在geom\u smooth and stat\u fit\u tidy中向glm公式添加偏移项

我有一个 data.frame group 三分之一 cluster binomial glm 用一个 logit link function ),并使用 ggplot2 的 geom_bar 和 geom_smooth ,并使用 ggpmisc 的 stat_fit_tidy .

下面是它的样子:

数据:

library(dplyr)

observed.probability.df <- data.frame(cluster = c("c1","c1","c2","c2","c3","c3"), group = rep(c("A","B"),3), p = c(0.4,0.6,0.5,0.5,0.6,0.4))
observed.data.df <- do.call(rbind,lapply(c("c1","c2","c3"), function(l){
  do.call(rbind,lapply(c("A","B"), function(g)
    data.frame(cluster = l, group = g, value = c(rep(0,1000*dplyr::filter(observed.probability.df, cluster == l & group != g)$p),rep(1,1000*dplyr::filter(observed.probability.df, cluster == l & group == g)$p)))
  ))
}))

observed.probability.df$cluster <- factor(observed.probability.df$cluster, levels = c("c1","c2","c3"))
observed.data.df$cluster <- factor(observed.data.df$cluster, levels = c("c1","c2","c3"))
observed.probability.df$group <- factor(observed.probability.df$group, levels = c("A","B"))
observed.data.df$group <- factor(observed.data.df$group, levels = c("A","B"))

绘图:

library(ggplot2)
library(ggpmisc)

ggplot(observed.probability.df, aes(x = group, y = p, group = cluster, fill = group)) +
  geom_bar(stat = 'identity') +
  geom_smooth(data = observed.data.df, mapping = aes(x = group, y = value, group = cluster), color = "black", method = 'glm', method.args = list(family = binomial(link = 'logit'))) + 
  stat_fit_tidy(data = observed.data.df, mapping = aes(x = group, y = value, group = cluster, label = sprintf("P = %.3g", stat(x_p.value))), method = 'glm', method.args = list(formula = y ~ x, family = binomial(link = 'logit')), parse = T, label.x = "center", label.y = "top") +
  scale_x_discrete(name = NULL,labels = levels(observed.probability.df$group), breaks = sort(unique(observed.probability.df$group))) +
  facet_wrap(as.formula("~ cluster")) + theme_minimal() + theme(legend.title = element_blank()) + ylab("Fraction of cells")

假设我有每一个的预期概率 组 offset 到 几何平滑 和 保持健康整洁 s。我该怎么做?

跟随 this Cross Validated post ,我将这些偏移量添加到 observed.data.df :

observed.data.df <- observed.data.df %>% dplyr::left_join(data.frame(group = c("A","B"), p = qlogis(c(0.45,0.55))))

然后尝试添加 offset(p) 表达式到 几何平滑 和 保持健康整洁 :

ggplot(observed.probability.df, aes(x = group, y = p, group = cluster, fill = group)) +
  geom_bar(stat = 'identity') +
  geom_smooth(data = observed.data.df, mapping = aes(x = group, y = value, group = cluster), color = "black", method = 'glm', method.args = list(formula = y ~ x + offset(p), family = binomial(link = 'logit'))) + 
  stat_fit_tidy(data = observed.data.df, mapping = aes(x = group, y = value, group = cluster, label = sprintf("P = %.3g", stat(x_p.value))), method = 'glm', method.args = list(formula = y ~ x + offset(p), family = binomial(link = 'logit')), parse = T, label.x = "center", label.y = "top") +
  scale_x_discrete(name = NULL,labels = levels(observed.probability.df$group), breaks = sort(unique(observed.probability.df$group))) +
  facet_wrap(as.formula("~ cluster")) + theme_minimal() + theme(legend.title = element_blank()) + ylab("Fraction of cells")

但我得到这些警告:

Warning messages:
1: Computation failed in `stat_smooth()`:
invalid type (closure) for variable 'offset(p)' 
2: Computation failed in `stat_smooth()`:
invalid type (closure) for variable 'offset(p)' 
3: Computation failed in `stat_smooth()`:
invalid type (closure) for variable 'offset(p)' 
4: Computation failed in `stat_fit_tidy()`:
invalid type (closure) for variable 'offset(p)' 
5: Computation failed in `stat_fit_tidy()`:
invalid type (closure) for variable 'offset(p)' 
6: Computation failed in `stat_fit_tidy()`:
invalid type (closure) for variable 'offset(p)' 

表示此添加未被识别,并且绘图仅显示条形图:

你知道怎么把抵消项加到 几何平滑 和 保持健康整洁 glm公司 什么?甚至只是为了 几何平滑 glm(注释 保持健康整洁 行)?

几何钢筋 通过拟合得到的预测回归线、SE和p值 外面 ggplot 呼叫( fit <- glm(value ~ group + offset(p), data = observed.data.df, family = binomial(link = 'logit'))