q_beta_f: M-Step Expected Log-Likelihood with respect to Beta

In COMBO: Correcting Misclassified Binary Outcomes in Association Studies

M-Step Expected Log-Likelihood with respect to Beta

Description

Objective function of the form: Q_\beta = \sum_{i = 1}^N \Bigl[ \sum_{j = 0}^1 w_{ij} \text{log} \{ \pi_{ij} \}\Bigr]. Used to obtain estimates of \beta parameters.

Usage

q_beta_f(beta, X, w_mat, sample_size, n_cat)

Arguments

beta	A numeric vector of regression parameters for the Y (true outcome) ~ X (predictor matrix of interest).
X	A numeric design matrix.
w_mat	Matrix of E-step weights obtained from w_j.
sample_size	An integer value specifying the number of observations in the sample. This value should be equal to the number of rows of the design matrix, X.
n_cat	The number of categorical values that the true outcome, Y, can take.

Value

q_beta_f returns the negative value of the expected log-likelihood function, Q_\beta = \sum_{i = 1}^N \Bigl[ \sum_{j = 1}^2 w_{ij} \text{log} \{ \pi_{ij} \}\Bigr], at the provided inputs.

COMBO documentation built on Oct. 30, 2024, 5:07 p.m.