Calculate linear predictor from covariates

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"),
  ...
)

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)

Value

data.table

See also

outcome_count outcome_binary outcome_continuous outcome_phreg