Several parameters (notably the dispersion parameters: the variance of random effects and the residual variance parameter, if any) can be estimated either by iterative algorithms, or by generic optimization methods. The development of the `fitme`

function aims to provide full control of the selection of algorithms. For example, if two random effects are fitted, then `init=list(lambda=c(NA,NaN))`

enforces generic optimization for the first variance and iterative algorithms for the second. `init=list(lambda=c(0.1,NaN))`

has the same effect and additionnally provides control of the initial value for optimization (whereas `init.HLfit=list(lambda=c(NA,0.1))`

will provide control of the initial value for iterations).

How to know which algorithm has been selected for each parameter? `fitme(., verbose=c(verbose=TRUE))`

shows sucessive values of the variables estimated by optimization (See Examples; if no value appears, then all are estimated by iterative methods). The first lines of the summary of a fit object should tell which variances are estimated by the “outer” method.

`corrHLfit`

, which uses inner optimization by default, can be forced to perform outer optimization. Its control is more limited, as `NA`

s and `NaN`

s are not allowed. Instead, only numeric values as in `init=list(lambda=0.1)`

are allowed.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
## Not run:
air <- data.frame(passengers = as.numeric(AirPassengers),
year_z = scale(rep(1949:1960, each = 12)),
month = factor(rep(1:12, 12)))
air$time <- 1:nrow(air)
# Use verbose to find that lambda is estimated by optimization
fitme(passengers ~ month * year_z + AR1(1|time), data = air,
verbose=c(TRACE=TRUE))
# Use init to enforce iterative algorithm for lambda estimation:
fitme(passengers ~ month * year_z + AR1(1|time), data = air,
verbose=c(TRACE=TRUE), init=list(lambda=NaN))
# (but then it may be better to enforce it also fro phi: init=list(lambda=NaN, phi=NaN))
#
# Use init to enforce generic optimization for lambda estimation,
# and control initial value:
fitme(passengers ~ month * year_z + AR1(1|time), data = air,
verbose=c(TRACE=TRUE), init=list(lambda=0.1))
# See help("multinomial") for more examples of control by initial values.
## End(Not run)
``` |

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