Usage
feglm_control(
dev_tol = 1e-06,
center_tol = 1e-06,
iter_max = 25L,
iter_center_max = 10000L,
iter_inner_max = 50L,
iter_interrupt = 1000L,
iter_ssr = 10L,
limit = 10L,
trace = FALSE,
drop_pc = TRUE,
keep_mx = FALSE
)
Arguments
- dev_tol
tolerance level for the first stopping condition of the maximization routine. The stopping condition is based on the relative change of the deviance in iteration \(r\) and can be expressed as follows: \(|dev_{r} - dev_{r - 1}| / (0.1 + |dev_{r}|) < tol\). The default is
1.0e-08
.- center_tol
tolerance level for the stopping condition of the centering algorithm. The stopping condition is based on the relative change of the centered variable similar to the
'lfe'
package. The default is1.0e-08
.- iter_max
unsigned integer indicating the maximum number of iterations in the maximization routine. The default is
25L
.- iter_center_max
unsigned integer indicating the maximum number of iterations in the centering algorithm. The default is
10000L
.- iter_inner_max
unsigned integer indicating the maximum number of iterations in the inner loop of the centering algorithm. The default is
50L
.- iter_interrupt
unsigned integer indicating the maximum number of iterations before the algorithm is interrupted. The default is
1000L
.- iter_ssr
unsigned integer indicating the number of iterations to skip before checking if the sum of squared residuals improves. The default is
10L
.- limit
unsigned integer indicating the maximum number of iterations of
theta.ml
. The default is10L
.- trace
logical indicating if output should be produced in each iteration. Default is
FALSE
.- drop_pc
logical indicating to drop observations that are perfectly classified/separated and hence do not contribute to the log-likelihood. This option is useful to reduce the computational costs of the maximization problem and improves the numerical stability of the algorithm. Note that dropping perfectly separated observations does not affect the estimates. The default is
TRUE
.- keep_mx
logical indicating if the centered regressor matrix should be stored. The centered regressor matrix is required for some covariance estimators, bias corrections, and average partial effects. This option saves some computation time at the cost of memory. The default is
TRUE
.
Examples
feglm_control(0.05, 0.05, 10L, 10L, TRUE, TRUE, TRUE)
#> $dev_tol
#> [1] 0.05
#>
#> $center_tol
#> [1] 0.05
#>
#> $iter_max
#> [1] 10
#>
#> $iter_center_max
#> [1] 10
#>
#> $iter_inner_max
#> [1] 1
#>
#> $iter_interrupt
#> [1] 1
#>
#> $iter_ssr
#> [1] 1
#>
#> $limit
#> [1] 10
#>
#> $trace
#> [1] FALSE
#>
#> $drop_pc
#> [1] TRUE
#>
#> $keep_mx
#> [1] FALSE
#>