true_classification_prob function

Compute Probability of Each True Mediator, for Every Subject

Compute the probability of the latent true mediator $M \in \{1, 2 \}$ as $P(M_i = j | X_i) = \frac{\exp(X_i \beta)}{1 + \exp(X_i \beta)}$

for each of the $i = 1, \dots,$ n subjects.

true_classification_prob(beta_matrix, x_matrix)

Arguments

• beta_matrix: A numeric column matrix of estimated regression parameters for the true mediator mechanism, M (true mediator) ~ X (predictor matrix of interest), obtained from COMMA_EM, COMMA_PVW, or COMMA_OLS.
• x_matrix: A numeric matrix of covariates in the true mediator mechanism. x_matrix should not contain an intercept.

Returns

true_classification_prob returns a dataframe containing three columns. The first column, Subject, represents the subject ID, from $1$ to n, where n is the sample size, or equivalently, the number of rows in x_matrix. The second column, M, represents a true, latent mediator category $M \in \{1, 2 \}$. The last column, Probability, is the value of the equation $P(M_i = j | X_i) = \frac{\exp(X_i \beta)}{1 + \exp(X_i \beta)}$ computed for each subject and true, latent mediator category.

Examples

set.seed(123)
sample_size <- 1000
cov1 <- rnorm(sample_size)
cov2 <- rnorm(sample_size, 1, 2)
x_matrix <- matrix(c(cov1, cov2), nrow = sample_size, byrow = FALSE)
estimated_betas <- matrix(c(1, -1, .5), ncol = 1)
P_M <- true_classification_prob(estimated_betas, x_matrix)
head(P_M)