Quasi restricted maximum likelihood (qREML) algorithm for models with penalised splines or simple i.i.d. random effects

This algorithm can be used very flexibly to fit statistical models that involve penalised splines or simple i.i.d. random effects, i.e. that have penalties of the form $$0.5 \sum_{i} \lambda_i b_i^T S_i b_i,$$ with smoothing parameters $\lambda_i$, coefficient vectors $b_i$, and fixed penalty matrices $S_i$.

The qREML algorithm is typically much faster than REML or marginal ML using the full Laplace approximation method, but may be slightly less accurate regarding the estimation of the penalty strength parameters.

Under the hood, qreml uses the R package RTMB for automatic differentiation in the inner optimisation. The user has to specify the penalised negative log-likelihood function pnll structured as dictated by RTMB and use the penalty function to compute the quadratic-form penalty inside the likelihood.

Usage

qreml(
  pnll,
  par,
  dat,
  random,
  psname = "lambda",
  alpha = 0,
  smoothing = 1,
  maxiter = 100,
  tol = 1e-04,
  control = list(reltol = 1e-10, maxit = 1000),
  silent = 1,
  joint_unc = TRUE,
  saveall = FALSE,
  epsilon = c(0.01, 0.1)
)

Arguments

pnll

penalised negative log-likelihood function that is structured as dictated by RTMB and uses the penalty function from LaMa to compute the penalty

Needs to be a function of the named list of initial parameters par only.

par

named list of initial parameters

The random effects/ spline coefficients can be vectors or matrices, the latter summarising several random effects of the same structure, each one being a row in the matrix.

dat

initial data list that contains the data used in the likelihood function, hyperparameters, and the initial penalty strength vector

If the initial penalty strength vector is not called lambda, the name it has in dat needs to be specified using the penalty argument below. Its length needs to match the to the total number of random effects.

random

vector of names of the random effects/ penalised parameters in par

Caution: The ordering of random needs to match the order of the random effects passed to penalty inside the likelihood function.

psname

optional name given to the penalty strength parameter in dat. Defaults to "lambda".

alpha

optional hyperparamater for exponential smoothing of the penalty strengths

For larger values smoother convergence is to be expected but the algorithm may need more iterations.

smoothing

optional scaling factor for the final penalty strength parameters

Increasing this beyond one will lead to a smoother final model. Can be an integer or a vector of length equal to the length of the penalty strength parameter.

maxiter

maximum number of iterations in the outer optimisation over the penalty strength parameters.

tol

Convergence tolerance for the penalty strength parameters.

control

list of control parameters for optim to use in the inner optimisation. Here, optim uses the BFGS method which cannot be changed.

We advise against changing the default values of reltol and maxit as this can decrease the accuracy of the Laplace approximation.

silent

integer silencing level: 0 corresponds to full printing of inner and outer iterations, 1 to printing of outer iterations only, and 2 to no printing.

joint_unc

logical, if TRUE, joint RTMB object is returned allowing for joint uncertainty quantification

saveall

logical, if TRUE, then all model objects from each iteration are saved in the final model object.

epsilon

vector of two values specifying the cycling detection parameters. If the relative change of the new penalty strength to the previous one is larger than epsilon[1] but the change to the one before is smaller than epsilon[2], the algorithm will average the two last values to prevent cycling.

Value

returns a model list influenced by the users report statements in pnll

Examples

data = trex[1:1000,] # subset

# initial parameter list
par = list(logmu = log(c(0.3, 1)), # step mean
           logsigma = log(c(0.2, 0.7)), # step sd
           beta0 = c(-2,2), # state process intercept
           betaspline = matrix(rep(0, 18), nrow = 2)) # state process spline coefs
          
# data object with initial penalty strength lambda
dat = list(step = data$step, # step length
           tod = data$tod, # time of day covariate
           N = 2, # number of states
           lambda = rep(10,2)) # initial penalty strength

# building model matrices
modmat = make_matrices(~ s(tod, bs = "cp"), 
                       data = data.frame(tod = 1:24), 
                       knots = list(tod = c(0,24))) # wrapping points
dat$Z = modmat$Z # spline design matrix
dat$S = modmat$S # penalty matrix

# penalised negative log-likelihood function
pnll = function(par) {
  getAll(par, dat) # makes everything contained available without $
  Gamma = tpm_g(Z, cbind(beta0, betaspline), ad = TRUE) # transition probabilities
  delta = stationary_p(Gamma, t = 1, ad = TRUE) # initial distribution
  mu = exp(logmu) # step mean
  sigma = exp(logsigma) # step sd
  # calculating all state-dependent densities
  allprobs = matrix(1, nrow = length(step), ncol = N)
  ind = which(!is.na(step)) # only for non-NA obs.
  for(j in 1:N) allprobs[ind,j] = dgamma2(step[ind],mu[j],sigma[j])
  -forward_g(delta, Gamma[,,tod], allprobs, ad = TRUE) +
      penalty(betaspline, S, lambda) # this does all the penalization work
}

# model fitting
mod = qreml(pnll, par, dat, random = "betaspline")
#> Creating AD function
#> Initializing with lambda: 10 10 
#> outer 1 - lambda: 3.636 2.859 
#> outer 2 - lambda: 1.691 1.652 
#> outer 3 - lambda: 0.967 1.184 
#> outer 4 - lambda: 0.671 0.919 
#> outer 5 - lambda: 0.546 0.739 
#> outer 6 - lambda: 0.493 0.603 
#> outer 7 - lambda: 0.472 0.494 
#> outer 8 - lambda: 0.464 0.406 
#> outer 9 - lambda: 0.463 0.334 
#> outer 10 - lambda: 0.464 0.276 
#> outer 11 - lambda: 0.467 0.23 
#> outer 12 - lambda: 0.471 0.194 
#> outer 13 - lambda: 0.474 0.166 
#> outer 14 - lambda: 0.477 0.146 
#> outer 15 - lambda: 0.48 0.131 
#> outer 16 - lambda: 0.483 0.12 
#> outer 17 - lambda: 0.484 0.112 
#> outer 18 - lambda: 0.486 0.106 
#> outer 19 - lambda: 0.487 0.102 
#> outer 20 - lambda: 0.488 0.099 
#> outer 21 - lambda: 0.489 0.097 
#> outer 22 - lambda: 0.489 0.095 
#> outer 23 - lambda: 0.49 0.094 
#> outer 24 - lambda: 0.49 0.093 
#> outer 25 - lambda: 0.49 0.093 
#> outer 26 - lambda: 0.49 0.092 
#> outer 27 - lambda: 0.49 0.092 
#> outer 28 - lambda: 0.49 0.092 
#> Converged
#> Final model fit with lambda: 0.49 0.092

Quasi restricted maximum likelihood (qREML) algorithm for models with penalised splines or simple i.i.d. random effects

Usage

Arguments

Value

See also

Examples