IRLS_EM {regmhmm} | R Documentation |
Iterative Reweighted Least Squares for the EM algorithm
Description
Iterative Reweighted Least Squares algorithm for optimizing the parameters in the M-step of the EM algorithm.
Usage
IRLS_EM(X, gamma, Y, beta, family, eps_IRLS, max_N)
Arguments
X |
A design matrix of size n x p. |
gamma |
A vector of size n specifying the posterior probability of the hidden states. |
Y |
A vector of observations of size n. |
beta |
A vector of size p + 1 specifying the GLM parameters. |
family |
The family of the response. |
eps_IRLS |
convergence tolerance in the iteratively reweighted least squares step. |
max_N |
the maximal number of IRLS iterations. |
Value
A vector representing the estimates of beta.
Examples
## Not run:
# Example usage of the function
IRLS_EM_one_step <- IRLS_EM_one(X,
gamma,
Y,
beta,
family)
## End(Not run)
[Package regmhmm version 1.0.0 Index]