How to plot estimate values for a lmer regression model in R?

Question

I have data that looks like this:

height <- c(1,2,3,4,2,4,6,8)
weight <- c(12,13,14,15,22,23,24,25)
type <- c("Wheat","Wheat","Wheat","Wheat","Rice","Rice","Rice","Rice")
set <- c(1,1,1,1,2,2,2,2)
dat <- data.frame(set,type,height,weight)

I run a lmer model with set as a random effect in R:

mod <- lmer(weight~height + type + (1|set), data = dat)

Now, I want to plot the estimates of the model and plot a regression, with weight on the x-axis and height on the y-axis, facet(~type)

I use the predict function as follows

dat$pred <- predict(mod, type = "response")

And I want to achieve a ggplot that will look like this:

ggplot(dat,aes(x = weight, y = height)) +
geom_point() + geom_smooth(method="lm", fill=NA) + facet_grid(~ type, scales = "free")

However, I note that the predict function has only a singular output. How do I plot that to achieve the same as above? Or do I have to store two different predict responses, and then plug it into the x,y of ggplot?

Marius · Accepted Answer · 2017-09-05 02:11:07Z

1

I can adapt your plot to show raw vs. predicted values like this:

ggplot(dat,aes(y = height)) +
    geom_point(aes(x = weight)) +
    geom_line(aes(x = pred)) + 
    facet_grid(~ type, scales = "free")

In your example plot though you have weight, the outcome variable in your model, on the x-axis, which is confusing. Normally you would have the outcome/predicted variable on the y-axis, so I would have plotted your model predictions like:

ggplot(dat,aes(x = height)) +
    geom_point(aes(y = weight)) +
    geom_line(aes(y = pred)) + 
    facet_grid(~ type, scales = "free")

answered Sep 5, 2017 at 2:11

Marius

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