ols_bca_topic {MLBC} | R Documentation |
Additive bias-corrected OLS for topic models (BCA-Topic)
Description
Bias-corrected additive estimator for topic model regression. This method applies additive bias correction to regressions that include topic proportions as covariates, accounting for estimation uncertainty in the topic model.
Usage
ols_bca_topic(
Y,
Q = NULL,
W,
S,
B,
k,
data = parent.frame(),
intercept = TRUE,
...
)
## Default S3 method:
ols_bca_topic(
Y,
Q = NULL,
W,
S,
B,
k,
data = parent.frame(),
intercept = TRUE,
...
)
## S3 method for class 'formula'
ols_bca_topic(
Y,
Q = NULL,
W,
S,
B,
k,
data = parent.frame(),
intercept = TRUE,
...
)
Arguments
Y |
numeric response vector, or a one-sided formula |
Q |
numeric matrix of additional controls (if Y is numeric) |
W |
numeric matrix of document-term frequencies |
S |
numeric matrix of topic loadings |
B |
numeric matrix of topic-word distributions |
k |
numeric; bias correction parameter |
data |
data frame (if Y is a formula) |
intercept |
logical; if TRUE, includes an intercept term |
... |
additional arguments |
Value
An object of class mlbc_fit
and mlbc_bca_topic
with:
-
coef
: bias-corrected coefficient estimates -
vcov
: adjusted variance-covariance matrix
Examples
# Load topic model dataset
data(topic_model_data)
# Extract components
Y <- topic_model_data$estimation_data$ly
Z <- as.matrix(topic_model_data$covars)
theta_full <- as.matrix(topic_model_data$theta_est_full)
beta_full <- as.matrix(topic_model_data$beta_est_full)
lda_data <- as.matrix(topic_model_data$lda_data)
# Apply additive bias correction
kappa <- mean(1.0 / lda_data[, 1]) * sqrt(nrow(lda_data))
S <- matrix(c(1.0, 0.0), nrow = 1)
fit <- ols_bca_topic(Y, Z, theta_full, S, beta_full, k = kappa)
summary(fit)
[Package MLBC version 0.2.2 Index]