# Maximize the approximated log-likelihood

### Description

Find the approximate maximum likelihood estimate, and estimate the variance of the estimate

### Usage

1 | ```
optimize_glmm(lfun, p_beta, p_theta, prev_fit = NULL, verbose = 1L)
``` |

### Arguments

`lfun` |
the approximated loglikelihood function |

`p_beta` |
the number of covariates |

`p_theta` |
the number of random effects |

`prev_fit` |
a |

`verbose` |
controls how much detail to print out while fitting the model. For verbose = 0, print nothing. For verbose = 1 (the default), print output approximately once a second during model fitting. For verbose = 2, print out the parameter value and log-likelihood at every stage of optimization. |

### Value

A list, containing the parameter estimate and variance matrix