plot mixed effect model with interaction in fixed effects in ggplot

Question

I am plotting the interaction of the fixed effects in a mixed effects model based on a lmer() object. To do so, I predict new values based on my model. This works fine, except that, due to how I generate them, the predictions stretch out over the whole possible x-axis range. I could now restrict the predicted regression lines to the range of their respective grouping variable by defining new.dat based on a loop (change max and min values depending on the grouping variable "Variety") etc., but - is there a more elegant/ easier solution to plot this? Am I missing out on something (I'm relatively new to R)?

Data:

library(datasets)
data("Oats")

# manipulate data so it resembles more my actual data
Oats <- Oats %>%
  filter((Variety == "Golden Rain"  & nitro>=0.2) | (Variety == "Marvellous" & nitro <=0.4) | (Variety == "Victory" & nitro<=0.4 & nitro>=0.2))  #%>%

Model & plotting:

mod2 <- lmer(yield ~ nitro * Variety + (1| Variety), data=Oats)

new.dat <- data.frame(nitro=seq(min(Oats$nitro),max(Oats$nitro), length.out = 48), Variety= Oats$Variety)
new.dat$pred<-predict(mod2,newdata=new.dat,re.form=~0)

ggplot(data=Oats, aes(x=nitro, y=yield, col = Variety)) +
  geom_point() +
  geom_line(data=new.dat, aes(y=pred)) +
  geom_point(data=new.dat, aes(y=pred))

Thanks a lot for every hint!

cimentadaj · Accepted Answer · 2020-01-29 16:34:10Z

You can get that by calculating the min/max per group and then calculating sequences by group. Keeping with tidyverse since your code already uses it:

library(tidyverse)
library(pairwiseCI)
#> Loading required package: MCPAN
#> Loading required package: coin
#> Loading required package: survival
library(lme4)
#> Loading required package: Matrix
#> 
#> Attaching package: 'Matrix'
#> The following objects are masked from 'package:tidyr':
#> 
#>     expand, pack, unpack

data("Oats")

 ## manipulate data so it resembles more my actual data
Oats <-
  Oats %>%
  filter((Variety == "Golden Rain"  & nitro>=0.2) | (Variety == "Marvellous" & nitro <=0.4) | (Variety == "Victory" & nitro<=0.4 & nitro>=0.2))  #%>%

mod2 <- lmer(yield ~ nitro * Variety + (1| Variety), data=Oats)
#> Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
#> unable to evaluate scaled gradient
#> Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
#> Hessian is numerically singular: parameters are not uniquely determined

## Calculate min/max by group
all_vals <-
  Oats %>%
  group_by(Variety) %>%
  summarize(min_nitro = min(nitro),
            max_nitro = max(nitro))
## Calculate sequence for each group
new.dat <-
  all_vals %>%
  group_split(Variety) %>%
  map_dfr(~ data.frame(Variety = .x$Variety, nitro = seq(.x$min_nitro, .x$max_nitro, length.out = 20)))


new.dat$pred<-predict(mod2,newdata=new.dat,re.form=~0)

ggplot(data=Oats, aes(x=nitro, y=yield, col = Variety)) +
  geom_point() +
  geom_line(data=new.dat, aes(y=pred)) +
  geom_point(data=new.dat, aes(y=pred))

Thank you, that is indeed a better solution that using loops etc. and it does exactly what I want!

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