Question

以下代码运行非常简单的lm()，并尝试在一个较小的数据框中汇总结果（因子水平，系数）：

df <- data.frame(star_sign = c("Aries", "Taurus", "Gemini", "Cancer", "Leo", "Virgo", "Libra", "Scorpio", "Sagittarius", "Capricorn", "Aquarius", "Pisces"),
                 y = c(1.1, 1.2, 1.4, 1.3, 1.8, 1.6, 1.4, 1.3, 1.2, 1.1, 1.5, 1.3))

levels(df$star_sign) #alphabetical order

# fit a simple linear model

my_lm <- lm(y ~ 1 + star_sign, data = df)
summary(my_lm) # intercept is based on first level of factor, aquarius

# I want the levels to work properly 1..12 = Aries, Taurus...Pisces so I'm going to redefine the factor levels

df$my_levels <- c("Aries", "Taurus", "Gemini", "Cancer", "Leo", "Virgo", "Libra", "Scorpio", "Sagittarius", "Capricorn", "Aquarius", "Pisces")

df$star_sign <- factor(df$star_sign, levels = df$my_levels)

my_lm <- lm(y ~ 1 + star_sign_, data = df)
summary(my_lm) # intercept is based on first level of factor which is now Aries

# but for my model fit I want the reference level to be Virgo (because reasons)

df$star_sign_2 <- relevel(df$star_sign, ref = "Virgo")

my_lm <- lm(y ~ 1 + star_sign_2, data = df)
summary(my_lm)

df_results <- data.frame(factor_level = names(my_lm$coefficients), coeff = my_lm$coefficients )

# tidy up
rownames(df_results) <- 1:12
df_results$factor_level <- as.factor(gsub("star_sign_2", "", df_results$factor_level))

# change label of "(Intercept)" to "Virgo"
df_results$factor_level <- plyr::revalue(df_results$factor_level, c("(Intercept)" = "Virgo"))

levels(df_results$factor_level) # the levels are alphabetical + Virgo at the front (not same as display order from lm)

因子水平的排列顺序不正确：我想对df_results进行排序，以便使星号的显示顺序与原始顺序相同（白羊座，金牛座...双鱼座），如df$my_levels列。我认为我对操纵因子及其标签/水平等没有很好的了解。因此，我很努力地知道如何做到这一点。

这也是一段冗长而笨拙的代码。有没有更简洁的方法来做这种事情？

谢谢。

（从数学上讲，ps的模型显然是微不足道的，但是对于这些目的来说是可以的-我只是对如何操纵输出感兴趣）

Answer 1

以下是我使用broom包（和dplyr）进行模型系数提取的方法：

library(broom)
library(dplyr)
broom::tidy(my_lm) %>%
  mutate(term = sub("star_sign_2", "", term),
         term = ifelse(term == "(Intercept)", "Virgo", term),
         term = factor(term, levels = unique(term)))
# A tibble: 12 x 5
   term        estimate std.error statistic p.value
   <fct>          <dbl>     <dbl>     <dbl>   <dbl>
 1 Virgo          1.6         NaN       NaN     NaN
 2 Aries         -0.500       NaN       NaN     NaN
 3 Taurus        -0.4         NaN       NaN     NaN
 4 Gemini        -0.2         NaN       NaN     NaN
 5 Cancer        -0.300       NaN       NaN     NaN
 6 Leo            0.20        NaN       NaN     NaN
 7 Libra         -0.2         NaN       NaN     NaN
 8 Scorpio       -0.3         NaN       NaN     NaN
 9 Sagittarius   -0.4         NaN       NaN     NaN
10 Capricorn     -0.500       NaN       NaN     NaN
11 Aquarius      -0.1         NaN       NaN     NaN
12 Pisces        -0.300       NaN       NaN     NaN

设置levels = unique(term)是将级别按出现顺序排列的好方法。

我要提出的另一条建议是在数据框中按您想要的不需要的顺序保留级别的向量，然后在需要建立顺序时引用它。例如，

astro_order = c("Aries", "Taurus", "Gemini", "Cancer", "Leo", "Virgo", "Libra", "Scorpio", "Sagittarius", "Capricorn", "Aquarius", "Pisces")

# messy but effective:
astro_order_virgo1 = factor(astro_order, levels = astro_order) %>% 
  relevel("Virgo") %>%
  levels()

因此，您可以将上面的最后一步替换为term = factor(term, levels = astro_order_virgo1)。

这种将级别顺序分开的方法很好，因为（a）如果对数据帧重新排序，它不会改变；（b）如果数据帧很长，并且您有重复的输入项，它也一样有效您的因子水平。

Answer 2

如果我了解您需要执行的操作，则非常简单。只需在脚本末尾添加以下代码。我也鼓励您深入学习dplyr或tidyverse。让我知道您是否有任何问题：）

## ADDED: 

#WE CREATE AN ID to maintain order in df_results 
df$id <- 1:nrow(df)


library(dplyr)
#Perform left _ join (you could also do inner or right, you'll get the same result in this case )
df_results = left_join(df_results,df, by=c('factor_level'='star_sign_2'))
df_results = df_results %>% arrange(id)

# select desired columns (optionally) 
df_results = df_results %>% select(factor_level,coeff) 


head(df_results)

 factor_level coeff
1        Aries  -0.5
2       Taurus  -0.4
3       Gemini  -0.2
4       Cancer  -0.3
5          Leo   0.2
6        Virgo   1.6

R中的因子水平和建模

2 个答案: