计算多元线性回归的预测

如何解决计算多元线性回归的预测

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

解决方法

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,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,5L,4L,0L,3L,4L),smoker = c("no","yes","no","no"),region = structure(c(1L,.Label = c("northeast","northwest","southeast"),class = "factor")),row.names = c(NA,-10L),class = "data.frame")