R: Plot interaction between categorial Factor and continuous Variable on DV

Question

What I have is a 3-Levels Repeated Measures Factor and a continuous variable (Scores in psychological questionnaire, measured only once pre-experiment, NEO), which showed significant interaction together in a Linear Mixed Effects Model with a Dependent Variable (DV; State-Scores measured at each time level, IAS).

To see the nature of this interaction, I would like to create a plot with time levels on X-Axis, State-Score on Y-Axis and multiple curves for the continuous variable, similar to this . The continuous variable should be categorized in, say quartiles (so I get 4 different curves), which is exactly what I can't achieve. Until now I get a separate curve for each value in the continuous variable.

My goal is also comparable to this , but I need the categorial (time) variable not as separate curves but on the X-Axis.

I tried out a lot with different plot functions in R but did'nt manage to get what I want, maybe because I am not so skilled in dealing with R.

F. e.

gplot(Data_long, aes(x = time, y = IAS, colour = NEO, group = NEO)) + 
  geom_line()

from the first link shows me dozens of curves (one for each value in the measurement NEO) and I can't find how to group continuous variables in a meaningful way in that gplot function.

Edit:

Original Data:

http://www.pastebin.ca/2598926

(I hope it is not too inconvenient.)

This object (Data_long) was created/converted with the following line:

Data_long <- transform(Data_long0, neo.binned=cut(NEO,c(25,38,46,55,73),labels=c(".25",".50",".75","1.00")))

Every value in the neo.binned col seems to be set correctly with enough cases per quantile.

What I then tried and didn't work:

ggplot(Data_long, aes(x = time, y = ias, color = neo.binned)) + stat_summary(fun.y="median",geom="line")

geom_path: Each group consist of only one observation. Do you need to adjust the group >aesthetic?

I got 92 subjects and values for NEO between 26-73. Any hints what to enter for cut and labels function? Quantiles are 0% 25% 50% 75% 100% 26 38 46 55 73.

Answer 1

Do you mean something like this? Here, your data is binned according to NEO into three classes, and then the median of IAS over these bins is drawn. Check out ?cut .

Data_long <- transform(Data_long, neo.binned=cut(NEO,c(0,3,7,10),labels=c("lo","med","hi")))

Plot everything in one plot.

ggplot(Data_long, aes(x = time, y = IAS, color = neo.binned)) 
  + stat_summary(aes(group=neo.binned),fun.y="median",geom="line")

And stealing from CMichael's answer you can do it all in multiple (somehow you linked to facetted plots in your question):

ggplot(Data_long,aes(x=time,y=IAS)) 
  + stat_summary(fun.y="median",geom="line") 
  + facet_grid(neo.binned ~ .)

Answer 2

Do you mean facetting @ziggystar initial Plot?

quantiles = quantile(Data_long$NEO,c(0.25,0.5,0.75))

Data_long$NEOQuantile = ifelse(Data_long$NEO<=quantiles[1],"first NEO Quantile",
                               ifelse(Data_long$NEO<=quantiles[2],
                                      "second NEO Quantile",
                                      ifelse(Data_long$NEO<=quantiles[3],
                                             "third NEO Quantile","forth NEO Quantile")))

require(ggplot2)
p = ggplot(Data_long,aes(x=time,y=IAS)) + stat_quantile(quantiles=c(1),formula=y ~ x)
p = p + facet_grid(.~NEOQuantile)
p