currently 4 distinct clusters

summary(Tulsa_Cluster1 <- glm(formula = Tulsa$Result ~ Tulsa$FactorA Tulsa$FactorB + Tulsa$FactorC + Tulsa$FactorD + Tulsa$FactorE + Tulsa$FactorF, 
                              method = "glm.fit", family = gaussian(),subset = Tulsa$Cluster =="1"))

summary(Tulsa_Step <- stepAIC(Tulsa_Cluster1, direction = "both",trace = FALSE, 
                              scope=list(lower = ~ Tulsa_Cluster1$FactorA)))

---

Tulsa$Cluster考虑使用
unique
、
levels
、
split
、和
by
来迭代集群的唯一值的以下方法之一：

唯一的+
用于

for (c in unique(Tulsa$Cluster)) {
   Tulsa_Cluster <- glm(formula = Result ~ FactorA + FactorB + FactorC + FactorD + FactorE + FactorF, 
                        data = Tulsa, method = "glm.fit", family = gaussian(), subset = Cluster == c)
   print(summary(Tulsa_Cluster))  # REQUIRED IN for LOOPS TO FOR CONSOLE OUTPUT

   Tulsa_Step <- stepAIC(Tulsa_Cluster, direction = "both", trace = FALSE, 
                         scope=list(lower = ~ Tulsa_Cluster$FactorA))
   print(summary(Tulsa_Step))     # REQUIRED IN for LOOPS TO FOR CONSOLE OUTPUT
}

for (c in levels(factor(Tulsa$Cluster))) {
   Tulsa_Cluster <- glm(formula = Result ~ FactorA + FactorB + FactorC + FactorD + FactorE + FactorF, 
                        data = Tulsa, method = "glm.fit", family = gaussian(), subset = Cluster == c)
   print(summary(Tulsa_Cluster))

   Tulsa_Step <- stepAIC(Tulsa_Cluster, direction = "both", trace = FALSE, 
                         scope=list(lower = ~ Tulsa_Cluster$FactorA))
   print(summary(Tulsa_Step))
}


下面两个返回一个命名的对象列表

split+
lappy
（无子集参数）

考虑使用
unique
、
levels
、
split
和
by
迭代集群的唯一值的以下方法之一：

唯一的+
用于

for (c in unique(Tulsa$Cluster)) {
   Tulsa_Cluster <- glm(formula = Result ~ FactorA + FactorB + FactorC + FactorD + FactorE + FactorF, 
                        data = Tulsa, method = "glm.fit", family = gaussian(), subset = Cluster == c)
   print(summary(Tulsa_Cluster))  # REQUIRED IN for LOOPS TO FOR CONSOLE OUTPUT

   Tulsa_Step <- stepAIC(Tulsa_Cluster, direction = "both", trace = FALSE, 
                         scope=list(lower = ~ Tulsa_Cluster$FactorA))
   print(summary(Tulsa_Step))     # REQUIRED IN for LOOPS TO FOR CONSOLE OUTPUT
}

for (c in levels(factor(Tulsa$Cluster))) {
   Tulsa_Cluster <- glm(formula = Result ~ FactorA + FactorB + FactorC + FactorD + FactorE + FactorF, 
                        data = Tulsa, method = "glm.fit", family = gaussian(), subset = Cluster == c)
   print(summary(Tulsa_Cluster))

   Tulsa_Step <- stepAIC(Tulsa_Cluster, direction = "both", trace = FALSE, 
                         scope=list(lower = ~ Tulsa_Cluster$FactorA))
   print(summary(Tulsa_Step))
}


下面两个返回一个命名的对象列表

split+
lappy
（无子集参数）

摘要
行的哪一部分需要循环？冻糕-两种说法。第一条语句是集群的“基本”回归。第二条语句是逐步回归，在初始回归完成后立即开始。需要循环
摘要
行的哪一部分？冻糕-这两条语句。第一条语句是集群的“基本”回归。第二种说法是逐步回归，初始回归一完成就开始了。谢谢！这太棒了，我真的很感谢你的帮助。谢谢！这太棒了，我真的很感谢你的帮助。