CODEWITHSUNDEEP
rplotggplot2
entne
entne

Reputation: 137

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

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.

Upvotes: 0

Views: 1990

Answers (2)

ziggystar
ziggystar

Reputation: 28680

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")

enter image description here 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 ~ .)

enter image description here

Upvotes: 1

CMichael
CMichael

Reputation: 1866

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

enter image description here

Upvotes: 0

Related Questions