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Stein-Fojo longitudinal model with subject-level covariates

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

Models the log-normal distribution parameters for baseline, shrinkage, and growth using separate subject-level covariate predictors.

The intercept column generated by each formula is removed. The separately specified intercept parameter is the reference-level value except for the exponential parametrization, whose reference-level value is one. linear uses intercept + X beta, proportional uses intercept * (1 + X beta), exponential uses intercept ^ (X beta), and log-linear uses exp(intercept + X beta).

Usage 

LongitudinalSteinFojoCov(
  mu_b_formula = ~study,
  omega_b_formula = ~study,
  mu_s_formula = ~arm,
  omega_s_formula = ~arm,
  mu_g_formula = ~arm,
  omega_g_formula = ~arm,
  mu_b_parametrization = "linear",
  omega_b_parametrization = "log-linear",
  mu_s_parametrization = "linear",
  omega_s_parametrization = "log-linear",
  mu_g_parametrization = "linear",
  omega_g_parametrization = "log-linear",
  mu_b_intercept_prior = prior_normal(log(60), 1),
  mu_b_coefficients_prior = prior_normal(0, 1),
  omega_b_intercept_prior = prior_normal(log(0.2), 1),
  omega_b_coefficients_prior = prior_normal(0, 1),
  mu_s_intercept_prior = prior_normal(log(0.5), 1),
  mu_s_coefficients_prior = prior_normal(0, 1),
  omega_s_intercept_prior = prior_normal(log(0.2), 1),
  omega_s_coefficients_prior = prior_normal(0, 1),
  mu_g_intercept_prior = prior_normal(log(0.3), 1),
  mu_g_coefficients_prior = prior_normal(0, 1),
  omega_g_intercept_prior = prior_normal(log(0.2), 1),
  omega_g_coefficients_prior = prior_normal(0, 1),
  sigma = prior_lognormal(log(0.1), 1),
  scaled_variance = FALSE,
  centred_baseline = TRUE,
  centred_shrinkage = FALSE,
  centred_growth = FALSE
)

Arguments

mu_b_formula

(formula) One-sided formula for subject-level covariate effects on the baseline mean.

omega_b_formula

(formula) One-sided formula for subject-level covariate effects on the baseline standard deviation.

mu_s_formula

(formula) One-sided formula for subject-level covariate effects on the shrinkage mean.

omega_s_formula

(formula) One-sided formula for subject-level covariate effects on the shrinkage standard deviation.

mu_g_formula

(formula) One-sided formula for subject-level covariate effects on the growth mean.

omega_g_formula

(formula) One-sided formula for subject-level covariate effects on the growth standard deviation.

mu_b_parametrization

(character) Parametrization of the baseline mean predictor.

omega_b_parametrization

(character) Parametrization of the baseline standard deviation predictor. Only "exponential" and "log-linear" are supported.

mu_s_parametrization

(character) Parametrization of the shrinkage mean predictor.

omega_s_parametrization

(character) Parametrization of the shrinkage standard deviation predictor. Only "exponential" and "log-linear" are supported.

mu_g_parametrization

(character) Parametrization of the growth mean predictor.

omega_g_parametrization

(character) Parametrization of the growth standard deviation predictor. Only "exponential" and "log-linear" are supported.

mu_b_intercept_prior

(Prior) Prior for the intercept of the baseline mean predictor.

mu_b_coefficients_prior

(Prior) Prior for the covariate coefficients of the baseline mean predictor.

omega_b_intercept_prior

(Prior) Prior for the intercept of the baseline standard deviation predictor.

omega_b_coefficients_prior

(Prior) Prior for the covariate coefficients of the baseline standard deviation predictor.

mu_s_intercept_prior

(Prior) Prior for the intercept of the shrinkage mean predictor.

mu_s_coefficients_prior

(Prior) Prior for the covariate coefficients of the shrinkage mean predictor.

omega_s_intercept_prior

(Prior) Prior for the intercept of the shrinkage standard deviation predictor.

omega_s_coefficients_prior

(Prior) Prior for the covariate coefficients of the shrinkage standard deviation predictor.

mu_g_intercept_prior

(Prior) Prior for the intercept of the growth mean predictor.

mu_g_coefficients_prior

(Prior) Prior for the covariate coefficients of the growth mean predictor.

omega_g_intercept_prior

(Prior) Prior for the intercept of the growth standard deviation predictor.

omega_g_coefficients_prior

(Prior) Prior for the covariate coefficients of the growth standard deviation predictor.

sigma

(Prior) Prior for the observation-error standard deviation.

scaled_variance

(logical) Whether to use multiplicative observation error.

centred_baseline

(logical) Whether to use a centred parameterization for the subject-specific baseline effects.

centred_shrinkage

(logical) Whether to use a centred parameterization for the subject-specific shrinkage effects.

centred_growth

(logical) Whether to use a centred parameterization for the subject-specific growth effects.

Value

A LongitudinalSteinFojoCov object.

Examples 

LongitudinalSteinFojoCov()
#> 
#> Stein-Fojo with Covariates Longitudinal Model (additive error) with parameters:
#>     lm_sfc_mu_b_intercept ~ normal(mu = 4.09434, sigma = 1)
#>     lm_sfc_mu_b_coefficients ~ normal(mu = 0, sigma = 1)
#>     lm_sfc_omega_b_intercept ~ normal(mu = -1.60944, sigma = 1)
#>     lm_sfc_omega_b_coefficients ~ normal(mu = 0, sigma = 1)
#>     lm_sfc_mu_s_intercept ~ normal(mu = -0.69315, sigma = 1)
#>     lm_sfc_mu_s_coefficients ~ normal(mu = 0, sigma = 1)
#>     lm_sfc_omega_s_intercept ~ normal(mu = -1.60944, sigma = 1)
#>     lm_sfc_omega_s_coefficients ~ normal(mu = 0, sigma = 1)
#>     lm_sfc_mu_g_intercept ~ normal(mu = -1.20397, sigma = 1)
#>     lm_sfc_mu_g_coefficients ~ normal(mu = 0, sigma = 1)
#>     lm_sfc_omega_g_intercept ~ normal(mu = -1.60944, sigma = 1)
#>     lm_sfc_omega_g_coefficients ~ normal(mu = 0, sigma = 1)
#>     lm_sfc_sigma ~ lognormal(mu = -2.30259, sigma = 1)
#>     lm_sfc_psi_b ~ <None>
#>     lm_sfc_eta_tilde_s ~ std_normal()
#>     lm_sfc_eta_tilde_g ~ std_normal()
#>