The R package *bamlss* provides a modular computational
framework for flexible Bayesian regression models (and beyond). The
implementation follows the conceptional framework presented in Umlauf, Klein, and Zeileis (2018) and provides a
modular “Lego toolbox” for setting up regression models. In this setting
not only the response distribution or the regression terms are “Lego
bricks” but also the estimation algorithm or the MCMC sampler.

The highlights of the package are:

- The usual R “look & feel” for regression modeling.
- Estimation of classic (GAM-type) regression models (Bayesian or frequentist).
- Estimation of flexible (GAMLSS-type) distributional regression models.
- An extensible “plug & play” approach for regression terms.
- Modular combinations of fitting algorithms and samplers.

Especially the last item is notable because the models in
*bamlss* are not limited to a specific estimation algorithm but
different engines can be plugged in without necessitating changes in
other aspects of the model specification.

More detailed overviews and examples are provided in the articles:

The stable release version of *bamlss* is hosted on the
Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=bamlss and can be
installed via

The development version of *bamlss* is hosted on R-Forge at https://R-Forge.R-project.org/projects/bayesr/ in a
Subversion (SVN) repository. It can be installed via

This section gives a first quick overview of the functionality of the
package and demonstrates that the usual “look & feel” when using
well-established model fitting functions like `glm()`

is an
elementary part of *bamlss*, i.e., first steps and basic handling
of the package should be relatively simple. We illustrate the first
steps with *bamlss* using a data set taken from the
*Regression Book* (Fahrmeir et al.
2013) which is about prices of used VW Golf cars. The data is
loaded with

```
## price age kilometer tia abs sunroof
## 1 7.30 73 10 12 yes yes
## 2 3.85 115 30 20 yes no
## 3 2.95 127 43 6 no yes
## 4 4.80 104 54 25 yes yes
## 5 6.20 86 57 23 no no
## 6 5.90 74 57 25 yes no
```

In this example the aim is to model the `price`

in 1000
Euro. Using *bamlss* a first Bayesian linear model could be set
up by first specifying a model formula

afterwards the fully Bayesian model using MCMC simulation is estimated by

Note that the default number of iterations for the MCMC sampler is
1200, the burnin-phase is 200 and thinning is 1 (see the manual of the
default MCMC sampler `sam_GMCMC()`

).
The reason is that during the modeling process, users usually want to
obtain first results rather quickly. Afterwards, if a final model is
estimated the number of iterations of the sampler is usually set much
higher to get close to i.i.d. samples from the posterior distribution.
To obtain reasonable starting values for the MCMC sampler we run a
backfitting algorithm that optimizes the posterior mode. The
*bamlss* package uses its own family objects, which can be
specified as characters using the `bamlss()`

wrapper, in this case `family = "gaussian"`

(see also BAMLSS
Families). In addition, the package also supports all families
provided from the *gamlss* families.

The model summary gives

```
##
## Call:
## bamlss(formula = f, family = "gaussian", data = Golf)
## ---
## Family: gaussian
## Link function: mu = identity, sigma = log
## *---
## Formula mu:
## ---
## price ~ age + kilometer + tia + abs + sunroof
## -
## Parametric coefficients:
## Mean 2.5% 50% 97.5% parameters
## (Intercept) 9.333318 8.526293 9.330200 10.173709 9.311
## age -0.038461 -0.045355 -0.038341 -0.031706 -0.038
## kilometer -0.009686 -0.012547 -0.009667 -0.007061 -0.010
## tia -0.005811 -0.022870 -0.005752 0.010105 -0.005
## absyes -0.240481 -0.492048 -0.237776 -0.003060 -0.238
## sunroofyes -0.024021 -0.300878 -0.025127 0.238145 -0.010
## -
## Acceptance probability:
## Mean 2.5% 50% 97.5%
## alpha 1 1 1 1
## ---
## Formula sigma:
## ---
## sigma ~ 1
## -
## Parametric coefficients:
## Mean 2.5% 50% 97.5% parameters
## (Intercept) -0.2457 -0.3479 -0.2465 -0.1274 -0.271
## -
## Acceptance probability:
## Mean 2.5% 50% 97.5%
## alpha 0.9703 0.7652 1.0000 1
## ---
## Sampler summary:
## -
## DIC = 408.9675 logLik = -201.0372 pd = 6.8932
## runtime = 0.682
## ---
## Optimizer summary:
## -
## AICc = 409.6319 edf = 7 logLik = -197.4745
## logPost = -252.2614 nobs = 172 runtime = 0.089
```

indicating high acceptance rates as reported by the
`alpha`

parameter in the linear model output, which is a sign
of good mixing of the MCMC chains. The mixing can also be inspected
graphically by

Note, for convenience we only show the traceplots of the intercepts.
Considering significance of the estimated effects, only variables
`tia`

and `sunroof`

seem to have no effect on
`price`

since the credible intervals of estimated parameters
contain zero. This information can also be extracted using the
implemented `confint()`

method.

```
## 2.5% 97.5%
## mu.(Intercept) 8.52629257 10.173709336
## mu.age -0.04535461 -0.031705531
## mu.kilometer -0.01254739 -0.007060627
## mu.tia -0.02286985 0.010105028
## mu.absyes -0.49204765 -0.003060006
## mu.sunroofyes -0.30087769 0.238144948
## sigma.(Intercept) -0.34791813 -0.127380063
```

Since the prices cannot be negative, a possible consideration is to
use a logarithmic transformation of the response `price`

```
set.seed(111)
f <- log(price) ~ age + kilometer + tia + abs + sunroof
b2 <- bamlss(f, family = "gaussian", data = Golf)
```

and compare the models using the `predict()`

method

`## [1] 0.5818444`

`## [1] 0.5410859`

indicating that the transformation seems to improve the model fit.

Instead of using linear effects, another option would be to use
polynomial regression for covariates `age`

,
`kilometer`

and `tia`

. A polynomial model using
polynomials of order 3 is estimated with

```
set.seed(222)
f <- log(price) ~ poly(age, 3) + poly(kilometer, 3) + poly(tia, 3) + abs + sunroof
b3 <- bamlss(f, family = "gaussian", data = Golf)
```

Comparing the models using the `DIC()`

function

```
## DIC pd
## b2 -15.19596 6.893153
## b3 -10.78925 13.186991
```

suggests that the polynomial model is slightly better. The effects
can be inspected graphically, to better understand their influence on
`price`

. Using the polynomial model, graphical inspections
can be done using the `predict()`

method.

One major difference compared to other regression model
implementations is that predictions can be made for single variables,
only, where the user does not have to create a new data frame containing
all variables. For example, posterior mean estimates and 95% credible
intervals for variable `age`

can be obtained by

```
nd <- data.frame("age" = seq(min(Golf$age), max(Golf$age), length = 100))
nd$page <- predict(b3, newdata = nd, model = "mu", term = "age",
FUN = c95, intercept = FALSE)
head(nd)
```

```
## age page.2.5% page.Mean page.97.5%
## 1 65.00000 0.3085312 0.4849915 0.6592331
## 2 65.77778 0.3120472 0.4757206 0.6362999
## 3 66.55556 0.3167776 0.4663256 0.6152946
## 4 67.33333 0.3172396 0.4568109 0.5950657
## 5 68.11111 0.3201536 0.4471811 0.5742218
## 6 68.88889 0.3202023 0.4374406 0.5555146
```

Note that the prediction does not include the model intercept.
Similarly for variables `kilometer`

and `tia`

```
nd$kilometer <- seq(min(Golf$kilometer), max(Golf$kilometer), length = 100)
nd$tia <- seq(min(Golf$tia), max(Golf$tia), length = 100)
nd$pkilometer <- predict(b3, newdata = nd, model = "mu", term = "kilometer",
FUN = c95, intercept = FALSE)
nd$ptia <- predict(b3, newdata = nd, model = "mu", term = "tia",
FUN = c95, intercept = FALSE)
```

Here, we need to specify for which `model`

predictions
should be calculated, and if predictions only for variable
`age`

are created, argument `term`

needs also be
specified. Argument `FUN`

can be any function that should be
applied on the samples of the linear predictor. For more examples see
the documentation of the `predict.bamlss()`

method.

Then, the estimated effects can be visualized with

```
par(mfrow = c(1, 3))
ylim <- range(c(nd$page, nd$pkilometer, nd$ptia))
plot2d(page ~ age, data = nd, ylim = ylim)
plot2d(pkilometer ~ kilometer, data = nd, ylim = ylim)
plot2d(ptia ~ tia, data = nd, ylim = ylim)
```

The figure clearly shows the negative effect on the logarithmic
`price`

for variable `age`

and
`kilometer`

. The effect of `tia`

is not
significant according the 95% credible intervals, since the interval
always contains the zero horizontal line.

As a second startup on how to use *bamlss* for full
distributional regression, we illustrate the basic steps on a small
textbook example using the well-known simulated motorcycle accident data
(Silverman 1985). The data contain
measurements of the head acceleration (in \(g\), variable `accel`

) in a
simulated motorcycle accident, recorded in milliseconds after impact
(variable `times`

).

```
## times accel
## 1 2.4 0.0
## 2 2.6 -1.3
## 3 3.2 -2.7
## 4 3.6 0.0
## 5 4.0 -2.7
## 6 6.2 -2.7
```

To estimate a Gaussian location-scale model with \[ \texttt{accel} \sim \mathcal{N}(\mu = f(\texttt{times}), \log(\sigma) = f(\texttt{times})) \] we use the following model formula

where `s()`

is the smooth term constructor from the
*mgcv* (Wood 2020). Note, that
formulae are provided as `list`

s of formulae, i.e., each list
entry represents one parameter of the response distribution. Also note
that all smooth terms, i.e., `te()`

, `ti()`

, etc.,
are supported by *bamlss*. This way, it is also possible to
incorporate user defined model terms. A fully Bayesian model is the
estimated with

using the default of 1200 iterations of the MCMC sampler to obtain
first results quickly (see the documentation `sam_GMCMC()`

for further details on tuning parameters). Note that per default
`bamlss()`

uses a backfitting algorithm to compute posterior mode estimates,
afterwards these estimates are used as starting values for the MCMC
chains. The returned object is of class `"bamlss"`

for which
generic extractor functions like `summary()`

,
`plot()`

, `predict()`

, etc., are provided. For
example, the estimated effects for distribution parameters
`mu`

and `sigma`

can be visualized by

The model summary gives

```
##
## Call:
## bamlss(formula = f, family = "gaussian", data = mcycle)
## ---
## Family: gaussian
## Link function: mu = identity, sigma = log
## *---
## Formula mu:
## ---
## accel ~ s(times, k = 20)
## -
## Parametric coefficients:
## Mean 2.5% 50% 97.5% parameters
## (Intercept) -25.13 -29.36 -25.35 -20.34 -25.14
## -
## Acceptance probability:
## Mean 2.5% 50% 97.5%
## alpha 1 1 1 1
## -
## Smooth terms:
## Mean 2.5% 50% 97.5% parameters
## s(times).tau21 425657.47 175634.81 372121.15 914429.32 209325.2
## s(times).alpha 1.00 1.00 1.00 1.00 NA
## s(times).edf 14.24 12.64 14.22 15.97 13.6
## ---
## Formula sigma:
## ---
## sigma ~ s(times, k = 20)
## -
## Parametric coefficients:
## Mean 2.5% 50% 97.5% parameters
## (Intercept) 2.680 2.549 2.676 2.831 2.581
## -
## Acceptance probability:
## Mean 2.5% 50% 97.5%
## alpha 0.9664 0.7510 1.0000 1
## -
## Smooth terms:
## Mean 2.5% 50% 97.5% parameters
## s(times).tau21 1.458e+02 2.384e+01 1.213e+02 4.604e+02 81.406
## s(times).alpha 5.385e-01 7.903e-04 5.069e-01 1.000e+00 NA
## s(times).edf 9.415e+00 6.491e+00 9.500e+00 1.259e+01 8.675
## ---
## Sampler summary:
## -
## DIC = 1115.068 logLik = -545.2265 pd = 24.6149
## runtime = 1.946
## ---
## Optimizer summary:
## -
## AICc = 1123.881 edf = 24.2718 logLik = -531.975
## logPost = -747.4106 nobs = 133 runtime = 0.192
```

showing, e.g., the acceptance probabilities of the MCMC chains
(`alpha`

), the estimated degrees of freedom of the optimizer
and the successive sampler (`edf`

), the final AIC and DIC as
well as parametric model coefficients (in this case only the
intercepts). As mentioned in the first example, using MCMC involves
convergence checks of the sampled parameters. The easiest diagnostics
are traceplots

Note again that this call would show all traceplots, for convenience we
only show the plots for the intercepts. In this case, the traceplots do
not indicate convergence of the Markov chains for parameter
`"mu"`

. To fix this, the number of iterations can be
increased and also the burnin and thinning parameters can be adapted
(see `sam_GMCMC()`

).
Further inspections are the maximum autocorrelation of all parameters,
using `plot.bamlss()`

setting argument `which = "max-acf"`

, besides other
convergence diagnostics, e.g., diagnostics that are part of the
*coda* package (Plummer et al.
2006).

Inspecting randomized quantile residuals (Dunn and Smyth 1996) is useful for judging how well the model fits to the data

Randomized quantile residuals are the default method in *bamlss*,
which are computed using the CDF function of the corresponding family
object.

The posterior mean including 95% credible intervals for new data based on MCMC samples for parameter \(\mu\) can be computed by

```
nd <- data.frame("times" = seq(2.4, 57.6, length = 100))
nd$ptimes <- predict(b, newdata = nd, model = "mu", FUN = c95)
plot2d(ptimes ~ times, data = nd)
```

and as above in the first example, argument `FUN`

can be any
function, e.g., the `identity()`

function could be used to
calculate other statistics of the distribution, e.g., plot the estimated
densities for each iteration of the MCMC sampler for
`times = 10`

and `times = 40`

:

```
## Predict for the two scenarios.
nd <- data.frame("times" = c(10, 40))
ptimes <- predict(b, newdata = nd, FUN = identity, type = "parameter")
## Extract the family object.
fam <- family(b)
## Compute densities.
dens <- list("t10" = NULL, "t40" = NULL)
for(i in 1:ncol(ptimes$mu)) {
## Densities for times = 10.
par <- list(
"mu" = ptimes$mu[1, i, drop = TRUE],
"sigma" = ptimes$sigma[1, i, drop = TRUE]
)
dens$t10 <- cbind(dens$t10, fam$d(mcycle$accel, par))
## Densities for times = 40.
par <- list(
"mu" = ptimes$mu[2, i, drop = TRUE],
"sigma" = ptimes$sigma[2, i, drop = TRUE]
)
dens$t40 <- cbind(dens$t40, fam$d(mcycle$accel, par))
}
## Visualize.
par(mar = c(4.1, 4.1, 0.1, 0.1))
col <- rainbow_hcl(2, alpha = 0.01)
plot2d(dens$t10 ~ accel, data = mcycle,
col.lines = col[1], ylab = "Density")
plot2d(dens$t40 ~ accel, data = mcycle,
col.lines = col[2], add = TRUE)
```

Dunn, Peter K., and Gordon K. Smyth. 1996. “Randomized Quantile
Residuals.” *Journal of Computational and Graphical
Statistics* 5 (3): 236–44.

Fahrmeir, Ludwig, Thomas Kneib, Stefan Lang, and Brian Marx. 2013.
*Regression – Models, Methods and Applications*. Berlin:
Springer-Verlag.

Plummer, Martyn, Nicky Best, Kate Cowles, and Karen Vines. 2006.
“coda: Convergence Diagnosis and
Output Analysis for MCMC.” *R News* 6 (1):
7–11. https://doi.org/10.18637/jss.v021.i11.

Silverman, B. W. 1985. “Some Aspects of the Spline Smoothing
Approach to Non-Parametric Regression Curve Fitting.” *Journal
of the Royal Statistical Society. Series B (Methodological)* 47 (1):
1–52.

Umlauf, Nikolaus, Nadja Klein, and Achim Zeileis. 2018.
“BAMLSS: Bayesian Additive Models for
Location, Scale and Shape (and Beyond).” *Journal of
Computational and Graphical Statistics* 27 (3): 612–27. https://doi.org/10.1080/10618600.2017.1407325.

Umlauf, Nikolaus, Nadja Klein, Achim Zeileis, and Thorsten Simon. 2021.
“bamlss: Bayesian
Additive Models for Location Scale and Shape (and Beyond).”
*Journal of Statistical Software* 100 (4): 1–53. https://doi.org/10.18637/jss.v100.i04.

———. 2024. *bamlss: Bayesian
Additive Models for Location Scale and Shape (and Beyond)*. https://CRAN.R-project.org/package=bamlss.

Wood, S. N. 2020. *mgcv: GAMs with
GCV/AIC/REML Smoothness Estimation and GAMMs by PQL*. https://CRAN.R-project.org/package=mgcv.