HRV Linear Modeling
hrv_linear_model Linear models for each HRV measure.
hrv_linear_model(data, covar, hrv, prop.weight = FALSE)
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 |
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 |
Linear models built with dependent variable being the HRV measures (e.g. HF, LF, SDNN, etc). Allows for covariates to be included as available.
List of models with names
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