- formula
A two-sided linear formula object describing both the fixed-effects and random-effects part of the model, with the response on the left of a ~ operator and the terms, separated by + operators, on the right. Random-effects terms are distinguished by vertical bars ("|") separating expressions for design matrices from grouping factors.
- data
data frame (tibbles are OK) containing model variables. Not required, but strongly recommended; if data is not specified, downstream methods such as prediction with new data (predict(fitted_model, newdata = ...)) will fail. If it is necessary to call glmmTMB with model variables taken from the environment rather than from a data frame, specifying data=NULL will suppress the warning message.
- family
a family function, a character string naming a family function, or the result of a call to a family function (variance/link function) information. See family for a generic discussion of families or family_glmmTMB for details of glmmTMB-specific families.
- smoothZ
A list includes a set of smooth Z matrixs (called 'smooth term') used in the mixed effects model, the name of 'smooth term' should be different any variables in the model, each 'smooth term' is the result of function smZ. e.g.
smoothZ=list(sm1=smZ(x1), sm2=smZ(x2, by=f1), sm3=smZ(x3, by=f2, group=TRUE), ...) where 'sm1' to 'sm3' should be new variable names in the data, and x1 to x3 are covariates, and f1, f2 are factors.
- 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 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).
- weights
weights, as in glm. Not automatically scaled to have sum 1.
- offset
offset for conditional model (only).
- contrasts
an optional list, e.g., list(fac1="contr.sum"). See the contrasts.arg of model.matrix.default.
- na.action
a function that specifies how to handle observations
containing NAs. The default action (na.omit,
inherited from the 'factory fresh' value of
getOption("na.action")) strips any observations with any
missing values in any variables. Using na.action = na.exclude
will similarly drop observations with missing values while fitting the model,
but will fill in NA values for the predicted and residual
values for cases that were excluded during the fitting process
because of missingness.
- se
whether to return standard errors.
- verbose
whether progress indication should be printed to the console.
- doFit
whether to fit the full model, or (if FALSE) return the preprocessed data and parameter objects, without fitting the model.
- control
control parameters, see glmmTMBControl.
- REML
whether to use REML estimation rather than maximum likelihood.
- start
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).
- 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
a named logical vector containing (possibly) elements named "cond", "zi", "disp" to indicate whether fixed-effect model matrices for particular model components should be generated as sparse matrices, e.g. c(cond=TRUE). Default is all FALSE
- prt
Logical scalar - Should the info to be print on screen in the middle of the process or not?
- predict_info
Logical scalar - Should provide the info for function semipred or not? In case of there is a correlation theta parameter appearing, you may set predict=FALSE.