为什么R不显示双向方差分析的f值？

我有三个变量：

month <- c("1","1","1","1","2","2","2","2","3","3","3","3","4","4","4","4","5","5","5","5","6","6","6","6")
region <-c("1","2","3","4","1","2","3","4","1","2","3","4","1","2","3","4","1","2","3","4","1","2","3","4")
sales <-c(85, 107,61, 22, 40, 65, 58,51,60,41,45,27,15,30,68,63,28,3,57,12,36,21,10,16)

data <- cbind(sales, month, region)
data <- as.data.frame(data)

mod.aov <- aov(sales ~ month*region, data = data)
summary(mod.aov)

• 房屋销售数量
• 月份（成对）
• 城市区域（N-W-E-S）

我想创建一个双向方差分析，对变量进行交互作用：

month <- c("1","1","1","1","2","2","2","2","3","3","3","3","4","4","4","4","5","5","5","5","6","6","6","6")
region <-c("1","2","3","4","1","2","3","4","1","2","3","4","1","2","3","4","1","2","3","4","1","2","3","4")
sales <-c(85, 107,61, 22, 40, 65, 58,51,60,41,45,27,15,30,68,63,28,3,57,12,36,21,10,16)

data <- cbind(sales, month, region)
data <- as.data.frame(data)

mod.aov <- aov(sales ~ month*region, data = data)
summary(mod.aov)

R未显示此模型的f值。为什么呢

和这个例子相关的是，对两个分类变量进行线性回归模型，作为它们之间相互作用的预测因子，是可能的（或有意义的）吗？ 如有任何见解，将不胜感激。

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谢谢

我在另一篇文章中读到，模型已经饱和，因为没有足够的数据点来满足模型所需的所有自由度

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每个月和数据组合只有一次观察，无法估计n=1的月：区域的影响

data = data.frame(sales,month,region)
table(data$month,data$region)
   
    1 2 3 4
  1 1 1 1 1
  2 1 1 1 1
  3 1 1 1 1
  4 1 1 1 1
  5 1 1 1 1
  6 1 1 1 1

可以粗略地解释为，方差分析是方差分析，n=1表示无方差。因此，摘要不显示f值

要回答这个问题：

与这个例子相关的是，是否有可能（或有意义） 对两个分类变量执行线性回归模型，如下所示： 它们之间相互作用的预测因子？任何洞察都会是错误的 感激

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是的，您可以这样做，例如，在这种情况下，如果您每个月和区域组合有多个复制，那么您基本上是在为每个月的不同区域效果建模。

您可以检查您的代码吗，它应该是

data=data.frame（sales，month，region）

您不能用数字绑定字符，所有字符都将转换为字符