Extract info from formulas, reTrms, etc., format for TMB
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),
REML = FALSE,
start = NULL,
map = NULL,
sparseX = NULL,
control = glmmTMBControl(),
old_smooths = NULL,
priors = NULL
)combined fixed and random effects formula, following lme4 syntax.
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.
a one-sided formula for dispersion containing only fixed 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).
combined formula
call to model frame
model frame
observed y
response column
model weights (for binomial-type models, used as size/number of trials)
an optional list, e.g., list(fac1="contr.sum"). See the contrasts.arg of model.matrix.default.
family object
(logical) compute standard error?
original glmmTMB call
whether progress indication should be printed to the console.
zero-inflation code
flag to enable sds of predictions
which observations in model frame represent predictions
whether to use REML estimation rather than maximum likelihood.
starting values, expressed as a list with possible components beta, betazi, betad (fixed-effect parameters for conditional, zero-inflation, dispersion models); b, bzi (conditional modes for conditional and zero-inflation models); theta, thetazi (random-effect parameters, on the standard deviation/Cholesky scale, for conditional and z-i models); psi (extra family parameters, e.g., shape for Tweedie models).
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.
see glmmTMB
control parameters, see glmmTMBControl.
(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 (only cond and zi at present); each smooth has sm and re elements
see priors