#Read in the ANT data (see ?ANT).
data(ANT)
head(ANT)
#fit a mixed effects model to the error rate data
er_fit = lmer(
	formula = error ~ cue*flank*group + (1|subnum)
	, family = binomial
	, data = ANT
)
#obtain the predictions from the model
er_preds = ezPredict(
	fit = er_fit
)
#compute 95% CI for each prediction
er_preds$lo = er_preds$value - qnorm(.975)*sqrt(er_preds$var)
er_preds$hi = er_preds$value + qnorm(.975)*sqrt(er_preds$var)
#visualize the predictions
ggplot(
	data = er_preds
	, mapping = aes(
		x = flank
		, y = value
		, ymin = lo
		, ymax = hi
	)
)+
geom_point(
	alpha = .75
)+
geom_line(
	alpha = .5
)+
geom_errorbar(
	alpha = .5
)+
facet_grid(
	cue ~ group
)+
labs(
	y = 'Error Rate (log odds)'
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