提取按因子分组的每个回归的R ^ 2(R平方)值

Question

我想知道是否有办法为每个回归方程提取R2.

d <- data.frame(
  state = rep(c('NY', 'CA'), 10),
  year = rep(1:10, 2),
  response= rnorm(20)
)

library(plyr)
models <- dlply(d, "state", function(df) 
  lm(response ~ year, data = df))

ldply(models, coef)
l_ply(models, summary, .print = TRUE)

我试过了

l_ply(models, summary$r.squared, .print = TRUE)

但是这会抛出以下错误消息

Error in summary$r.squared : object of type 'closure' is not subsettable

Answer 1

您可以这样做以获得R平方值和系数:

ldply(models, function(x) {r.sq <- summary(x)$r.squared
                           intercept <- summary(x)$coefficients[1]
                           beta <- summary(x)$coefficients[2]
                           data.frame(r.sq, intercept, beta)})
#  state        r.sq intercept        beta
#1    CA 0.230696121 0.4915617 -0.12343947
#2    NY 0.003506936 0.1971734 -0.01227367

Answer 2

使用扫帚包将统计分析对象转换为data.frames并dplyr用于bind_rows:

library(dplyr) ; library(broom)
cbind(
  state = attr(models, "split_labels"),
  bind_rows(lapply(models, function(x) cbind(
    intercept = tidy(x)$estimate[1],
    beta = tidy(x)$estimate[2],
    glance(x))))
)

  state  intercept        beta  r.squared adj.r.squared    sigma statistic   p.value df    logLik      AIC      BIC deviance df.residual
1    CA 0.38653551 -0.05459205 0.01427426   -0.10894146 1.434599 0.1158477 0.7423473  2 -16.68252 39.36505 40.27280 16.46460           8
2    NY 0.09028554 -0.08462742 0.04138985   -0.07843642 1.287909 0.3454155 0.5729312  2 -15.60387 37.20773 38.11549 13.26968           8