我创建了一个数据集,用于将治疗随机分配给实验对象。个人将接受三次治疗。有7种治疗方法,我需要确保一个人在被随机分配的同时不会多次接受相同的治疗方法。有35个个体和7种治疗方法,因此每种治疗方法有5个重复样本。
数据:
set.seed(566)
treatments<-rep(c(2,4,8,16,32,64,100), each=5)
random_design<-data.frame(individual=c(1:35), trial1=sample(treatments), trial2=sample(treatments), trial3=sample(treatments))
如您所见,有些人在不同的试验中受到相同的待遇。有没有一种方法可以对sample()施加条件,以使单个x不能与先前的试验具有相同的待遇?
您似乎想先随机分配给个体三个治疗,因此,如果有K种治疗,并且想要随机选择3个而没有替代,那么对每个个体都这样做,然后合并治疗效果。例如,使用您的数字并使用data.table
,这是一个解决方案:
set.seed(566)
library(data.table)
exp_num = 7
#set up a data.table to hold treatment effects
treat_dt = data.table("experiment_num" = 1:exp_num, "treatment_effect" = c(2,4,8,16,32,64,100))
#now create a datatable of subjectsXtrials
subj_dt = data.table(expand.grid("id" = 1:35, "trial" = paste0("trial",1:3)))
#now randomly assign three experiments without replacement by id
subj_dt[, exp_assigned := sample(1:exp_num,3, replace = F), by = id]
#now merge in effects with treat_dt by experiment...
subj_dt = merge(subj_dt,treat_dt, by.x = "exp_assigned",by.y = "experiment_num", all.x = T, all.y = F)
#and youre done! option to get back a dataset where each id is a single row
alt_dt = dcast(subj_dt[,.(id,trial,treatment_effect)], id ~ trial, value.var = "treatment_effect")
然后输出显示为alt_dt
> head(alt_dt)
id trial1 trial2 trial3
1: 1 100 32 8
2: 2 100 64 32
3: 3 4 16 2
4: 4 100 64 8
5: 5 8 16 4
6: 6 64 100 8