R 使用加权t检验总结多列
我有以下数据，想计算加权p值。我复习了。但我的版本应该使用重量。我可以使用Code2来实现这一点。但是有30多列。如何有效地计算加权p值 代码1R 使用加权t检验总结多列,r,statistics,tidyverse,R,Statistics,Tidyverse,我有以下数据，想计算加权p值。我复习了。但我的版本应该使用重量。我可以使用Code2来实现这一点。但是有30多列。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
# 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
我们可以将
摘要与交叉使用
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])
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])