Order of predictions from merTools predictInterval() - r

Question

I'm encountering an issue with predictInterval() from merTools. The predictions seem to be out of order when compared to the data and midpoint predictions using the standard predict() method for lme4. I can't reproduce the problem with simulated data, so the best I can do is show the lmerMod object and some of my data.
> # display input data to the model
> head(inputData)
id y x z
1 calibration19 1.336 0.531 001
2 calibration20 1.336 0.433 001
3 calibration22 0.042 0.432 001
4 calibration23 0.042 0.423 001
5 calibration16 3.300 0.491 001
6 calibration17 3.300 0.465 001
> sapply(inputData, class)
id y x z
"factor" "numeric" "numeric" "factor"
>
> # fit mixed effects regression with random intercept on z
> lmeFit = lmer(y ~ x + (1 | z), inputData)
>
> # display lmerMod object
> lmeFit
Linear mixed model fit by REML ['lmerMod']
Formula: y ~ x + (1 | z)
Data: inputData
REML criterion at convergence: 444.245
Random effects:
Groups Name Std.Dev.
z (Intercept) 0.3097
Residual 0.9682
Number of obs: 157, groups: z, 17
Fixed Effects:
(Intercept) x
-0.4291 5.5638
>
> # display new data to predict in
> head(predData)
id x z
1 29999900108 0.343 001
2 29999900207 0.315 001
3 29999900306 0.336 001
4 29999900405 0.408 001
5 29999900504 0.369 001
6 29999900603 0.282 001
> sapply(predData, class)
id x z
"factor" "numeric" "factor"
>
> # estimate fitted values using predict()
> set.seed(1)
> preds_mid = predict(lmeFit, newdata=predData)
>
> # estimate fitted values using predictInterval()
> set.seed(1)
> preds_interval = predictInterval(lmeFit, newdata=predData, n.sims=1000) # wrong order
>
> # estimate fitted values just for the first observation to confirm that it should be similar to preds_mid
> set.seed(1)
> preds_interval_first_row = predictInterval(lmeFit, newdata=predData[1,], n.sims=1000)
>
> # display results
> head(preds_mid) # correct prediction
1 2 3 4 5 6
1.256860 1.101074 1.217913 1.618505 1.401518 0.917470
> head(preds_interval) # incorrect order
fit upr lwr
1 1.512410 2.694813 0.133571198
2 1.273143 2.521899 0.009878347
3 1.398273 2.785358 0.232501376
4 1.878165 3.188086 0.625161201
5 1.605049 2.813737 0.379167003
6 1.147415 2.417980 -0.108547846
> preds_interval_first_row # correct prediction
fit upr lwr
1 1.244366 2.537451 -0.04911808
> preds_interval[round(preds_interval$fit,3)==round(preds_interval_first_row$fit,3),] # the correct prediction ends up as observation 1033
fit upr lwr
1033 1.244261 2.457012 -0.0001299777
>
To put this into words, the first observation of my data frame predData should have a fitted value around 1.25 according to the predict() method, but it has a value around 1.5 using the predictInterval() method. This does not seem to be simply due to differences in the prediction approaches, because if I restrict the newdata argument to the first row of predData, the resulting fitted value is around 1.25, as expected.
The fact that I can't reproduce the problem with simulated data leads me to believe it has to do with an attribute of my input or prediction data. I've tried reclassifying the factor variable as character, enforcing the order of the rows prior to fitting the model, between fitting the model and predicting, but found no success.
Is this a known issue? What can I do to avoid it?

Answer 1

I have attempted to make a minimal reproducible example of this issue, but have been unsuccessful.
library(merTools)
d <- data.frame(x = rnorm(1000), z = sample(1:25L, 1000, replace=TRUE),
id = sample(LETTERS, 1000, replace = TRUE))
d$z <- as.factor(d$z)
d$id <- factor(d$id)
d$y <- simulate(~x+(1|z),family = gaussian,
newdata=d,
newparams=list(beta=c(2, -1.1), theta=c(.25),
sigma = c(.23)), seed =463)[[1]]
lmeFit <- lmer(y ~ x + (1|z), data = d)
predData <- data.frame(x = rnorm(25), z = sample(1:25L, 25, replace=TRUE),
id = sample(LETTERS, 25, replace = TRUE))
predData$z <- as.factor(predData$z)
predData$id <- factor(predData$id)
predict(lmeFit, predData)
predictInterval(lmeFit, predData)
predictInterval(lmeFit, predData[1, ])
But, playing around with this code I was not able to recreate the error observed above. Can you post a synthetic example or see if you can create a synthetic example?
Or can you test the issue first coercing the factors to characters and seeing if you see the same re-ordering issue?