将数据框与缺失值对齐 [英] Aligning Data frame with missing values

问题描述

我正在使用具有许多NA值的数据框.虽然我可以创建线性模型，但由于缺少值和缺少指标列，因此我随后无法将模型的拟合值与原始数据对齐.

I'm using a data frame with many NA values. While I'm able to create a linear model, I am subsequently unable to line the fitted values of the model up with the original data due to the missing values and lack of indicator column.

这是一个可复制的示例:

Here's a reproducible example:

library(MASS)
dat <- Aids2
# Add NA's 
dat[floor(runif(100, min = 1, max = nrow(dat))),3] <- NA
# Create a model
model <- lm(death ~ diag + age, data = dat)
# Different Values
length(fitted.values(model))
# 2745
nrow(dat)
# 2843

推荐答案

这里实际上有三种解决方案:

There are actually three solutions here:

1. 填充NA自己调整值；
2. 使用predict()计算拟合值；
3. 自己删除不完整的案例，仅将完整的案例传递给lm().

1. pad NA to fitted values ourselves;
2. use predict() to compute fitted values;
3. drop incomplete cases ourselves and pass only complete cases to lm().

选项1

## row indicator with `NA`
id <- attr(na.omit(dat), "na.action")
fitted <- rep(NA, nrow(dat))
fitted[-id] <- model$fitted
nrow(dat)
# 2843
length(fitted)
# 2843
sum(!is.na(fitted))
# 2745

选项2

## the default NA action for "predict.lm" is "na.pass"
pred <- predict(model, newdata = dat)  ## has to use "newdata = dat" here!
nrow(dat)
# 2843
length(pred)
# 2843
sum(!is.na(pred))
# 2745

选项3

或者，您可以简单地将没有任何NA的数据帧传递给lm():

Alternatively, you might simply pass a data frame without any NA to lm():

complete.dat <- na.omit(dat)
fit <- lm(death ~ diag + age, data = complete.dat)
nrow(complete.dat)
# 2745
length(fit$fitted)
# 2745
sum(!is.na(fit$fitted))
# 2745

总之，

• 选项1 通过填充NA来直接进行对齐"，但是我认为人们很少采用这种方法；
• 选项2 确实很简单，但是计算成本更高；
• 选项3 是我的最爱，因为它使所有事情变得简单.

• Option 1 does the "alignment" in a straightforward manner by padding NA, but I think people seldom take this approach;
• Option 2 is really simple, but it is more computationally costly;
• Option 3 is my favourite as it keeps all things simple.

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