bamlss: lasso – R documentation

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lasso

Lasso Smooth Constructor

Description

Smooth constructors and optimizer for Lasso penalization with bamlss. The penalization is based on a Taylor series approximation of the Lasso penalty.

Usage

## Smooth constructor function.
la(formula, type = c("single", "multiple"), ...)

## Single Lasso smoothing parameter optimizer.
opt_lasso(x, y, start = NULL, adaptive = TRUE, lower = 0.001, upper = 1000,
  nlambda = 100, lambda = NULL,  multiple = FALSE, verbose = TRUE,
  digits = 4, flush = TRUE, nu = NULL, stop.nu = NULL,
  ridge = .Machine$double.eps^0.5, zeromodel = NULL, ...)

lasso(x, y, start = NULL, adaptive = TRUE, lower = 0.001, upper = 1000,
  nlambda = 100, lambda = NULL,  multiple = FALSE, verbose = TRUE,
  digits = 4, flush = TRUE, nu = NULL, stop.nu = NULL,
  ridge = .Machine$double.eps^0.5, zeromodel = NULL, ...)

## Lasso transformation function to set
## adaptive weights from an unpenalized model.
lasso_transform(x, zeromodel, nobs = NULL, ...)

## Plotting function for opt_lasso() optimizer.
lasso_plot(x, which = c("criterion", "parameters"),
  spar = TRUE, model = NULL, name = NULL, mstop = NULL,
  retrans = FALSE, color = NULL, show.lambda = TRUE,
  labels = NULL, digits = 2, ...)

## Extract optimum stopping iteration for opt_lasso() optimizer.
## Based on the minimum of the information criterion.
lasso_stop(x)

## Extract retransformed Lasso coefficients.
lasso_coef(x, ...)

Arguments

`formula`	A formula like `~ x1 + x2 + ... + xk` of variables which should be penalized with Lasso.
`type`	Should one single penalty parameter be used or multiple parameters, one for each covariate in `formula`.
`x`	For function `opt_lasso()` and `lasso_transform()` the `x` list, as returned from function `bamlss.frame`, holding all model matrices and other information that is used for fitting the model. For the plotting function and `lasso_stop()`/`lasso_coef()` the corresponding `bamlss` object fitted with the `opt_lasso()` optimizer.
`y`	The model response, as returned from function `bamlss.frame`.
`start`	A vector of starting values. Note, Lasso smoothing parameters will be dropped.
`adaptive`	Should adaptive weights be used for fused Lasso terms?
`lower`	Numeric. The minimum lambda value.
`upper`	Numeric. The maximum lambda value.
`nlambda`	Integer. The number of smoothing parameters for which coefficients should be estimated, i.e., the vector of smoothing parameters is build up as a sequence from `lower` to `upper` with length `nlambda`.
`lambda`	Numeric. A sequence/vector of lambda parameters that should be used.
`multiple`	Logical. Should the lambda grid be exapnded to search for multiple lambdas, one for each distributional parameter.
`verbose`	Print information during runtime of the algorithm.
`digits`	Set the digits for printing when `verbose = TRUE`. If the optimum lambda value is plotted, the number of decimal decimal places to be used within `lasso_plot()`.
`flush`	use `flush.console` for displaying the current output in the console.
`nu`	Numeric or logical. Defines the step length for parameter updating of a model term, useful when the algorithm encounters convergence problems. If `nu = TRUE` the step length parameter is optimized for each model term in each iteration of the backfitting algorithm.
`stop.nu`	Integer. Should step length reduction be stopped after `stop.nu` iterations of the Lasso algorithm?
`ridge`	A ridge penalty parameter that should be used when finding adaptive weights, i.e., parameters from an unpenalized model. The ridge penalty is used to stabilize the estimation in complex models.
`zeromodel`	A model containing the unpenalized parameters, e.g., for each `la()` terms one can place a simple ridge penalty with `la(x, ridge = TRUE, sp = 0.1)`. This way it is possible to find the unpenalized parameters that can be used as adaptive weights for fusion penalties.
`nobs`	Integer, number of observations of the data used for modeling. If not supplied `nobs` is taken from the number of rows from the model term design matrices.
`which`	Which of the two provided plots should be created, character or integer `1` and `2`.
`spar`	Should graphical parameters be set by the plotting function?
`model`	Character selecting for which model the plot shpuld be created.
`name`	Character, the name of the coefficient group that should be plotted. Note that the string provided in `name` will be removed from the labels on the 4th axis.
`mstop`	Integer vector, defines the path length to be plotted.
`retrans`	Logical, should coefficients be re-transformed before plotting?
`color`	Colors or color function that creates colors for the group paths.
`show.lambda`	Logical. Should the optimum value of the penalty parameter lambda be shown?
`labels`	A character string of labels that should be used on the 4 axis.
`...`	Arguments passed to the subsequent smooth constructor function. `lambda` controls the starting value of the penalty parameter, `const` the constant that is added within the penalty approximation. Moreover, `fuse = 1` enforces nominal fusion of categorical variables and `fuse = 2` ordered fusion within `la()` Note that `la()` terms with and without fusion should not be mixed when using the `opt_lasso()` optimizer function. For the optimizer `opt_lasso()` arguments passed to function `bfit`.

Value

For function la(), similar to function s a simple smooth specification object.

For function opt_lasso() a list containing the following objects:

`fitted.values`	A named list of the fitted values based on the last lasso iteration of the modeled parameters of the selected distribution.
`parameters`	A matrix, each row corresponds to the parameter values of one boosting iteration.
`lasso.stats`	A matrix containing information about the log-likelihood, log-posterior and the information criterion for each lambda.

References

Andreas Groll, Julien Hambuckers, Thomas Kneib, and Nikolaus Umlauf (2019). Lasso-type penalization in the framework of generalized additive models for location, scale and shape. Computational Statistics \& Data Analysis. doi: 10.1016/j.csda.2019.06.005

Oelker Margreth-Ruth and Tutz Gerhard (2015). A uniform framework for combination of penalties in generalized structured models. Adv Data Anal Classif. doi: 10.1007/s11634-015-0205-y

Examples

## Not run: ## Simulated fusion Lasso example.
bmu <- c(0,0,0,2,2,2,4,4,4)
bsigma <- c(0,0,0,-2,-2,-2,-1,-1,-1)
id <- factor(sort(rep(1:length(bmu), length.out = 300)))

## Response.
set.seed(123)
y <- bmu[id] + rnorm(length(id), sd = exp(bsigma[id]))

## Estimate model:
## fuse=1 -> nominal fusion,
## fuse=2 -> ordinal fusion,
## first, unpenalized model to be used for adaptive fusion weights.
f <- list(y ~ la(id,fuse=2,fx=TRUE), sigma ~ la(id,fuse=1,fx=TRUE))
b0 <- bamlss(f, sampler = FALSE)

## Model with single lambda parameter.
f <- list(y ~ la(id,fuse=2), sigma ~ la(id,fuse=1))
b1 <- bamlss(f, sampler = FALSE, optimizer = opt_lasso,
  criterion = "BIC", zeromodel = b0)

## Plot information criterion and coefficient paths.
lasso_plot(b1, which = 1)
lasso_plot(b1, which = 2)
lasso_plot(b1, which = 2, model = "mu", name = "mu.s.la(id).id")
lasso_plot(b1, which = 2, model = "sigma", name = "sigma.s.la(id).id")

## Extract coefficients for optimum Lasso parameter.
coef(b1, mstop = lasso_stop(b1))

## Predict with optimum Lasso parameter.
p1 <- predict(b1, mstop = lasso_stop(b1))

## Full MCMC, needs lasso_transform() to assign the
## adaptive weights from unpenalized model b0.
b2 <- bamlss(f, optimizer = FALSE, transform = lasso_transform,
  zeromodel = b0, nobs = length(y), start = coef(b1, mstop = lasso_stop(b1)),
  n.iter = 4000, burnin = 1000)
summary(b2)
plot(b2)

ci <- confint(b2, model = "mu", pterms = FALSE, sterms = TRUE)
lasso_plot(b1, which = 2, model = "mu", name = "mu.s.la(id).id", spar = FALSE)
for(i in 1:8) {
  abline(h = ci[i, 1], lty = 2, col = "red")
  abline(h = ci[i, 2], lty = 2, col = "red")
}

## End(Not run)

bamlss

Bayesian Additive Models for Location, Scale, and Shape (and Beyond)

v1.1-3

GPL-2 | GPL-3

Authors

Nikolaus Umlauf [aut, cre] (<https://orcid.org/0000-0003-2160-9803>), Nadja Klein [aut] (<https://orcid.org/0000-0002-5072-5347>), Achim Zeileis [aut] (<https://orcid.org/0000-0003-0918-3766>), Meike Koehler [ctb], Thorsten Simon [aut] (<https://orcid.org/0000-0002-3778-7738>), Stanislaus Stadlmann [ctb]

Initial release

2021-01-25