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LongitudinalRandomSlope

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

This class extends the general LongitudinalModel class for using the random slope linear model for the longitudinal outcome.

Usage 

LongitudinalRandomSlope(
  intercept = prior_normal(30, 10),
  slope_mu = prior_normal(1, 3),
  slope_sigma = prior_lognormal(0, 1.5),
  sigma = prior_lognormal(0, 1.5),
  scaled_variance = FALSE
)

Arguments

intercept

(Prior) for the intercept.

slope_mu

(Prior) for the population slope slope_mu (one per arm).

slope_sigma

(Prior) for the random slope standard deviation slope_sigma (one per arm).

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.)

Value

A LongitudinalRandomSlope object.

Examples 

LongitudinalRandomSlope()
#> 
#> Random Slope Longitudinal Model (additive error) with parameters:
#>     lm_rs_intercept ~ normal(mu = 30, sigma = 10)
#>     lm_rs_slope_mu ~ normal(mu = 1, sigma = 3)
#>     lm_rs_slope_sigma ~ lognormal(mu = 0, sigma = 1.5)
#>     lm_rs_sigma ~ lognormal(mu = 0, sigma = 1.5)
#>     lm_rs_ind_rnd_slope ~ <None>
#>