Extract info from formulas, reTrms, etc., format for TMB
mkTMBStruc(
formula,
ziformula,
dispformula,
combForm,
mf,
fr,
yobs,
respCol,
weights,
contrasts,
size = NULL,
family,
se = NULL,
call = NULL,
verbose = NULL,
ziPredictCode = "corrected",
doPredict = 0,
whichPredict = integer(0),
REML = FALSE,
start = NULL,
map = NULL,
sparseX = 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: at present it is set to sqrt(.Machine$double.eps)), forcing variance into the random effects.
combined formula
call to model frame
model frame
observed y
response column
weights, as in glm. Not automatically scaled to have sum 1.
an optional list, e.g., list(fac1="contr.sum"). See the contrasts.arg of model.matrix.default.
number of trials in binomial and betabinomial families
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); thetaf (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
offset for zero-inflated model
offset for dispersion model