如何解决使用加权 t 检验汇总多列
我有以下数据，想计算加权 p 值。我查看了 dplyr summarise multiple columns using t.test。但我的版本应该使用重量。我可以使用 Code2 来做到这一点。但是有超过 30 列。如何有效地计算加权 p 值？
代码 1
# A tibble: 877 x 5
cat population farms farmland weight
<chr> <dbl> <dbl> <dbl> <dbl>
1 Treated 9.89 8.00 12.3 1
2 Control 10.3 7.81 12.1 0.714
3 Control 10.2 8.04 12.4 0.156
4 Control 10.3 7.97 12.1 0.340
5 Control 10.9 8.87 12.7 2.85
6 Control 10.4 8.35 12.5 0.934
7 Control 10.5 8.58 12.9 0.193
8 Control 10.6 8.57 12.6 0.276
9 Control 10.2 8.54 12.5 0.344
10 Control 10.5 8.76 12.6 0.625
# … with 867 more rows
代码 2
wtd.t.test(
x = df$population[df$cat == "Treated"],y = df$population[df$cat == "Control"],weight = df$weight[df$cat == "Treated"],weighty = df$weight[df$cat == "Control"])$coefficients[3]
解决方法
我们可以将 summarise 与 across 一起使用
library(dplyr)
df %>%
summarise(across(c(population:farmland),~ weights::wtd.t.test(x = .[cat == 'Treated'],y = .[cat == 'Control'],weight = weight[cat == 'Treated'],weighty= weight[cat == 'Control'])$coefficients[3]))
或者使用 lapply/sapply
sapply(df[2:4],function(v)
weights::wtd.t.test(x = v[df$cat == "Treated"],y = v[df$cat == "Control"],weight = df$weight[df$cat == "Treated"],weighty = df$weight[df$cat == "Control"])$coefficients[3])
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