Description Usage Arguments Details Value

Compute outcome predictions using posterior samples. Exposure data for prediction can be either original data used for model fit or new data.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 | ```
## S3 method for class 'stanemax'
posterior_predict(
object,
newdata = NULL,
returnType = c("matrix", "dataframe", "tibble"),
newDataType = c("raw", "modelframe"),
...
)
posterior_predict_quantile(
object,
newdata = NULL,
ci = 0.9,
pi = 0.9,
newDataType = c("raw", "modelframe")
)
``` |

`object` |
A |

`newdata` |
An optional data frame that contains columns needed for model to run (exposure and covariates). If the model does not have any covariate, this can be a numeric vector corresponding to the exposure metric. |

`returnType` |
An optional string specifying the type of return object. |

`newDataType` |
An optional string specifying the type of newdata input, whether in the format of an original data frame or a processed model frame. Mostly used for internal purposes and users can usually leave at default. |

`...` |
Additional arguments passed to methods. |

`ci` |
Credible interval of the response without residual variability. |

`pi` |
Prediction interval of the response with residual variability. |

Run `vignette("emaxmodel", package = "rstanemax")`

to see
how you can use the posterior prediction for plotting estimated Emax curve.

An object that contain predicted response with posterior distribution of parameters. The default is a matrix containing predicted response. Each row of the matrix is a vector of predictions generated using a single draw of the model parameters from the posterior distribution.

If either `dataframe`

or `tibble`

is specified, the function returns a data frame or tibble object in a long format -
each row is a prediction generated using a single draw of the model parameters and a corresponding exposure.

Two types of predictions are generated with this function.
`respHat`

corresponds to the prediction without considering residual variability and is intended to provide credible interval of "mean" response.
`response`

include residual variability in its calculation, therefore the range represents prediction interval of observed response.

The return object also contains exposure and parameter values used for calculation.

With `posterior_predict_quantile()`

function, you can obtain quantiles
of `respHat`

and `response`

as specified by `ci`

and `pi`

.

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