Prepare priors — prep_priors • serodynamics

Takes multiple vector inputs to allow for modifiable priors. Priors can be specified as an option in run_mod.

Usage

prep_priors(
  max_antigens,
  mu_hyp_param = c(1, 7, 1, -4, -1),
  prec_hyp_param = c(1, 1e-05, 1, 0.001, 1),
  omega_param = c(1, 50, 1, 10, 1),
  wishdf_param = 20,
  prec_logy_hyp_param = c(4, 1)
)

Arguments

max_antigens

An integer specifying how many antigen-isotypes (biomarkers) will be modeled.

mu_hyp_param

A numeric vector of 5 values representing the prior mean for the population level parameters parameters (y0, y1, t1, r, alpha) for each biomarker. If specified, must be 5 values long, representing the following parameters:

y0 = baseline antibody concentration (default = 1.0)
y1 = peak antibody concentration (default = 7.0)
t1 = time to peak (default = 1.0)
r = shape parameter (default = -4.0)
alpha = decay rate (default = -1.0)

prec_hyp_param

A numeric vector of 5 values corresponding to hyperprior diagonal entries for the precision matrix (i.e. inverse variance) representing prior covariance of uncertainty around mu_hyp_param. If specified, must be 5 values long:

defaults: y0 = 1.0, y1 = 0.00001, t1 = 1.0, r = 0.001, alpha = 1.0

omega_param

A numeric vector of 5 values corresponding to the diagonal entries representing the Wishart hyperprior distributions of prec_hyp_param, describing how much we expect parameters to vary between individuals. If specified, must be 5 values long:

defaults: y0 = 1.0, y1 = 50.0, t1 = 1.0, r = 10.0, alpha = 1.0

wishdf_param

An integer vector of 1 value specifying the degrees of freedom for the Wishart hyperprior distribution of prec_hyp_param. If specified, must be 1 value long.

default = 20.0
The value of wishdf_param controls how informative the Wishart prior is. Higher values lead to tighter priors on individual variation. Lower values (e.g., 5–10) make the prior more weakly informative, which can help improve convergence if the model is over-regularized.

prec_logy_hyp_param

A numeric vector of 2 values corresponding to hyperprior diagonal entries on the log-scale for the precision matrix (i.e. inverse variance) representing prior beliefs of individual variation. If specified, must be 2 values long:

defaults = 4.0, 1.0

Value

A "curve_params_priors" object (a subclass of list with the inputs to prep_priors() attached as attributes entry named "used_priors"), containing the following elements:

"n_params": Corresponds to the 5 parameters being estimated.
"mu.hyp": A matrix of hyperpriors with dimensions max_antigens x 5 (# of parameters), representing the mean of the hyperprior distribution for the five seroresponse parameters: y0, y1, t1, r, and alpha).
"prec.hyp": A three-dimensional numeric array with dimensions max_antigens x 5 (# of parameters), containing the precision matrices of the hyperprior distributions of mu.hyp, for each biomarker.
"omega" : A three-dimensional numeric array with 5 matrix,each with dimensions max_antigens x 5 (# of parameters), representing the precision matrix of Wishart hyper-priors for prec.hyp.
"wishdf": A vector of 2 values specifying the degrees of freedom for the Wishart distribution used in the subject-level precision prior.
"prec.logy.hyp": A matrix of hyper-parameters for the precision (inverse variance) of individual variation measuring max_antigens x 2, on the log-scale.
used_priors = inputs to prep_priors() attached as attributes.

Examples


prep_priors(max_antigens = 2,
            mu_hyp_param = c(1.0, 7.0, 1.0, -4.0, -1.0),
            prec_hyp_param = c(1.0, 0.00001, 1.0, 0.001, 1.0),
            omega_param = c(1.0, 50.0, 1.0, 10.0, 1.0),
            wishdf_param = 20,
            prec_logy_hyp_param = c(4.0, 1.0))
#> $n_params
#> [1] 5
#> 
#> $mu.hyp
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    7    1   -4   -1
#> [2,]    1    7    1   -4   -1
#> 
#> $prec.hyp
#> , , 1
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    0    0    0    0
#> [2,]    1    0    0    0    0
#> 
#> , , 2
#> 
#>      [,1]  [,2] [,3] [,4] [,5]
#> [1,]    0 1e-05    0    0    0
#> [2,]    0 1e-05    0    0    0
#> 
#> , , 3
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0    0    1    0    0
#> [2,]    0    0    1    0    0
#> 
#> , , 4
#> 
#>      [,1] [,2] [,3]  [,4] [,5]
#> [1,]    0    0    0 0.001    0
#> [2,]    0    0    0 0.001    0
#> 
#> , , 5
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0    0    0    0    1
#> [2,]    0    0    0    0    1
#> 
#> 
#> $omega
#> , , 1
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    0    0    0    0
#> [2,]    1    0    0    0    0
#> 
#> , , 2
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0   50    0    0    0
#> [2,]    0   50    0    0    0
#> 
#> , , 3
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0    0    1    0    0
#> [2,]    0    0    1    0    0
#> 
#> , , 4
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0    0    0   10    0
#> [2,]    0    0    0   10    0
#> 
#> , , 5
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0    0    0    0    1
#> [2,]    0    0    0    0    1
#> 
#> 
#> $wishdf
#> [1] 20 20
#> 
#> $prec.logy.hyp
#>      [,1] [,2]
#> [1,]    4    1
#> [2,]    4    1
#> 
#> attr(,"class")
#> [1] "curve_params_priors" "list"               
#> attr(,"used_priors")
#> attr(,"used_priors")$mu_hyp_param
#> [1]  1  7  1 -4 -1
#> 
#> attr(,"used_priors")$prec_hyp_param
#> [1] 1e+00 1e-05 1e+00 1e-03 1e+00
#> 
#> attr(,"used_priors")$omega_param
#> [1]  1 50  1 10  1
#> 
#> attr(,"used_priors")$wishdf_param
#> [1] 20
#> 
#> attr(,"used_priors")$prec_logy_hyp_param
#> [1] 4 1
#> 

prep_priors(max_antigens = 2)
#> $n_params
#> [1] 5
#> 
#> $mu.hyp
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    7    1   -4   -1
#> [2,]    1    7    1   -4   -1
#> 
#> $prec.hyp
#> , , 1
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    0    0    0    0
#> [2,]    1    0    0    0    0
#> 
#> , , 2
#> 
#>      [,1]  [,2] [,3] [,4] [,5]
#> [1,]    0 1e-05    0    0    0
#> [2,]    0 1e-05    0    0    0
#> 
#> , , 3
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0    0    1    0    0
#> [2,]    0    0    1    0    0
#> 
#> , , 4
#> 
#>      [,1] [,2] [,3]  [,4] [,5]
#> [1,]    0    0    0 0.001    0
#> [2,]    0    0    0 0.001    0
#> 
#> , , 5
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0    0    0    0    1
#> [2,]    0    0    0    0    1
#> 
#> 
#> $omega
#> , , 1
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    1    0    0    0    0
#> [2,]    1    0    0    0    0
#> 
#> , , 2
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0   50    0    0    0
#> [2,]    0   50    0    0    0
#> 
#> , , 3
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0    0    1    0    0
#> [2,]    0    0    1    0    0
#> 
#> , , 4
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0    0    0   10    0
#> [2,]    0    0    0   10    0
#> 
#> , , 5
#> 
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    0    0    0    0    1
#> [2,]    0    0    0    0    1
#> 
#> 
#> $wishdf
#> [1] 20 20
#> 
#> $prec.logy.hyp
#>      [,1] [,2]
#> [1,]    4    1
#> [2,]    4    1
#> 
#> attr(,"class")
#> [1] "curve_params_priors" "list"               
#> attr(,"used_priors")
#> attr(,"used_priors")$mu_hyp_param
#> [1]  1  7  1 -4 -1
#> 
#> attr(,"used_priors")$prec_hyp_param
#> [1] 1e+00 1e-05 1e+00 1e-03 1e+00
#> 
#> attr(,"used_priors")$omega_param
#> [1]  1 50  1 10  1
#> 
#> attr(,"used_priors")$wishdf_param
#> [1] 20
#> 
#> attr(,"used_priors")$prec_logy_hyp_param
#> [1] 4 1
#>