dominant.ll.linear {genpwr}

Function to Calculate Dominant Log Likelihood for a Linear Regression Model

Description

Calculates the log likelihood for a given set of linear regression coefficients under a dominant genetic model.

Usage

dominant.ll.linear(beta, m, es, sd_y_x_model, sd_y_x_truth)

Arguments

beta

Vector of linear regression coefficients.

m

Minor allele frequency.

es

Vector of effect sizes with two elements, (mean AB - mean AA) and (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.

Value

The log likelihood.

Examples

dominant.ll.linear(beta = c(0.0000000, 0.1578947), m = 0.1, es = c(0,3), 
 sd_y_x_model = 0.9980797, sd_y_x_truth = 0.9544108)