calc.like.linear: Function to Calculate Log Likelihood for a Linear Regression...

In genpwr: Power Calculations Under Genetic Model Misspecification

Description

Convenience function to calculate the log likelihood of a specified model.

Usage

calc.like.linear(beta, m, es_ab, es_bb, sd_y_x_model, sd_y_x_truth, model)

Arguments

beta	Vector of linear regression coefficients.
m	Minor allele frequency.
es_ab	effect size for mean AB - mean AA
es_bb	effect size for mean BB - mean AA
sd_y_x_model	The standard deviation of Y (the outcome) given X (predictors/genotype) under the test model.
sd_y_x_truth	The standard deviation of Y given X (predictors/genotype) given genotype under the true model.
model	The genetic model in the linear regression: "Dominant", "Additive", "Recessive", "2df" or "null"

Value

The log likelihood.

Examples

calc.like.linear(beta = c(0.0000000, 0.1578947), m = 0.1, es_ab = 0, es_bb = 3, 
 sd_y_x_model = 0.9980797, sd_y_x_truth = 0.9544108, model = "Dominant")

genpwr documentation built on March 31, 2021, 1:06 a.m.