discord (version 1.3)

discord_regression: Perform a Linear Regression within the Discordant Kinship Framework

Description

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

Value

Resulting `lm` object from performing the discordant regression.

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. Can be NULL if no predictors are desired.

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. Default is c("_s1","_s2").

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".

fast

Logical. If TRUE, uses a faster method for data processing.

Examples

discord_regression(
  data = data_sample,
  outcome = "height",
  predictors = "weight",
  pair_identifiers = c("_s1", "_s2"),
  sex = NULL,
  race = NULL
)