# posterior_linpred.stanreg

##### Posterior distribution of the linear predictor

Extract the posterior draws of the linear predictor, possibly transformed by
the inverse-link function. This function is occasionally useful, but it
should be used sparingly. Inference and model checking should generally be
carried out using the posterior predictive distribution (i.e., using
`posterior_predict`

).

##### Usage

```
# S3 method for stanreg
posterior_linpred(object, transform = FALSE,
newdata = NULL, draws = NULL, re.form = NULL, offset = NULL,
XZ = FALSE, ...)
```

##### Arguments

- object
A fitted model object returned by one of the rstanarm modeling functions. See

`stanreg-objects`

.- transform
Should the linear predictor be transformed using the inverse-link function? The default is

`FALSE`

, in which case the untransformed linear predictor is returned.- newdata, draws, re.form, offset
Same as for

`posterior_predict`

.- XZ
If

`TRUE`

then instead of computing the linear predictor the design matrix`X`

(or`cbind(X,Z)`

for models with group-specific terms) constructed from`newdata`

is returned. The default is`FALSE`

.- ...
Currently ignored.

##### Value

The default is to return a `draws`

by `nrow(newdata)`

matrix of simulations from the posterior distribution of the (possibly
transformed) linear predictor. The exception is if the argument `XZ`

is set to `TRUE`

(see the `XZ`

argument description above).

##### Note

For models estimated with `stan_clogit`

, the number of
successes per stratum is ostensibly fixed by the research design. Thus,
when calling `posterior_linpred`

with new data and ```
transform =
TRUE
```

, the `data.frame`

passed to the `newdata`

argument must
contain an outcome variable and a stratifying factor, both with the same
name as in the original `data.frame`

. Then, the probabilities will
condition on this outcome in the new data.

##### See Also

`posterior_predict`

to draw from the posterior
predictive distribution of the outcome, which is typically preferable.

##### Examples

```
# NOT RUN {
if (!exists("example_model")) example(example_model)
print(family(example_model))
# linear predictor on log-odds scale
linpred <- posterior_linpred(example_model)
colMeans(linpred)
# probabilities
probs <- posterior_linpred(example_model, transform = TRUE)
colMeans(probs)
# not conditioning on any group-level parameters
probs2 <- posterior_linpred(example_model, transform = TRUE, re.form = NA)
apply(probs2, 2, median)
# }
```

*Documentation reproduced from package rstanarm, version 2.17.4, License: GPL (>= 3)*