我有一个数据框(调查数据)随着时间的推移,感兴趣的结果(1
或T
)适用于两组(T
用于控制,set.seed(3546)
Data <- data.frame(
date = sample((as.Date(as.Date("2011-12-30"):as.Date("2012-01-04"),
origin="1970-01-01")),
1000, replace = TRUE),
treatment_group = sample(c("C", "T"), 1000, replace = TRUE),
outcome = sample(c("1", "0"), 1000, replace = TRUE)
)
用于治疗),如下所示:
1
为此,我分别绘制了两组显示结果Data %>%
mutate(treatment_group = factor(treatment_group, levels = c("T", "C")),
date = as.POSIXct(date)) %>%
group_by(treatment_group, date) %>%
summarise(prop = sum(outcome=="1")/n()) %>% #calculate proportion
ggplot() +
theme_classic() +
xlab("Date") +
ylab('Proportion outcome mentioned')+
scale_color_manual(values = c('C' = 'black', 'T' = 'darkgrey'),
labels = c('C' = 'Remaining sample',
'T' = 'Treated Group'),
name = "Legend") +
geom_smooth(aes(x = date, y = prop, color = treatment_group),
se = F, method = 'loess') +
geom_point(aes(x = date, y = prop, color = treatment_group))
的比例,我用以下代码进行了分析:
1
我想要的 - 但无法弄清楚如何 - 是一行显示每个时间点的值与相应置信度之间的 差异 间隔(对于比例差异的点估计),大致相同(显然风格将保持不变 - 只是为了给你一个想法)
该行应指明该特定日期结果<?php
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的比例之间的差异。非常感谢您的帮助。 :)
答案 0 :(得分:1)
如果您对prop
中的不确定性没有任何衡量标准,您希望如何计算CI?
除此之外,您可以通过以下方式重新设定日期以绘制比例差异:
Data %>%
mutate(
treatment_group = factor(treatment_group, levels = c("T", "C")),
date = as.POSIXct(date)) %>% #convert date to date
group_by(treatment_group, date) %>% #group
summarise(
prop = sum(outcome == "1") / n()) %>% #calculate proportion
spread(treatment_group, prop) %>%
mutate(propdiff = T - C) %>%
ggplot(aes(date, propdiff)) +
geom_line() +
geom_point()
说明:关注summarise
后,我们会将数据从long转换为wide,并将propdiff
计算为prop(T) - prop(C)
。