`bfit.Rd`

This optimizer function is a generic tool for fitting BAMLSS using a backfitting algorithm. The backfitting procedure is based on iteratively weighted least squares (IWLS) for finding posterior mode estimates, however, the updating methods for model terms can be more general, see the details section. In addition, the default IWLS updating scheme implements optimum smoothing variance selection based on information criteria using a stepwise approach.

## Optimizer functions: opt_bfit(x, y, family, start = NULL, weights = NULL, offset = NULL, update = "iwls", criterion = c("AICc", "BIC", "AIC"), eps = .Machine$double.eps^0.25, maxit = 400, outer = NULL, inner = FALSE, mgcv = FALSE, verbose = TRUE, digits = 4, flush = TRUE, nu = TRUE, stop.nu = NULL, ...) bfit(x, y, family, start = NULL, weights = NULL, offset = NULL, update = "iwls", criterion = c("AICc", "BIC", "AIC"), eps = .Machine$double.eps^0.25, maxit = 400, outer = NULL, inner = FALSE, mgcv = FALSE, verbose = TRUE, digits = 4, flush = TRUE, nu = TRUE, stop.nu = NULL, ...) ## Model term updating functions: bfit_iwls(x, family, y, eta, id, weights, criterion, ...) bfit_iwls_Matrix(x, family, y, eta, id, weights, criterion, ...) bfit_lm(x, family, y, eta, id, weights, criterion, ...) bfit_optim(x, family, y, eta, id, weights, criterion, ...) bfit_glmnet(x, family, y, eta, id, weights, criterion, ...)

x | For function |
---|---|

y | The model response, as returned from function |

family | A bamlss family object, see |

start | A named numeric vector containing possible starting values, the names are based on
function |

weights | Prior weights on the data, as returned from function |

offset | Can be used to supply model offsets for use in fitting,
returned from function |

update | Sets the updating function for model terms, e.g. for a term |

criterion | Set the information criterion that should be used, e.g., for smoothing
variance selection. Options are the corrected AIC |

eps | The relative convergence tolerance of the backfitting algorithm. |

maxit | The maximum number of iterations for the backfitting algorithm |

outer | Should the current working observations and weights be computed in one outer iteration, otherwise the working observations are computed anew for each model term updating step. The default will run one outer iteration first, afterwards model weights are computed for each term anew. |

inner | Should the model terms for one parameter of the modeled distribution be fully updated until convergence in an inner iteration, i.e., the algorithm waits until coefficients for the current distribution parameter do not change anymore before updating the next parameter. |

mgcv | Should the mgcv |

verbose | Print information during runtime of the algorithm. |

digits | Set the digits for printing when |

flush | use |

nu | Logical, numeric or |

stop.nu | Integer. Should step length reduction be stopped after |

eta | The current value of the predictors, provided as a named list, one list entry
for each parameter. The names correspond to the parameter names in the family object,
see |

id | Character, the name of the current parameter for which the model term should be updated. |

… | For function |

This algorithm is based on iteratively weighted least squares (IWLS) for BAMLSS, i.e., a Newton-Raphson or Fisher scoring algorithm is applied, similar to Rigby~and~Stasinopoulos~(2005). The algorithm utilizes the chain rule for computing derivatives of the log-posterior w.r.t. regression coefficients, therefore, to compute the working observations and weights only the derivatives of the log-likelihood w.r.t. the predictors are required.

It is assumed that the provided `family`

object holds functions for computing the first
and second order derivatives of the log-likelihood w.r.t. the predictors. These Functions
are provided within the named lists `"score"`

and `"hess"`

within the `family`

object. See the documentation of `family.bamlss`

and the code of the provided
families, e.g. `gaussian_bamlss`

, for examples of the required structure.

The algorithm either updates each model term over all distributional parameters sequentially,
or does a full update until convergence for model terms for one distributional parameter before
updating the next parameter, see argument `inner`

. Additionally, working observations and
weights can be computed only once in an `outer`

iteration.

Starting values of regression coefficients and smoothing variances can be supplied, moreover,
if a family object holds functions for initializing the distributional parameters, see also
`family.bamlss`

, starting values are based on the initialize functions.

The default updating function for model terms is based on IWLS, which is assigned by function
`bamlss.engine.setup`

, however, special updating functions can be used.
This is achieved by providing an updating function to argument
`update`

, which should be used for all model terms. Another option is to set the updating
function within the `xt`

argument of the mgcv smooth term constructor functions, see
e.g. function `s`

. If the `xt`

list then holds an element named `"update"`

,
which is a valid updating function, this updating function is used for the corresponding model
term. This way it is possible to call different (special) updating functions for specific terms,
e.g., that do not fit in the IWLS scheme. See the examples below. Note that this does not work if
`mgcv = TRUE`

, since the `gam`

function assumes a strict linear
representation of smooth terms.

A model term updating function has the following arguments:

`update(x, family, y, eta, id, weights, criterion, ...)`

Here `x`

is an object as returned from function `smooth.construct`

or `smoothCon`

. The `x`

object is preprocessed by function
`bamlss.engine.setup`

, i.e., an element called `"state"`

is assigned. The state
element represents the current state of the model term holding the current values of the
parameters with corresponding fitted values, as well as equivalent degrees of freedom, see
also the values that are returned by such functions below. The backfitting algorithm uses the
state of a model term for generating updates of the parameters. Note that for special model
terms the state list should already be provided within the call to the corresponding
smooth constructor function, see the growth curve example below.

In addition, for special model terms the fitted values may not be computed by a linear combination
of the design matrix and the coefficients. Therefore, the `x`

object should hold an element
named `"fit.fun"`

which is a function for computing the fitted values.
See also `smooth.construct.bamlss.frame`

and `predict.bamlss`

that use
this setup. The arguments of fitting functions are

`fit.fun(X, b, ...)`

where `X`

is the design matrix and `b`

is the vector of coefficients. Hence, for
usual IWLS updating the fitted values are computed by `X %*% b`

. For special terms like
nonlinear growth curves this may not be the case, see the example below. The fitting functions
are assigned by `bamlss.engine.setup`

, unless the function is already provided
after calling the constructor function `smooth.construct`

or
`smoothCon`

. Note that the dots argument is usually not needed by the user.

The default updating function is `bfit_iwls()`

. Function `bfit_iwls_Matrix()`

uses the
sparse matrix infrastructures of package Matrix. The Matrix package and
`bfit_iwls_Matrix()`

is used for model terms where
the maximum number of non-zero entries in the design matrix is less than half of the total number
of columns, if an additional argument `force.Matrix`

is set to `TRUE`

in the
`opt_bfit()`

call.

The IWLS updating functions find optimum smoothing variances according to an information criterion using a stepwise approach, i.e., in each iteration and for each model term update the updating functions try to find a better smoothing variance to control the trade-off between over-smoothing and nonlinear functional estimation. The search interval is centered around the current state of the smoothing variances, hence, in each iteration only a slight improvement is achieved. This algorithm is based on Belitz~and~Lang~(2008) and can also be viewed as a boosting approach for optimization.

For function `opt_bfit()`

a list containing the following objects:

A named list of the fitted values of the modeled parameters of the selected distribution.

The estimated set regression coefficients and smoothing variances.

The equivalent degrees of freedom used to fit the model.

The value of the log-likelihood.

The value of the log-posterior.

The value of the information criterion.

Logical, indicating convergence of the backfitting algorithm.

The resulting fitted values after updating.

The resulting named numeric vector of updated model term parameters.
Coefficients should be named with `"b1"`

, …, `"bk"`

, where `k`

is
the total number of coefficients. Smoothing variances should be named with
`"tau21"`

, …, `"tau2m"`

, where `m`

is the total number of smoothing
variances assigned to the model term.

The equivalent degrees of freedom used to produce the fitted values.

Optional, the coefficient Hessian information

Optional, the value of the log-prior of the model term.

Belitz C, Lang S (2008). Simultaneous Selection of Variables and Smoothing Parameters in
Structured Additive Regression Models. *Computational Statistics \& Data Analysis*,
**53**, pp 61-81.

Umlauf N, Klein N, Zeileis A (2016). Bayesian Additive Models for Location
Scale and Shape (and Beyond). *(to appear)*

Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location,
scale and shape, (with discussion), *Appl. Statist.*, **54**, part 3, pp 507-554.

# NOT RUN { ## Simulated data example illustrating ## how to call the optimizer function. ## This is done internally within ## the setup of function bamlss(). d <- GAMart(n = 200) f <- num ~ s(x1) + s(x2) + s(x3) bf <- bamlss.frame(f, data = d, family = "gaussian") opt <- with(bf, opt_bfit(x, y, family)) print(str(opt)) ## Same with bamlss(). b <- bamlss(f, data = d, family = "gaussian", sampler = FALSE) plot(b) summary(b) ## Use of different updating function. b <- bamlss(f, data = d, family = "gaussian", sampler = FALSE, update = bfit_lm) plot(b) ## Use mgcv gam() function for updating. b <- bamlss(f, data = d, family = "gaussian", sampler = FALSE, mgcv = TRUE) plot(b) ## Special smooth constructor including updating/sampler ## function for nonlinear Gompertz curves. ## Note: element special.npar is needed here since this ## function has 3 parameters but the design matrix only ## one column! smooth.construct.gc.smooth.spec <- function(object, data, knots) { object$X <- matrix(as.numeric(data[[object$term]]), ncol = 1) center <- if(!is.null(object$xt$center)) { object$xt$center } else TRUE object$by.done <- TRUE if(object$by != "NA") stop("by variables not supported!") object$fit.fun <- function(X, b, ...) { f <- b[1] * exp(-b[2] * exp(-b[3] * drop(X))) if(center) f <- f - mean(f) f } object$update <- bfit_optim object$propose <- GMCMC_slice object$prior <- function(b) { sum(dnorm(b, sd = 1000, log = TRUE)) } object$fixed <- TRUE object$state$parameters <- c("b1" = 0, "b2" = 0.5, "b3" = 0.1) object$state$fitted.values <- rep(0, length(object$X)) object$state$edf <- 3 object$special.npar <- 3 ## Important! class(object) <- c("gc.smooth", "no.mgcv", "special") object } ## Work around for the "prediction matrix" of a growth curve. Predict.matrix.gc.smooth <- function(object, data, knots) { X <- matrix(as.numeric(data[[object$term]]), ncol = 1) X } ## Heteroscedastic growth curve data example. set.seed(111) d <- data.frame("time" = 1:30) d$y <- 2 + 1 / (1 + exp(0.5 * (15 - d$time))) + rnorm(30, sd = exp(-3 + 2 * cos(d$time/30 * 6 - 3))) ## Special model terms must be called with s2()! f <- list( y ~ s2(time, bs = "gc"), sigma ~ s(time) ) ## Fit model with special model term. b <- bamlss(f, data = d, optimizer = opt_bfit, sampler = sam_GMCMC) ## Plot the fitted curves. plot(b) ## Predict with special model term. nd <- data.frame("time" = seq(1, 30, length = 100)) p <- predict(b, newdata = nd, model = "mu", FUN = c95) plot(d, ylim = range(c(d$y, p))) matplot(nd$time, p, type = "l", lty = c(2, 1, 2), col = "black", add = TRUE) # }