spaMM (version 4.5.0)

inits: Controlling optimization strategy through initial values

Description

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 additionally 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(TRACE=TRUE)) shows successive 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 NAs and NaNs are not allowed. Instead, only numeric values as in init=list(lambda=0.1) are allowed.

Examples

if (FALSE) {
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 for 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.       
}