Question

我有关于保险的数据；年龄，性别，BMI，儿童，吸烟者，地区和收费。性别，吸烟者和地区是因素。性别：男，女，吸烟者：是，否，地区：东北，东南，西南，西北。

m2 <- lm(charges ~ age + sex + bmi + children + smoker + region)

在用数据拟合线性回归模型之后，我需要预测：男性，年龄= 40，bmi = 30，吸烟者=是，区域=西北。读取数据后，我尝试分解分类变量

data$sex <- as.factor(data$sex)
data$region <- as.factor(data$region)

使用预测功能：

predict(m2, list(age=40, sex=factor(male), bmi=30, children=2, smoker=factor(yes), 
                 region=factor(northwest)), int="p", level=0.98)

我只会得到错误。请帮忙

Answer 1

代替重新定义因素，只需在predict的引号中使用因素级别即可。

predict(m2, list(age=40, sex="male", bmi=30, children=2, smoker="yes", 
                 region="northwest"), int="p", level=0.98)
#         fit       lwr      upr
# 1 -1.978994 -9.368242 5.410254

数据

dat <- structure(list(charges = c(1.37095844714667, -0.564698171396089, 
0.363128411337339, 0.63286260496104, 0.404268323140999, -0.106124516091484, 
1.51152199743894, -0.0946590384130976, 2.01842371387704, -0.062714099052421
), age = c(20L, 58L, 44L, 53L, 22L, 51L, 20L, 75L, 59L, 41L), 
    sex = structure(c(2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("female", 
    "male"), class = "factor"), bmi = c(25.3024309248682, 24.6058854935878, 
    25.7881406228236, 25.6707038267505, 24.0508191903124, 25.036135738485, 
    27.115755613237, 25.1674409043556, 24.1201634714689, 25.9469131749433
    ), children = c(4L, 1L, 5L, 1L, 1L, 4L, 0L, 0L, 3L, 4L), 
    smoker = c("no", "yes", "yes", "no", "no", "yes", "yes", 
    "yes", "yes", "no"), region = structure(c(1L, 2L, 2L, 3L, 
    1L, 2L, 3L, 3L, 3L, 2L), .Label = c("northeast", "northwest", 
    "southeast"), class = "factor")), row.names = c(NA, -10L), class = "data.frame")

计算多元线性回归的预测

1 个答案: