嵌套 dataframe/tibble 的多元线性回归模型

Question

我正在尝试运行对嵌套数据框进行多元线性回归。我有这个数据样本：


            data.frame(Subcat,Date, COMM1, COMM2,UOM, AUC_TYPE, WINNING_PRICE
                        #--|----------|-----|-----|----|---------|-------|
                        1, 2017-03-07, 40750,41400,"MT","English",35000
                        1, 2017-03-15, 40750,40000,"MT","English",35600

                        2, 2017-10-16, 41000,40500,"METER","Yankee",56440
                        2, 2017-11-06, 41010,40510,"METER","Yankee",52000
                        2, 2019-01-26, 50010,50510,"METER","English",50000

                        3, 2017-03-07, 40750,41400,"MT","English",56900
                        3, 2018-05-26, 50010,50510,"MT","English",47000
                        3, 2019-01-21, 40750,40200,"MT","English",56000
                        3, 2019-01-21, 40750,40200,"MT","English",55900

                        4, 2017-11-08, 37500,39000,"LTR","Dynamic Sealbid",67000
                        4, 2017-11-08, 37500,39000,"LTR","Dynamic Sealbid",65900)

Factors/Character 变量已转换为虚拟变量，然后在子类别的基础上进行嵌套。

                    df2= df[,-2] %>% group_by(Subcat)%>%  nest()

输出是一个带有子目录和数据列的嵌套数据框。我正在尝试运行使用以下代码预测每个子类别的获胜价格的回归模型：

   df2= df[,-2] %>%  group_by(Subcat)%>%  nest() %>%  
      mutate(fit=map(data, ~ lm(WINNING_PRICE~.,data = .)),
         results=map(fit,augment)) %>%
  unnest()

显示错误输出错误：输入必须是向量列表另外：警告信息：现在需要 cols。请使用 cols = c(data, fit, results)。此外，数据帧 df2 未显示在控制台中。

我已引用此查询“”

提前致谢！

Answer 1

我认为这应该有效：

model_fn <- function(df1){ 
  lm(WINNING_PRICE ~ AUC_TYPE, data = df1)
}

fitted_bestel <- df2 %>%
   mutate(fit = map(data, model_fn))

错误来自您使用的两个点（一个替代所有协变量，一个替代数据）。

Answer 2

如果你想建模 WINNING_Price ~ Subcat 我不认为我们必须嵌套（第一个例子）。如果需要在 'data' 列中嵌套和拟合模型，两个模型元素都应位于嵌套数据帧 WINNING_PRICE ~ COMM1 中。以下是每种情况的两个示例： unnest() 错误也来自更改以指定要使用 'cols = ' 参数取消嵌套的列。

library(tidyverse)
df <- tribble(~Subcat, ~Date, ~COMM1, ~COMM2, ~UOM, ~AUC_TYPE, ~WINNING_PRICE,
                #--|----------|-----|-----|----|---------|-------|
                1, 2017-03-07, 40750,41400,"MT","English",35000,
                1, 2017-03-15, 40750,40000,"MT","English",35600,

                2, 2017-10-16, 41000,40500,"METER","Yankee",56440,
                2, 2017-11-06, 41010,40510,"METER","Yankee",52000,
                2, 2019-01-26, 50010,50510,"METER","English",50000,

                3, 2017-03-07, 40750,41400,"MT","English",56900,
                3, 2018-05-26, 50010,50510,"MT","English",47000,
                3, 2019-01-21, 40750,40200,"MT","English",56000,
                3, 2019-01-21, 40750,40200,"MT","English",55900,

                4, 2017-11-08, 37500,39000,"LTR","Dynamic Sealbid",67000,
                4, 2017-11-08, 37500,39000,"LTR","Dynamic Sealbid",65900)

fit <- lm(WINNING_PRICE ~ Subcat, data = df)

plot(df$Subcat, y = df$WINNING_PRICE)
abline(fit)

#to fit many model to data with 'data' next column  
df2= df[,-2] %>% group_by(Subcat)%>%  nest()

df3 <- df2 %>% 
  mutate(fit = map(data, ~lm(WINNING_PRICE~COMM1, data = .)),
         results = map(fit, broom::augment))
#need to specify cols to unnest (this was changed recentlyish)
df4 <- df3 %>% unnest(cols = data)

嵌套 dataframe/tibble 的多元线性回归模型

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tidyverse

tibble