| true_classification_prob {COMBO} | R Documentation |
Compute Probability of Each True Outcome, for Every Subject
Description
Compute the probability of the latent true outcome Y \in \{1, 2 \} as
P(Y_i = j | X_i) = \frac{\exp(X_i \beta)}{1 + \exp(X_i \beta)}
for each of the i = 1, \dots, n subjects.
Usage
true_classification_prob(beta_matrix, x_matrix)
Arguments
beta_matrix |
A numeric column matrix of estimated regression parameters for the
true outcome mechanism, |
x_matrix |
A numeric matrix of covariates in the true outcome mechanism.
|
Value
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, Y, represents a true, latent outcome category Y \in \{1, 2 \}.
The last column, Probability, is the value of the equation
P(Y_i = j | X_i) = \frac{\exp(X_i \beta)}{1 + \exp(X_i \beta)} computed
for each subject and true, latent outcome 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_Y <- true_classification_prob(estimated_betas, x_matrix)
head(P_Y)