finalimp: Wrapper for performing finalfit modelling using imputed data

In kamclean/finalpsm: Propensity-score matching with tidyverse and finalfit

Wrapper for performing finalfit modelling using imputed data

Description

Wrapper for performing finalfit modelling using imputed data

Usage

finalimp(data, dependent, explanatory, random_effect = NULL)

Arguments

data	Output from the impute() function.
dependent	Character vector of length 1: quoted name of dependent variable. Can be continuous, a binary factor, or a survival object of form Surv(time, status).
explanatory	Character vector of any length: quoted name(s) of explanatory variables.
random_effect	Character vector of length 1, either, (1) name of random intercept variable, e.g. "var1", (automatically convered to "(1 | var1)"); or, (2) the full lme4 specification, e.g. "(var1 | var2)". Note parenthesis MUST be included in (2) but NOT included in (1).

Value

Nested list of (1) the final model table, and (2) the underlying data, models, and metrics for the original and imputed datasets.

kamclean/finalpsm documentation built on Oct. 3, 2023, 3:52 a.m.