Need help to estimate adequately the parameters of a logistic model

I analyzed a data matrix using the Rasch model but I have the next problem: I need to inverse the sign of every estimated item difficulty to have a credible value. For example, if brm function return me a value of .9 for a specific item, I have to inverse the sign so the value of -.9 is the good one. Anybody knows how I can fix this with the next code?


formula15 <- bf(Xb15 ~ 1 + (1 | It15) + (1 | P15))
prior15 <- prior(“normal(0, 3)”, class = “sd”, group = “It15”) + prior(“normal(0, 3)”, class = “sd”, group = “P15”)
fit15 <- brm(formula = formula15,
data = X15,
family = brmsfamily(“bernoulli”,“logit”),
prior = prior15)

I guess the estimates are for intercepts instead of item difficulties. In IRT intercepts and item difficulties have exactly the opposite values, so you’re good.

Thank you so much for your answer :-) In this case, do you think I can also simply inverse the values of the 95% credible intervals from the output of the brm function?

yes. as long as your model itself is correct, intercepts are exactly the negative item difficulties.