Perform a Linear Regression within the Discordant Kinship Framework

Usage

discord_regression(
  data,
  outcome,
  predictors,
  demographics = NULL,
  id = NULL,
  sex = "sex",
  race = "race",
  pair_identifiers = c("_s1", "_s2"),
  data_processed = FALSE,
  coding_method = "none"
)

Arguments

data: The data set with kinship pairs
outcome: A character string containing the outcome variable of interest.
predictors: A character vector containing the column names for predicting the outcome.
demographics: Indicator variable for if the data has the sex and race demographics. If both are present (default, and recommended), value should be "both". Other options include "sex", "race", or "none".
id: Default's to NULL. If supplied, must specify the column name corresponding to unique kinship pair identifiers.
sex: A character string for the sex column name.
race: A character string for the race column name.
pair_identifiers: A character vector of length two that contains the variable identifier for each kinship pair
data_processed: Logical operator if data are already preprocessed by discord_data , default is FALSE
coding_method: A character string that indicates what kind of additional coding schemes should be used. Default is none. Other options include "binary" and "multi".

Value

Resulting `lm` object from performing the discordant regression.

Examples


discord_regression(
  data = data_sample,
  outcome = "height",
  predictors = "weight",
  pair_identifiers = c("_s1", "_s2"),
  sex = NULL,
  race = NULL
)
#> 
#> Call:
#> stats::lm(formula = stats::as.formula(paste(realOutcome, preds, 
#>     sep = " ~ ")), data = preppedData)
#> 
#> Coefficients:
#> (Intercept)  height_mean  weight_diff  weight_mean  
#>    0.624271     0.012577     0.323839    -0.005329  
#>