R: prediction

predict.glmmTMB {glmmTMB} | R Documentation

## prediction

### Description

prediction

### Usage

    ## S3 method for class 'glmmTMB'
    predict(object, newdata = NULL, se.fit = FALSE, re.form,
      allow.new.levels = FALSE, type = c("link", "response", "conditional",
      "zprob", "zlink"), zitype = NULL, na.action = na.pass, debug = FALSE,
      ...)

### Arguments

`object` | a `glmmTMB` object
`newdata` | new data for prediction
`se.fit` | return the standard errors of the predicted values?
`re.form` | (not yet implemented) specify which random effects to condition on when predicting
`allow.new.levels` | allow previously unobserved levels in random-effects variables? see details.
`type` | Denoting mu as the mean of the conditional distribution and `p` as the zero-inflation probability, the possible choices are: "link" conditional mean on the scale of the link function, or equivalently the linear predictor of the conditional model "response" expected value; this is mu*(1-p) for zero-inflated models and `mu` otherwise "conditional" mean of the conditional response; `mu` for all models (i.e., synonymous with `"response"` in the absence of zero-inflation "zprob" the probability of a structural zero (gives an error for non-zero-inflated models) ##' "zilink" predicted zero-inflation probability on the scale of the logit link function
`zitype` | deprecated: formerly used to specify type of zero-inflation probability. Now synonymous with `type`
`na.action` | how to handle missing values in `newdata` (see `na.action`); the default (`na.pass`) is to predict `NA`
`debug` | (logical) return the `TMBStruc` object that will be used internally for debugging?
`...` | unused - for method compatibility

### Details

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).

### Examples

    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)

[Package glmmTMB version 0.2.2.0 Index]