R中的因子水平和建模

Question

以下代码运行一个非常简单的lm()，并尝试在一个小数据框中总结结果（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)

因子水平的顺序不正确：我想对df_results 进行排序，以便星号以与它们最初（白羊座、金牛座...双鱼座）相同的顺序出现，如@ 987654324@专栏。我认为我对操纵因素及其标签/级别等没有很好的了解，所以我很难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。

（ps 从数学上讲，模型显然是微不足道的，但对于这些目的来说没关系 - 我只是对如何操作输出感兴趣）

Answer 1

以下是我使用broom 包（和dplyr）提取模型系数的方法：

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) 是一个很好的技巧，可以将关卡按出现的顺序排列。

我的另一个建议是在数据框中按照您希望不的顺序保留级别向量，然后在需要建立顺序时参考它。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()

那么你可以用term = factor(term, levels = astro_order_virgo1)替换上面的最后一步。

这种保持级别顺序分开的方法很好，因为 (a) 如果您重新排序数据框，它不会改变，并且 (b) 如果您的数据框很长并且您重复输入你的因素水平。

Answer 2

如果我了解您需要做什么，这非常简单。 只需在脚本末尾添加以下代码。我还鼓励您深入研究 dplyr 或 tidyverse。 如果您有任何问题，请告诉我:)

## 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