outcome_lp: Calculate linear predictor from covariates

Description

Calculate linear predictor $$\text{par}^\top X$$ where $X$ is the design matrix specified by the formula

Usage

outcome_lp(
  data,
  mean = NULL,
  par = NULL,
  model = NULL,
  offset = NULL,
  treatment = NULL,
  intercept = TRUE,
  default.parameter = 0,
  family = gaussian(),
  remove = c("id", "num"),
  ...
)

Value

data.table

Arguments

data: (data.table) Covariate data, usually the output of the covariate model of a Trial object.
mean: formula specifying design from 'data' or a function that maps x to the mean value. If NULL all main-effects of the covariates will be used
par: (numeric) Regression coefficients (default zero). Can be given as a named list corresponding to the column names of model.matrix
model: Optional model object (glm, mets::phreg, ...)
offset: Optional offset variable name
treatment: Optional name of treatment variable
intercept: When FALSE the intercept will removed from the design matrix
default.parameter: when model and treatment is specified, interaction terms between treatment and all other covariates in model is added to the simulation model. default.parameter specifies the default parameter of these extra parameters which can be changed individually with the par argument.
family: family (default 'gaussian(identity)'). The inverse link-function is used to map the mean to the linear predictor scale (if mean is given as a function)
remove: variables that will be removed from input data (if formula is not specified)
...: Additional arguments passed to mean function (see examples)