Append Model 2a factor priors to a Chapter 1 prior list

Model 2a reuses every Chapter 1 hyperprior unchanged and adds only two extra inputs needed by model_2a.jags:

• prec.lambda: the prior precision (1 / variance) of the factor loadings lambda[k, p]. Smaller values are more diffuse (allow larger cross-biomarker covariances).

• zero_p: a length-n_params vector of zeros (the mean of the within-biomarker random effects w).

Keeping this as its own one-job function makes it easy to test and to see exactly what Model 2a adds on top of Chapter 1.

Usage

add_factor_priors(priors, prec_lambda = 0.25)

Arguments

priors

A curve_params_priors list from prep_priors().

prec_lambda

A positive numeric scalar: prior precision of the loadings. Default 0.25 (loading SD = 2), weakly informative.

Value

The input list with prec.lambda and zero_p added.