我希望有人可以为我解释与glm(). 我不明白建议的诊断图。似乎更有信息量的情节是针对拟合值进行绘图，但也许我不明白一些事情。这是代码：

result <- glm(survive~age, data=donner, family=binomial)
# Why is this plotted against the respondent index?
plot(residuals(result,type="pearson"), main="pearson residual plot")

重现上述示例的数据：

> dput(donner)
structure(list(age = c(23L, 40L, 40L, 30L, 28L, 40L, 45L, 62L, 
65L, 45L, 25L, 28L, 28L, 23L, 22L, 23L, 28L, 15L, 47L, 57L, 20L, 
18L, 25L, 60L, 25L, 20L, 32L, 32L, 24L, 30L, 15L, 50L, 21L, 25L, 
46L, 32L, 30L, 25L, 25L, 25L, 30L, 35L, 23L, 24L, 25L), sex = c(1L, 
0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 
0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 1L, 0L, 0L, 
1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L), survive = c(0L, 
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 
1L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 1L, 
0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L)), .Names = c("age", 
"sex", "survive"), class = "data.frame", row.names = c(NA, -45L
))