families: Families for GAMLSS models
In gamboostLSS: Boosting Methods for GAMLSS

Description Usage Arguments Details Value Author(s) References See Also Examples

The package provides some pre-defined GAMLSS families, e.g. NBionomialLSS. Objects of the class families provide a convenient way to specify GAMLSS distributions to be fitted by one of the boosting algorithms implemented in this package. By using the function Families, a new object of the class families can be generated.

############################################################
# Families for continuous response

# Gaussian distribution
GaussianLSS(mu = NULL, sigma = NULL,
            stabilization = c("none", "MAD"))

# Student's t-distribution
StudentTLSS(mu = NULL, sigma = NULL, df = NULL,
            stabilization = c("none", "MAD"))

############################################################
# Families for continuous non-negative response

# Gamma distribution
GammaLSS(mu = NULL, sigma = NULL,
         stabilization = c("none", "MAD"))

############################################################
# Families for fractions and bounded continuous response

# Beta distribution
BetaLSS(mu = NULL, phi = NULL,
        stabilization = c("none", "MAD"))

############################################################
# Families for count data

# Negative binomial distribution
NBinomialLSS(mu = NULL, sigma = NULL,
             stabilization = c("none", "MAD"))

# Zero-inflated Poisson distribution
ZIPoLSS(mu = NULL, sigma = NULL,
        stabilization = c("none", "MAD"))

# Zero-inflated negative binomial distribution
ZINBLSS(mu = NULL, sigma = NULL, nu = NULL,
        stabilization = c("none", "MAD"))

############################################################
# Families for survival models (accelerated failure time
# models) for data with right censoring

# Log-normal distribution
LogNormalLSS(mu = NULL, sigma = NULL,
             stabilization = c("none", "MAD"))

# Log-logistic distribution
LogLogLSS(mu = NULL, sigma = NULL,
          stabilization = c("none", "MAD"))

# Weibull distribution
WeibullLSS(mu = NULL, sigma = NULL,
           stabilization = c("none", "MAD"))

############################################################
# Constructor function for new GAMLSS distributions
Families(..., qfun = NULL, name = NULL)

`...`	sub-families to be passed to constructor.
`qfun`	quantile function. This function can for example be used to compute (marginal) prediction intervals. See `predint`.
`name`	name of the families.
`mu`	offset value for mu.
`sigma`	offset value for sigma.
`phi`	offset value for phi.
`df`	offset value for df.
`nu`	offset value for nu.
`stabilization`	governs if the negative gradient should be standardized in each boosting step. It can be either "none" or "MAD". See also Details below.

The arguments of the families are the offsets for each distribution parameter. Offsets can be either scalar, a vector with length equal to the number of observations or NULL (default). In the latter case, a scalar offset for this component is computed by minimizing the risk function w.r.t. the corresponding distribution parameter (keeping the other parameters fixed).

Note that gamboostLSS is not restricted to three components but can handle an arbitrary number of components (which, of course, depends on the GAMLSS distribution). However, it is important that the names (for the offsets, in the sub-families etc.) are chosen consistently.

The ZIPoLSS families can be used to fit zero-inflated Poisson models. Here, mu and sigma refer to the location parameter of the Poisson component (with log link) and the mean of the zero-generating process (with logit link), respectively.

Similarly, ZINBLSS can be used to fit zero-inflated negative binomial models. Here, mu and sigma refer to the location and scale parameters (with log link) of the negative binomial component of the model. The zero-generating process (with logit link) is represented by nu.

The Families function can be used to implements a new GAMLSS distribution which can be used for fitting by mboostLSS. Thereby, the function builds a list of sub-families, one for each distribution parameter. The sub-families themselves are objects of the class boost_family, and can be constructed via the function Family of the mboost Package.

Arguments to be passed to Family: The loss for every distribution parameter (contained in objects of class boost_family) is the negative log-likelihood of the corresponding distribution. The ngradient is the negative partial derivative of the loss function with respect to the distribution parameter. For a two-parameter distribution (e.g. mu and sigma), the user therefore has to specify two sub-families with Family. The loss is basically the same function for both paramters, only ngradient differs. Both sub-families are passed to the Families constructor, which returns an object of the class families.

To (potentially) stabilize the model estimation by standardizing the negative gradients one can use the argument stabilization of the families. If stabilization = "MAD", the negative gradient is divided by its (weighted) median absolute deviation

median_i (|u_{k,i} - median_j(u_{k,j})|)

in each boosting step. See Hofner et. al (2014) for details.

An object of class families.

BetaLSS for boosting beta regression was implmented by Florian Wickler.

B. Hofner, A. Mayr, M. Schmid (2014). gamboostLSS: An R Package for Model Building and Variable Selection in the GAMLSS Framework. Technical Report, arXiv:1407.1774.

Mayr, A., Fenske, N., Hofner, B., Kneib, T. and Schmid, M. (2012): Generalized additive models for location, scale and shape for high-dimensional data - a flexible approach based on boosting. Journal of the Royal Statistical Society, Series C (Applied Statistics) 61(3): 403-427.

Rigby, R. A. and D. M. Stasinopoulos (2005). Generalized additive models for location, scale and shape (with discussion). Journal of the Royal Statistical Society, Series C (Applied Statistics), 54, 507-554.

as.families for applying GAMLSS distributions provided in the framework of the gamlss package.

The functions gamboostLSS and glmboostLSS can be used for model fitting.

See also the corresponding constructor function Family in mboost.

## Example to define a new distribution:
## Students t-distribution with two parameters, df and mu:

## sub-Family for mu
## -> generate object of the class family from the package mboost
newStudentTMu  <- function(mu, df){

    # loss is negative log-Likelihood, f is the parameter to be fitted with
    # id link -> f = mu
    loss <- function(df,  y, f) {
        -1 * (lgamma((df + 1)/2)  - lgamma(1/2) -
              lgamma(df/2) - 0.5 * log(df) -
              (df + 1)/2 * log(1 + (y - f)^2/(df )))
    }
    # risk is sum of loss
    risk <- function(y, f, w = 1) {
        sum(w * loss(y = y, f = f, df = df))
    }
    # ngradient is the negative derivate w.r.t. mu
    ngradient <- function(y, f, w = 1) {
        (df + 1) * (y - f)/(df  + (y - f)^2)
    }

    # use the Family constructor of mboost
    Family(ngradient = ngradient, risk = risk, loss = loss,
           response = function(f) f,
           name = "new Student's t-distribution: mu (id link)")
}

## sub-Family for df
newStudentTDf <- function(mu, df){

    # loss is negative log-Likelihood, f is the parameter to be fitted with
    # log-link: exp(f) = df
    loss <- function( mu, y, f) {
        -1 * (lgamma((exp(f) + 1)/2)  - lgamma(1/2) -
              lgamma(exp(f)/2) - 0.5 * f -
              (exp(f) + 1)/2 * log(1 + (y - mu)^2/(exp(f) )))
    }
    # risk is sum of loss
    risk <- function(y, f, w = 1) {
        sum(w * loss(y = y, f = f,  mu = mu))
    }
    # ngradient is the negative derivate w.r.t. df
    ngradient <- function(y, f, w = 1) {
        exp(f)/2 * (digamma((exp(f) + 1)/2) - digamma(exp(f)/2)) -
            0.5 - (exp(f)/2 * log(1 + (y - mu)^2 / (exp(f) )) -
                   (y - mu)^2 / (1 + (y - mu)^2 / exp(f)) * (exp(-f) + 1)/2)
    }
    # use the Family constructor of mboost
    Family(ngradient = ngradient, risk = risk, loss = loss,
           response = function(f) exp(f),
           name = "Student's t-distribution: df (log link)")
}

## families object for new distribution
newStudentT <- Families(mu= newStudentTMu(mu=mu, df=df),
                        df=newStudentTDf(mu=mu, df=df))

### Do not test the following code per default on CRAN as it takes some time to run:
### usage of the new Student's t distribution:
library(gamlss)   ## required for rTF
set.seed(1907)
n <- 5000
x1  <- runif(n)
x2 <- runif(n)
mu <- 2 -1*x1 - 3*x2
df <- exp(1 + 0.5*x1 )
y <- rTF(n = n, mu = mu, nu = df)

## model fitting
model <- glmboostLSS(y ~ x1 + x2, families = newStudentT,
                     control = boost_control(mstop = 100),
                     center = TRUE)
## shrinked effect estimates
coef(model, off2int = TRUE)

## compare to pre-defined three parametric t-distribution:
model2 <- glmboostLSS(y ~ x1 + x2, families = StudentTLSS(),
                      control = boost_control(mstop = 100),
                      center = TRUE)
coef(model2, off2int = TRUE)

## with effect on sigma:
sigma <- 3+ 1*x2
y <- rTF(n = n, mu = mu, nu = df, sigma=sigma)
model3 <- glmboostLSS(y ~ x1 + x2, families = StudentTLSS(),
                      control = boost_control(mstop = 100),
                      center = TRUE)
coef(model3, off2int = TRUE)