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LongitudinalGSF

Source: R/LongitudinalGSF.R
LongitudinalGSF-class.Rd

This class extends the general LongitudinalModel class for using the Generalized Stein-Fojo (GSF) model for the longitudinal outcome.

Usage 

LongitudinalGSF(
  mu_bsld = prior_normal(log(60), 1),
  mu_ks = prior_normal(log(0.5), 1),
  mu_kg = prior_normal(log(0.3), 1),
  mu_phi = prior_normal(qlogis(0.5), 1),
  omega_bsld = prior_lognormal(log(0.2), 1),
  omega_ks = prior_lognormal(log(0.2), 1),
  omega_kg = prior_lognormal(log(0.2), 1),
  omega_phi = prior_lognormal(log(0.2), 1),
  sigma = prior_lognormal(log(0.1), 1),
  scaled_variance = FALSE,
  centred = FALSE
)

Arguments

mu_bsld

(Prior) for the mean baseline value mu_bsld.

mu_ks

(Prior) for the mean shrinkage rate mu_ks.

mu_kg

(Prior) for the mean growth rate mu_kg.

mu_phi

(Prior) for the mean proportion of cells affected by the treatment mu_phi.

omega_bsld

(Prior) for the baseline value standard deviation omega_bsld.

omega_ks

(Prior) for the shrinkage rate standard deviation omega_ks.

omega_kg

(Prior) for the growth rate standard deviation omega_kg.

omega_phi

(Prior) for the standard deviation of the proportion of cells affected by the treatment omega_phi.

sigma

(Prior) for the variance of the longitudinal values sigma.

scaled_variance

(logical) whether the variance should be scaled by the expected value, corresponding to a multiplicative model. As a default, the variance is not scaled by the expected value, corresponding to an additive model. (See the "Statistical Specifications" vignette for more details.)

centred

(logical) whether to use the centred parameterization.

Value

A LongitudinalGSF object.

Examples 

LongitudinalGSF()
#> 
#> Generalized Stein-Fojo Longitudinal Model (additive error) with parameters:
#>     lm_gsf_mu_bsld ~ normal(mu = 4.09434, sigma = 1)
#>     lm_gsf_mu_ks ~ normal(mu = -0.69315, sigma = 1)
#>     lm_gsf_mu_kg ~ normal(mu = -1.20397, sigma = 1)
#>     lm_gsf_mu_phi ~ normal(mu = 0, sigma = 1)
#>     lm_gsf_omega_bsld ~ lognormal(mu = -1.60944, sigma = 1)
#>     lm_gsf_omega_ks ~ lognormal(mu = -1.60944, sigma = 1)
#>     lm_gsf_omega_kg ~ lognormal(mu = -1.60944, sigma = 1)
#>     lm_gsf_omega_phi ~ lognormal(mu = -1.60944, sigma = 1)
#>     lm_gsf_sigma ~ lognormal(mu = -2.30259, sigma = 1)
#>     lm_gsf_eta_tilde_bsld ~ std_normal()
#>     lm_gsf_eta_tilde_ks ~ std_normal()
#>     lm_gsf_eta_tilde_kg ~ std_normal()
#>     lm_gsf_eta_tilde_phi ~ std_normal()
#>