ERLS {DLMRMV} | R Documentation |
Exponentially Weighted Recursive Least Squares with Missing Value Imputation
Description
Exponentially Weighted Recursive Least Squares with Missing Value Imputation
Usage
ERLS(data, rho = 0.01, lambda = 0.95, nb = 100, niter = 1)
Arguments
data |
Linear regression dataset (1st column as Y, others as X) |
rho |
Regularization parameter |
lambda |
Forgetting factor |
nb |
Maximum iterations |
niter |
Initial iteration count (typically 1) |
Value
List containing:
Yhat |
Imputed response vector |
betahat |
Estimated coefficients |
Examples
set.seed(123)
data <- data.frame(
y = c(rnorm(50), rep(NA, 10)),
x1 = rnorm(60),
x2 = rnorm(60)
)
result <- ERLS(data, rho = 0.01, lambda = 0.95, nb = 100, niter = 1)
head(result$Yhat)
[Package DLMRMV version 0.2.0 Index]