Extract info from formulas, reTrms, etc., format for TMB

Usage

mkTMBStruc(
  formula,
  ziformula,
  dispformula,
  combForm,
  mf,
  fr,
  yobs,
  respCol,
  weights = NULL,
  contrasts,
  family,
  se = NULL,
  call = NULL,
  verbose = NULL,
  ziPredictCode = "corrected",
  doPredict = 0,
  whichPredict = integer(0),
  aggregate = factor(),
  REML = FALSE,
  start = NULL,
  map = NULL,
  sparseX = NULL,
  control = glmmTMBControl(),
  old_smooths = NULL,
  priors = NULL
)

Arguments

formula: combined fixed and random effects formula, following lme4 syntax.
ziformula: a one-sided (i.e., no response variable) formula for zero-inflation combining fixed and random effects: the default ~0 specifies no zero-inflation. Specifying ~. sets the zero-inflation formula identical to the right-hand side of formula (i.e., the conditional effects formula); terms can also be added or subtracted. When using ~. as the zero-inflation formula in models where the conditional effects formula contains an offset term, the offset term will automatically be dropped. The zero-inflation model uses a logit link.
dispformula: a one-sided formula for dispersion combining fixed and random effects: the default ~1 specifies the standard dispersion given any family. The argument is ignored for families that do not have a dispersion parameter. For an explanation of the dispersion parameter for each family, see sigma. The dispersion model uses a log link. In Gaussian mixed models, dispformula=~0 fixes the residual variance to be 0 (actually a small non-zero value), forcing variance into the random effects. The precise value can be controlled via control=glmmTMBControl(zero_dispval=...); the default value is sqrt(.Machine$double.eps).
combForm: combined formula
mf: call to model frame
fr: model frame
yobs: observed y
respCol: response column
weights: model weights (for binomial-type models, used as size/number of trials)
contrasts: an optional list, e.g., list(fac1="contr.sum"). See the contrasts.arg of model.matrix.default.
family: family object
se: (logical) compute standard error?
call: original glmmTMB call
verbose: whether progress indication should be printed to the console.
ziPredictCode: zero-inflation code
doPredict: flag to enable sds of predictions
whichPredict: which observations in model frame represent predictions
REML: whether to use REML estimation rather than maximum likelihood.
start: starting values, expressed as a list with possible components beta, betazi, betadisp (fixed-effect parameters for conditional, zero-inflation, dispersion models); b, bzi, bdisp (conditional modes for conditional, zero-inflation, and dispersion models); theta, thetazi, thetadisp (random-effect parameters, on the standard deviation/Cholesky scale, for conditional, z-i, and disp models); psi (extra family parameters, e.g., shape for Tweedie models).
map: a list specifying which parameter values should be fixed to a constant value rather than estimated. map should be a named list containing factors corresponding to a subset of the internal parameter names (see start parameter). Distinct factor values are fitted as separate parameter values, NA values are held fixed: e.g., map=list(beta=factor(c(1,2,3,NA))) would fit the first three fixed-effect parameters of the conditional model and fix the fourth parameter to its starting value. In general, users will probably want to use start to specify non-default starting values for fixed parameters. See MakeADFun for more details.
sparseX: see glmmTMB
control: control parameters, see glmmTMBControl.
old_smooths: (optional) smooth components from a previous fit: used when constructing a new model structure for prediction from an existing model. A list of smooths for each model component; each smooth has sm and re elements
priors: see priors