This building block declares a parameter model for a parameter that follows the normal distribution on the logit-scale.

prm_logit_normal(name, mean_logit = 0, var_logit = 1)

Arguments

name

Parameter name

mean_logit

Mean on the logit scale

var_logit

Variance on the logit scale

Value

A building block of type 'parameter'

Details

Parameter models specify type, name, and values for a parameter. The parameter model type is selected through the function name. The parameter name and values are provided as function arguments.

Parameter names

Every parameter must have a valid name. A parameter name can contain letters, numbers as well as the underscore character. The name needs to start with a letter.

Adding a parameter with an already existing name will replace the definition of the parameter. For example, the parameter “base” will have a log-normal distribution in the following snippet:

m <- model() +
 prm_normal("base") +
 prm_log_normal("base")

Parameter values

The parameter values that a parameter model expects vary by type. For example, prm_normal() requires the mean and the variance, whereas for prm_log_normal() median and variance on the log scale need to be provided. The argument name should indicate what parameter value is expected.

MU-referencing

assemblerr can include mu-referencing statements for parameter distributions that support it. The functionality can be activated by setting the option prm.use_mu_referencing to TRUE as shown in the following snippet:

m <- model() +
  prm_normal("base") +
  prm_log_normal("slp") +
  obs_additive(response~base+slp*time)

render(
  model = m,
  options = assemblerr_options(prm.use_mu_referencing = TRUE)
)

See also

Other parameter models: prm_log_normal(), prm_no_var(), prm_normal()

Examples

# EMAX dose-response model with emax (log-normal) and ed50 (no variability) parameters m2 <- model() + input_variable("dose") + prm_log_normal("emax", 10, 0.3) + prm_no_var("ed50", 5) + obs_proportional(effect~emax*dose/(ed50+dose)) # a log-normal parameter that is directly observed m <- model() + prm_log_normal("wt") + obs_additive(~wt)