Prediction in a linear mixed model in R-CodePudding

Consider the sleepstudy data in the lme4 package as shown below. The contains 18 subjects with repeated measurements of Reaction (Reaction is the response) taken on different days.

library("lme4")
head(sleepstudy)
  Reaction Days Subject
1 249.5600    0     308
2 258.7047    1     308
3 250.8006    2     308
4 321.4398    3     308
5 356.8519    4     308
6 414.6901    5     308

The following code fits a linear mixed model with a random intercept.

fit1 = lmer(Reaction ~ Days   (1 | Subject), data = sleepstudy)

We can obtain subject-specific random intercept using "ranef(fit1)". Also, one can use "predict(fit1)" to give predictions of the response for all the time points in the original data.

However, I would like to predict the response (Reaction) in R for the 18 subjects at Day=12 and Day 14 (Day 12 and 14 are days that are not in the original data but would like to make a prediction for Reaction). That is, I should end up with a dataset that looks like this.

 Days  Subject  Predicted_Response
  12     308        
  12     309
  ...
  12     371
  12     372

  14     308        
  14     309
  ...
  14     371
  14     372

CodePudding user response：

We can accomplish this with the "newdata" argument of the predict method:

library("lme4")
fit1 = lmer(Reaction ~ Days   (1 | Subject), data = sleepstudy)
newdata <- expand.grid(
  Days = c(12, 14),
  Subject = unique(sleepstudy$Subject)
)

newdata$Predicted_Response <- predict(fit1, newdata = newdata)

   Days Subject Predicted_Response
1    12     308           417.7962
2    14     308           438.7308
3    12     309           299.1630
4    14     309           320.0976
5    12     310           313.9040
6    14     310           334.8385
7    12     330           381.4190
8    14     330           402.3536
9    12     331           387.2287
10   14     331           408.1633
11   12     332           385.2338
12   14     332           406.1683
... etc ...