broom

这个软件包提供了三种完成三种不同类型整理的S3方法：GitHub链接

  • tidy: 构建一个总结模型或检验整洁的统计信息data.frame。这包括回归中coefficients and p-values，聚类中的每个集群信息per-cluster或multtest函数的每个测试信息per-test等
  • augment: 将列添加到建模的原始数据中。这包括预测，残差和集群分配等(predictions, residuals, and cluster assignments)
  • glance: 构建一个简洁的模型的单行摘要。这通常包含构建整个模型的计算值such as R^2, adjusted R^2, and residual standard error

考虑一个说明性的例子，对内置mtcars数据集进行线性拟合。

lmfit <- lm(mpg ~ wt, mtcars)

library(broom)
tidy(lmfit)
##          term  estimate std.error statistic      p.value
## 1 (Intercept) 37.285126  1.877627 19.857575 8.241799e-19
## 2          wt -5.344472  0.559101 -9.559044 1.293959e-10

head(augment(lmfit))
##           .rownames  mpg    wt  .fitted   .se.fit     .resid       .hat
## 1         Mazda RX4 21.0 2.620 23.28261 0.6335798 -2.2826106 0.04326896
## 2     Mazda RX4 Wag 21.0 2.875 21.91977 0.5714319 -0.9197704 0.03519677
## 3        Datsun 710 22.8 2.320 24.88595 0.7359177 -2.0859521 0.05837573
## 4    Hornet 4 Drive 21.4 3.215 20.10265 0.5384424  1.2973499 0.03125017
## 5 Hornet Sportabout 18.7 3.440 18.90014 0.5526562 -0.2001440 0.03292182
## 6           Valiant 18.1 3.460 18.79325 0.5552829 -0.6932545 0.03323551
##     .sigma      .cooksd  .std.resid
## 1 3.067494 1.327407e-02 -0.76616765
## 2 3.093068 1.723963e-03 -0.30743051
## 3 3.072127 1.543937e-02 -0.70575249
## 4 3.088268 3.020558e-03  0.43275114
## 5 3.097722 7.599578e-05 -0.06681879
## 6 3.095184 9.210650e-04 -0.23148309

glance(lmfit)
##   r.squared adj.r.squared    sigma statistic      p.value df    logLik
## 1 0.7528328     0.7445939 3.045882  91.37533 1.293959e-10  2 -80.01471
##        AIC      BIC deviance df.residual
## 1 166.0294 170.4266 278.3219          30