R 在geom_smooth and stat_fit_tidy中向glm公式添加偏移项

我有一个

data.frame

，在三个

cluster

s中每两个

group

s的计数，我正在对其进行逻辑回归（

binomial

glm

，带有

logit

链接函数

），并使用

ggplot2

的

geom\u-bar

和

geom\u平滑绘制，并使用
ggpmisc
的
stat\u fit\u tidy
添加p值

下面是它的样子：

数据：


假设我有每个
组的预期概率，我想将其作为
偏移量添加到
geom\u smooth
和
stat\u fit\u tidy
glm
s。我该怎么做

接下来，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))))

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，且绘图仅显示条形图：

您知道如何将偏移项添加到
geom\u smooth
和
stat\u fit\u tidy
glm
s中吗？甚至仅仅是对
geom\u smooth
glm（注释
stat\u fit\u tidy
行）

或者，是否可以将通过在
ggplot
调用外部拟合
glm
获得的预测回归线、SE和p值添加到
geom\u条中（
fit问题是，在ggplot中，模型公式中的
x
和
y
代表美学，而不是
数据中的变量名称，即，在ggplot中，模型公式中的名称代表美学。没有
p
美学，因此尝试拟合时，找不到
p
。无法传递数字v这里的ector作为ggplot将数据分成几组，并分别为每组拟合模型，我们可以将单个数值向量作为常量值传递。我认为需要定义一个新的伪美学及其相应的比例，才能以这种方式进行拟合

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

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")

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)'