prediction
# S3 method for glmmTMB
predict(
object,
newdata = NULL,
newparams = NULL,
se.fit = FALSE,
cov.fit = FALSE,
re.form = NULL,
allow.new.levels = FALSE,
type = c("link", "response", "conditional", "zprob", "zlink", "disp", "latent"),
zitype = NULL,
na.action = na.pass,
fast = NULL,
debug = FALSE,
aggregate = NULL,
do.bias.correct = FALSE,
bias.correct.control = list(sd = TRUE),
...
)a glmmTMB object
new data for prediction
new parameters for prediction
return the standard errors of the predicted values?
return the covariance matrix of the predicted values?
NULL to specify individual-level predictions; ~0 or NA to specify population-level predictions (i.e., setting all random effects to zero)
allow previously unobserved levels in random-effects variables? see details.
Denoting \(mu\) as the mean of the conditional distribution and
p as the zero-inflation probability,
the possible choices are:
the linear predictor of the conditional model, or
equivalently the conditional mean on the scale of the link function
(this equivalence does not hold for truncated distributions, where
the link-scaled value is not adjusted for the effect of truncation on the mean; to get the corrected value of the conditional mean on the linear predictor
scale, use family(m)$linkfun(predict(m, type = "conditional")))
expected value; this is \(mu*(1-p)\) for zero-inflated models
and mu otherwise
mean of the conditional response; mu for all models
(i.e., synonymous with "response" in the absence of zero-inflation
the probability of a structural zero (returns 0 for non-zero-inflated models)
predicted zero-inflation probability on the scale of
the logit link function (returns -Inf for non-zero-inflated models)
dispersion parameter, however it is defined for that particular family (as described in sigma.glmmTMB)
return latent variables
deprecated: formerly used to specify type of zero-inflation probability. Now synonymous with type
how to handle missing values in newdata (see na.action);
the default (na.pass) is to predict NA
predict without expanding memory (default is TRUE if newdata and newparams are NULL and population-level prediction is not being done)
(logical) return the TMBStruc object that will be
used internally for debugging?
(optional factor vector) sum the elements with matching factor levels
(logical) should aggregated predictions use Taylor expanded estimate of nonlinear contribution of random effects (see details)
a list sent to TMB's function sdreport(). See documentation there.
unused - for method compatibility
To compute population-level predictions for a given grouping variable (i.e., setting all random effects for that grouping variable to zero), set the grouping variable values to NA. Finer-scale control of conditioning (e.g. allowing variation among groups in intercepts but not slopes when predicting from a random-slopes model) is not currently possible.
Prediction of new random effect levels is possible as long as the model specification (fixed effects and parameters) is kept constant.
However, to ensure intentional usage, a warning is triggered if allow.new.levels=FALSE (the default).
Prediction using "data-dependent bases" (variables whose scaling or transformation depends on the original data, e.g. poly, ns, or poly) should work properly; however, users are advised to check results extra-carefully when using such variables. Models with different versions of the same data-dependent basis type in different components (e.g. formula= y ~ poly(x,3), dispformula= ~poly(x,2)) will probably not produce correct predictions.
Bias corrected predictions are based on the method described in Thorson J.T. & Kristensen (2016). These should be checked carefully by the user and are not extensively tested.
Thorson J.T. & Kristensen K. (2016) Implementing a generic method for bias correction in statistical models using random effects, with spatial and population dynamics examples. Fish. Res. 175, 66-74.
data(sleepstudy,package="lme4")
g0 <- glmmTMB(Reaction~Days+(Days|Subject),sleepstudy)
predict(g0, sleepstudy)
## Predict new Subject
nd <- sleepstudy[1,]
nd$Subject <- "new"
predict(g0, newdata=nd, allow.new.levels=TRUE)
## population-level prediction
nd_pop <- data.frame(Days=unique(sleepstudy$Days),
Subject=NA)
predict(g0, newdata=nd_pop)
## return latent variables (BLUPs/conditional modes/etc. ) with standard errors
## (actually conditional standard deviations)
predict(g0, type = "latent", se.fit = TRUE)
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