基于线性模型预测一些缺失值

Question

datax <- matrix(1:32, nrow = 8)
datax[2:5,1] <- NA
m <- data.frame(datax)
names(m)[c(1:4)] <- c("Length", "Width", "sex", "height")
model <- glm(Length ~ Width + sex + height, data = m)

你如何根据模型预测 NA 值（代码只是作为示例）

我有一个包含 15 个变量的数据集，响应变量有一些缺失值。我如何根据从该数据集构建的线性模型预测响应变量的缺失值？

Answer 1

我想你可以尝试像下面这样预测

options(warn=-1) # shut down warnings
m[is.na(m[1]),1] <- predict(model,newdata = subset(m[-1],is.na(m[1])))
options(warn=1) # turn on warnings

这样

> m
  Length Width sex height
1      1     9  17     25
2      2    10  18     26
3      3    11  19     27
4      4    12  20     28
5      5    13  21     29
6      6    14  22     30
7      7    15  23     31
8      8    16  24     32

Answer 2

如何将您的数据子集化为有和没有缺失值的部分，基于后者创建线性模型并通过 predict() 将缺失值归因于前者？

library(tidyverse)

datax <- matrix(1:32, nrow = 8)
datax[2:5,1] <- NA
m <- data.frame(datax)
names(m)[c(1:4)] <- c("Length", "Width", "sex", "height")

# Creating an index of rows with missing values in "Length"
missing_index <- which(is.na(m$Length))

# Subsetting rows with missing values
m_missing <- m[missing_index,]

# Subsetting the rest
m_rest <- m[-missing_index,]

# Creating a linear model on m_rest and making predictions on m_missing
model <- lm(Length ~ ., data = m_rest)
predictions <- predict(model, newdata = m_missing %>% select(-Length))

# Insert missing values into the original dataframe
m[missing_index, "Length"] <- predictions

导致：

> print(m)
  Length Width sex height
1      1     9  17     25
2      2    10  18     26
3      3    11  19     27
4      4    12  20     28
5      5    13  21     29
6      6    14  22     30
7      7    15  23     31
8      8    16  24     32

基于线性模型预测一些缺失值

predict some missing values based on linear model

r

glm