How can I plot an interaction in R when using a negative binomial model?

Question

I'm new to R. I used a negative binomial model to test the effects of 2 variables (1 binary variable and 1 continuous variable) and a count response variable. I also added their interaction to the model.

Since the results from the glm.nb is minimal, I would like to plot the result somehow, especially the interaction.

I've done this to run the model:

Y<- cbind(N_Cooperations)
Model8 <- glm.nb(Y ~ Condition + NR + Condition*NR)
summary(Model8)

Call:
glm.nb(formula = Y ~ Condition + NR + Condition * NR, init.theta = 2.012332023, 
    link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.2063  -0.9508  -0.1757   0.3389   2.5682  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)   
(Intercept)    1.5379     0.6920   2.222  0.02626 * 
Condition     -2.9514     1.0876  -2.714  0.00665 **
NR            -0.1470     0.2065  -0.712  0.47654   
Condition:NR   0.7771     0.3170   2.451  0.01423 * 
---

Then I tried to use plot(allEffects(Model8)) from the effects package to plot the interaction, but this is the message I received:

plot(allEffects(Model8))
Error in mod.matrix[, components] : subscript out of bounds
In addition: Warning messages:
1: In factor.cols & stranger.cols :
  longer object length is not a multiple of shorter object length
2: In (!factor.cols) & stranger.cols :
  longer object length is not a multiple of shorter object length

What am I missing?

Again, I'm very new to R. Sorry in advance if this sounds silly.

Answer 1

Most likely you have some other variables in your environment that is throwing the error. It would be useful if you place all your variables into a data.frame. It works for me with an example dataset:

library(MASS)
library(effects)

set.seed(111)
df = data.frame(Y = rnbinom(100,mu=10,size=1),
NR = runif(100),Condition=rbinom(100,1,0.5))

fit = glm.nb(Y ~ Condition*NR,data=df)

The interaction term is specified using : and this * is used for factor crossing. See help page if you are unclear. Below I can do the summary and we get a similar output to yours:

summary(fit)

Call:
glm.nb(formula = Y ~ Condition * NR, data = df, init.theta = 1.19503151, 
    link = log)

Deviance Residuals: 
    Min       1Q   Median       3Q      Max  
-2.3798  -1.0970  -0.2340   0.4323   2.0848  

Coefficients:
             Estimate Std. Error z value Pr(>|z|)    
(Intercept)    1.4744     0.2978   4.952 7.36e-07 ***
Condition      0.9414     0.3973   2.369   0.0178 *  
NR             1.0294     0.5003   2.058   0.0396 *  
Condition:NR  -0.9773     0.6809  -1.435   0.1512   

Plotting it works:

plot(allEffects(fit))