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LongitudinalClaretBruno

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

This class extends the general LongitudinalModel class for using the Claret-Bruno model for the longitudinal outcome.

Usage 

LongitudinalClaretBruno(
  mu_b = prior_normal(log(60), 0.5),
  mu_g = prior_normal(log(1), 0.5),
  mu_c = prior_normal(log(0.4), 0.5),
  mu_p = prior_normal(log(2), 0.5),
  omega_b = prior_lognormal(log(0.2), 0.5),
  omega_g = prior_lognormal(log(0.2), 0.5),
  omega_c = prior_lognormal(log(0.2), 0.5),
  omega_p = prior_lognormal(log(0.2), 0.5),
  sigma = prior_lognormal(log(0.1), 0.5),
  scaled_variance = FALSE,
  centred = FALSE
)

Arguments

mu_b

(Prior) for the mean population baseline sld value.

mu_g

(Prior) for the mean population growth rate.

mu_c

(Prior) for the mean population resistance rate.

mu_p

(Prior) for the mean population growth inhibition

omega_b

(Prior) for the population standard deviation for the baseline sld value.

omega_g

(Prior) for the population standard deviation for the growth rate.

omega_c

(Prior) for the population standard deviation for the resistance rate.

omega_p

(Prior) for the population standard deviation for the growth inhibition.

sigma

(Prior) for the variance of the longitudinal values.

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 LongitudinalClaretBruno object.

Examples 

LongitudinalClaretBruno()
#> 
#> Claret-Bruno Longitudinal Model (additive error) with parameters:
#>     lm_clbr_mu_b ~ normal(mu = 4.09434, sigma = 0.5)
#>     lm_clbr_mu_g ~ normal(mu = 0, sigma = 0.5)
#>     lm_clbr_mu_c ~ normal(mu = -0.91629, sigma = 0.5)
#>     lm_clbr_mu_p ~ normal(mu = 0.69315, sigma = 0.5)
#>     lm_clbr_omega_b ~ lognormal(mu = -1.60944, sigma = 0.5)
#>     lm_clbr_omega_g ~ lognormal(mu = -1.60944, sigma = 0.5)
#>     lm_clbr_omega_c ~ lognormal(mu = -1.60944, sigma = 0.5)
#>     lm_clbr_omega_p ~ lognormal(mu = -1.60944, sigma = 0.5)
#>     lm_clbr_sigma ~ lognormal(mu = -2.30259, sigma = 0.5)
#>     lm_clbr_eta_b ~ std_normal()
#>     lm_clbr_eta_g ~ std_normal()
#>     lm_clbr_eta_c ~ std_normal()
#>     lm_clbr_eta_p ~ std_normal()
#>