How to plot main (fixed) effect of GLMM on response variable

Hello all,

I am looking to plot the significant results of a GLMM output. One of the significant predictor variables (noise) is categorical and binary (can take 2 values - or names might be more accurate). The other two are continuous (self-report scales). However, I'm not sure what exactly this should look like - especially the categorical one as maybe a line graph isn't appropriate? - so I guess this is also a statistics question. Anyhow, I wrote code to create a line graph that plots noise against the estimated means but I'm not sure this is right nor how to interpret it other than using the gradient of the slope.

## Plot line graph for background noise main effect 
# Obtain estimated marginal means
em_means <-emmeans(model, specs = "noise")

# Extract predicted values
predicted_values <- predict(em_means)

# Step 4: Create a Data Frame for Plotting
plot_data <- data.frame(noise = unique(d_multiple_regression$noise), y = predicted_values)

# Step 5: Plot the Line Graph
ggplot(plot_data, aes(x = noise, y = y)) +
  geom_line() +
  labs(x = "Background noise", y = "Estimated Means") +

The code below outputs a similar graph but with the predicted probabilities of the response variable and a grey sloping overlay area around the line:

plot(ggpredict(model, terms = "noise"))

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