Question

我是R的新手，需要知道如何将变量“性别”保持在其常数均值，以便在对医生就诊数据使用泊松回归分析后进行预测。这是我的数据样本：

visits gender    illness    age.category

   1 female       1          <30
   1 female       1          <30
   1   male       3          <30
   1   male       1          <30
   1   male       2          <30
   1 female       5          <30
   1 female       4          <30
   1 female       3          <30
   1 female       2          <30
   1   male       1          <30

我已经获得了如何预测访问率的例子（见下文）男性和女性两周的医生（同时以不变的方式控制年龄和疾病）。

sex <- factor(c('female', 'male',))
avg.age <- mean(DoctorVisits$age)
avg.illness <- mean(DoctorVisits$illness)

hypothetical.person <- expand.grid(age=avg.age,
                               gender=sex,
                               illness=avg.illness)

predict(M.dr, 
    newdata = hypothetical.person,
    type = 'response')

但我需要预测两个星期内医生的就诊率（同时以不变的方式控制性和疾病）。然而，我不知道如何将性别保持在恒定的意义上。我如何确保这一点？

Answer 1

以下是我根据男性和女性及其平均病情为所有不同illness级别创建data.frame的方法。

xy <- read.table(text = "visits gender    illness    age.category
                 1 female       1          <30
                 1 female       1          <30
                 1   male       3          <30
                 1   male       1          <30
                 1   male       2          <30
                 1 female       5          <30
                 1 female       4          <30
                 1 female       3          <30
                 1 female       2          <30
                 1   male       1          <30", header = TRUE)

xy

sex <- factor(c('female', 'male'))
age.groups <- c("< 30", "30-50", "> 50")
avg.illness.by.gender <- aggregate(illness ~ gender, data = xy, FUN = mean)

out <- expand.grid(gender = sex, age = age.groups)
out[out$gender == "female", "illness"] <- avg.illness.by.gender[avg.illness.by.gender$gender == "female", "illness"]
out[out$gender == "male", "illness"] <- avg.illness.by.gender[avg.illness.by.gender$gender == "male", "illness"]
out

  gender   age  illness
1 female  < 30 2.666667
2   male  < 30 1.750000
3 female 30-50 2.666667
4   male 30-50 1.750000
5 female  > 50 2.666667
6   male  > 50 1.750000

泊松和预测

1 个答案: