按多组分层的 R 卡方

Question

我有以下带有 3 个因子变量和 1 个百分比变量的 df：

  df <- data.frame(
    group = rep(c("Case", "Control"), each=16),
    timing = rep(c("T0", "T1", "T2", "T3"), each=4, times=2),
    food.type = rep (c("Very healthy", "Healthy", "Unhealthy", "Very bad"), times = 8),
    intake.percentage = runif(32, min=1, max=25)
)

我如何执行测试（卡方）以评估每种食物的组（病例；对照组）之间每次 (T0-T3) 的统计差异？

方便大家理解的情节：

ggplot(df, aes(x = timing,y = intake.percentage, group=group)) +

geom_line(aes(colour=group)) +
geom_point(aes(colour=group, shape=group), size=3) +
theme_light(16) +

facet_grid(facets = .~food.type, scales = 'free')

Answer 1

对于它的编程部分（Whosebug 用于编程问题，Cross Validated 用于统计问题），假设您对每个场景进行了 100 次试验：

set.seed(1)
df <- data.frame(
  group = rep(c("Case", "Control"), each=16),
  timing = rep(c("T0", "T1", "T2", "T3"), each=4, times=2),
  food.type = rep (c("Very healthy", "Healthy", "Unhealthy", "Very bad"), times = 8),
  intake.percentage = runif(32, min=1, max=25)
)
lst <- with(df, split(transform(df, intake.percentage2=100-intake.percentage), list(timing, food.type)))
res <- lapply(lst, function(x) chisq.test(x[, -(1:3)]))
sapply(res, "[", "p.value")
# $T0.Healthy.p.value
# [1] 0.009604491
# 
# $T1.Healthy.p.value
# [1] 0.001794137
# 
# $T2.Healthy.p.value
# [1] 0.04958723
# 
# $T3.Healthy.p.value
# [1] 0.9904441
# 
# $T0.Unhealthy.p.value
# [1] 0.4369428
# ...

按多组分层的 R 卡方

R Chi-squared stratified by multiple groups

statistics

r

chi-squared