library("bamlss") prefix <- "http://www.bamlss.org/articles/" ## "" prefix2 <- "http://www.bamlss.org/reference/" ## ""

The R package *bamlss* provides a modular computational framework for flexible Bayesian
regression models (and beyond). The implementation follows the conceptional framework presented in
@bamlss:Umlauf+Klein+Zeileis:2017 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:

- Available Model Terms
- BAMLSS Families
- BAMLSS Model Frame
- Estimation Engines
- Bayesian Multinomial Regression
- Bivariate Gaussian models for wind vectors
- Complex Space-Time Interactions in a Cox Model
- Generalized Linear Models (+)
- LASSO-Type Penalization in the Framework of Generalized Additive Models for Location, Scale and Shape
- Munich Rent Model
- Spatial Location-Scale Model
- Fatalities Model for Austria
- Multivariate Normal Model

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

install.packages("bamlss")

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

install.packages("bamlss", repos = "http://R-Forge.R-project.org")

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* [@bamlss:Fahrmeir+Kneib+Lang+Marx:2013]
which is about prices of used VW Golf cars. The data is loaded with

data("Golf", package = "bamlss") head(Golf)

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

f <- price ~ age + kilometer + tia + abs + sunroof

afterwards the fully Bayesian model using MCMC simulation is estimated by

library("bamlss") set.seed(111) b1 <- bamlss(f, family = "gaussian", data = Golf)

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

```
summary(b1)
```

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

plot(b1, which = "samples")

bsp <- b1 bsp$samples <- bsp$samples[, c("mu.p.(Intercept)", "sigma.p.(Intercept)")] plot(bsp, which = "samples")

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.

confint(b1, prob = c(0.025, 0.975))

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 `DIC()`

```
DIC(b1, b2)
```

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(b1, b2, b3)
```

suggests that the polynomial model is not really an improvement, however, this is probably caused
by an insignificant `tia`

effect. 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)

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)

par(mfrow = c(1, 3), mar = c(4.1, 4.1, 0.1, 1.1)) 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.

data("mcycle", package = "MASS") f <- list(accel ~ s(times, k = 20), sigma ~ s(times, k = 20)) set.seed(123) b <- bamlss(f, data = mcycle, family = "gaussian")

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 [@bamlss: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`

).

data("mcycle", package = "MASS") head(mcycle)

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

f <- list(accel ~ s(times, k = 20), sigma ~ s(times, k = 20))

where `s()`

is the smooth term constructor from the *mgcv* [@bamlss:Wood:2018]. 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

set.seed(123) b <- bamlss(f, data = mcycle, family = "gaussian")

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

plot(b, model = c("mu", "sigma"))

par(mar = c(4.1, 4.1, 1.1, 1.1), mfrow = c(1, 2)) plot(b, pages = 1, spar = FALSE, scheme = 2, grid = 100)

The model summary gives

```
summary(b)
```

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

plot(b, which = "samples")

bsp <- b bsp$samples <- bsp$samples[, c("mu.p.(Intercept)", "sigma.p.(Intercept)")] plot(bsp, which = "samples")

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 [@bamlss:Plummer+Best+Cowles+Vines:2006].

Inspecting randomized quantile residuals [@bamlss:Dunn+Gordon:1996] is useful for judging how well the model fits to the data

plot(b, which = c("hist-resid", "qq-resid"))

par(mfrow = c(1, 2)) plot(b, which = c("hist-resid", "qq-resid"), spar = FALSE)

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)

par(mar = c(4.1, 4.1, 1.1, 1.1)) 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)

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