Dear paul,
Thank you for showing me how to formulate the non-linear model and set the bounded priors.
Nevertheless, I have a follow up question, and I was not sure if I needed to make a new post?
In fact, my parameters are assumed to depend on varios physical and chemical parameters.
How do I set priors on both the Intercept and the predictors? For example,
bform <- bf(firmness ~ (minutes > tg) * Gm * (1 - exp(-k * (minutes - tg))),
tg ~ pH + dose,
Gm ~ pH + protein,
k ~ pH + temperature,
nl = TRUE)
Would it then be correct to set priors as follows:
bprior <- prior(normal(11, 1), nlpar = tg, coef = Intercept)+
prior(normal(0, 10), nlpar = tg, coef = pH)+
prior(normal(0, 10), nlpar = tg, coef = dose)+
prior(normal(260, 1), nlpar = Gm, coef = Intercept)+
prior(normal(0, 10), nlpar = Gm, coef = pH)+
prior(normal(0, 10), nlpar = Gm, coef = protein)+
prior(normal(0.23, 0.05), nlpar = k, coef = Intercept)+
prior(normal(0, 1), nlpar = k, coef = pH)
prior(normal(0, 1), nlpar = k, coef = Temperature)
Or is there a better way?
/Jannik