card (version 0.1.0)

hrv_linear_model: HRV Linear Modeling

Description

hrv_linear_model Linear models for each HRV measure.

Usage

hrv_linear_model(data, covar, hrv, prop.weight = FALSE)

Arguments

data

Data frame that contains all covariates and outcomes. First column should be ID

covar

Vector names of the covariates, with first covariate being the primary exposure variable for linear regression

hrv

Vector names of the HRV measures, contained in data, that should be used. Can be generalized to any dependent variable set.

prop.weight

This is a logical value if propensity weighting should be done instead of traditional covariate adjustment. This calls for the propensity weighting function defined by card::recurrent_propensity that will generate both a PROP_SCORE column and PROP_WEIGHT column. Defaults to FALSE

Value

List of models with names

Details

Linear models built with dependent variable being the HRV measures (e.g. HF, LF, SDNN, etc). Allows for covariates to be included as available.