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fitted.brmsfit.Rmd
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fitted.brmsfit.Rmd
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---
title: "``brms::fitted.brmsfit``"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(brms)
```
fitted.brmsfit Extract Model Fitted values of brmsfit Objects
#### Description
Predict mean values of the response distribution (i.e., the ’regression line’) for a fitted model. Can be performed for the data used to fit the model (posterior predictive checks) or for new data. By definition, these predictions have smaller variance than the response predictions performed by the predict method. This is because the measurement error is not incorporated. The estimated means of both methods should, however, be very similar.
#### Usage
<pre><code>
## S3 method for class 'brmsfit'
fitted(object, newdata = NULL, re_formula = NULL,
scale = c("response", "linear"), resp = NULL, dpar = NULL,
nlpar = NULL, nsamples = NULL, subset = NULL, sort = FALSE,
summary = TRUE, robust = FALSE, probs = c(0.025, 0.975), ...)
## S3 method for class 'brmsfit'
posterior_linpred(object, transform = FALSE,
newdata = NULL, re_formula = NULL, re.form = NULL, resp = NULL,
dpar = NULL, nlpar = NULL, nsamples = NULL, subset = NULL,
sort = FALSE, ...)
</code></pre>
#### Arguments
* ``object`` An object of class brmsfit.
* ``newdata`` An optional data.frame for which to evaluate predictions. If NULL (default), the original data of the model is used.
* ``re_formula`` formula containing group-level effects to be considered in the prediction. If NULL (default), include all group-level effects; if NA, include no group-level effects.
* ``scale`` Either "response" or "linear". If "response" results are returned on the scale of the response variable. If "linear" fitted #### Values are returned on the scale of the linear predictor.
* `` resp``: Optional names of response variables. If specified, predictions are performed only for the specified response variables.
* ``dpar`` Optional name of a predicted distributional parameter. If specified, fitted values of this parameters are returned.
* ``nlpar`` Optional name of a predicted non-linear parameter. If specified, fitted values of this parameters are returned.
* ``nsamples`` Positive integer indicating how many posterior samples should be used. If NULL (the default) all samples are used. Ignored if subset is not NULL.
* ``subset`` A numeric vector specifying the posterior samples to be used. If NULL (the default), all samples are used.
* ``sort`` Logical. Only relevant for time series models. Indicating whether to return predicted values in the original order (FALSE; default) or in the order of the time series (TRUE).
* ``summary`` Should summary statistics (i.e. means, sds, and 95% intervals) be returned instead of the raw values? Default is TRUE.
* ``robust`` If FALSE (the default) the mean is used as the measure of central tendency and the standard deviation as the measure of variability. If TRUE, the median and the median absolute deviation (MAD) are applied instead. Only used if summary is TRUE.
* ``probs``: The percentiles to be computed by the quantile function. Only used if summary is TRUE.
* `` ... ``: Further arguments passed to extract_draws that control several aspects of data validation and prediction.
* ``transform``: Logical; alias of scale. If TRUE, scale is set to "response". If FALSE, scale is set to "linear". Only implemented for compatibility with the ``posterior_linpred`` generic.
* ``re.form``: Alias of re_formula.
#### Examples
```{r}
## fit a model
fit <- brm(rating ~ treat + period + carry + (1|subject), data = inhaler)
```
```{r}
## extract fitted vValues
fitted_values <- fitted(fit)
head(fitted_values)
```
```{r}
## plot fitted means against actual response
dat <- as.data.frame(cbind(Y = standata(fit)$Y, fitted_values))
ggplot(dat) + geom_point(aes(x = Estimate, y = Y))
## End(Not run)
```
#### Details
NA values within factors in newdata, are interpreted as if all dummy variables of this factor are zero. This allows, for instance, to make predictions of the grand mean when using sum coding. Method posterior_linpred.brmsfit is an alias of fitted.brmsfit with scale = "linear" and summary = FALSE.
#### Value
Fitted values extracted from object. The output depends on the family: If summary = TRUE it is a N x E x C array for categorical and ordinal models and a N x E matrix else. If summary = FALSE it is a S x N x C array for categorical and ordinal models and a S x N matrix else.
* N is the number of observations, S is the number of samples, C is the number of categories, and E is equal to length(probs) + 2.
* In multivariate models, the output is an array of 3 dimensions, where the third dimension indicates the response variables.