hyperblm() R function from [GeneralizedHyperbolic]

Fitting Linear Models with Hyperbolic Errors

Fits linear models with hyperbolic errors. Can be used to carry out linear regression for data exhibiting heavy tails and skewness. Displays the histogram, log-histogram (both with fitted error distribution), Q-Q plot and residuals vs. fitted values plot for the fitted linear model.


hyperblm(formula, data, subset, weights, na.action,
         xx = FALSE, y = FALSE, contrasts = NULL,
         offset, method = "Nelder-Mead",
         startMethod = "Nelder-Mead", startStarts = "BN",
         paramStart = NULL,
         maxiter = 100, tolerance = 0.0001,
         controlBFGS = list(maxit = 1000),
         controlNM = list(maxit = 10000),
         maxitNLM = 10000,
         controlCO = list(), silent = TRUE, ...)

## S3 method for class 'hyperblm'
print(x, digits = max(3, getOption("digits")-3), ...)

## S3 method for class 'hyperblm'
coef(object, ...)

## S3 method for class 'hyperblm'
plot(x, breaks = "FD",
                        plotTitles = c("Residuals vs Fitted Values",
                                       "Histogram of residuals",
                                       "Log-Histogram of residuals",
                                       "Q-Q Plot"),
                        ...)

Arguments

formula: an object of class "formula" (or one that can be coerced to that class): a symbolic description of the model to be fitted. The details of model specification are given under Details .
data: an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which lm is called.
subset: an optional vector specifying a subset of observations to be used in the fitting process.
weights: an optional vector of weights to be used in the fitting process. Should be NULL or a numeric vector. If non-NULL, weighted least squares is used with weights weights (that is, minimizing sum(w*e^2)); otherwise ordinary least squares is used. See also Details ,
na.action: A function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.fail if that is unset. The factory-fresh

default is na.omit. Another possible value is NULL, no action. Value na.exclude can be useful.
xx, y: Logicals. If TRUE, the corresponding components of the fit (the explanatory matrix and the response vector) are returned.
contrasts: An optional list. See the contrasts.arg

of model.matrix.default.
offset: An optional vector. See Details .
method: Character. Possible values are "BFGS", "Nelder-Mead" and "nlm". See Details .
startMethod: Character. Possible values are "BFGS" and "Nelder-Mead". See Details .
startStarts: Character. Possible values are "BN", "FN", "SL", "US" and "MoM". See Details .
paramStart: An optional vector. A vector of parameter start values for the optimization routine. See Details .
maxiter: Numeric. The maximum number of two-stage optimization alternating iterations. See Details .
tolerance: Numeric. The two-stage optimization convergence ratio. See Details .
controlBFGS, controlNM: Lists. Lists of control parameters for optim when using corresponding (BFGS, Nelder-Mead) optimisation method in first stage. See optim.
maxitNLM: Numeric. The maximum number of iterations for the NLM optimizer.
controlCO: List. A list of control parameters for constrOptim in second stage.
silent: Logical. If TRUE, the error messgae of optimizer will not be displayed.
x: An object of class "hyperblm".
object: An object of class "hyperblm".
breaks: May be a vector, a single number or a character string. See hist.
plotTitles: Titles to appear above the plots.
digits: Numeric. Desired number of digits when the object is printed.
...: Passes additional arguments to function hyperbFitStand, optim and constrOptim.

Details

Models for hyperblm are specified symbolically. A typical model has the form response ~ terms where response is the (numeric) response vector and terms is a series of terms which specifies a linear predictor for response. A terms specification of the form first + second indicates all the terms in first together with all the terms in second

with duplicates removed. A specification of the form first:second indicates the set of terms obtained by taking the interactions of all terms in first with all terms in second. The specification first*second indicates the cross of first and second. This is the same as first + second + first:second.

If the formula includes an offset, this is evaluated and subtracted from the response.

If response is a matrix a linear model is fitted separately by least-squares to each column of the matrix.

See model.matrix for some further details. The terms in the formula will be re-ordered so that main effects come first, followed by the interactions, all second-order, all third-order and so on.

A formula has an implied intercept term. To remove this use either y ~ x - 1 or y ~ 0 + x. See formula for more details of allowed formulae.

Non-NULL weights can be used to indicate that different observations have different variances (with the values in weights being inversely proportional to the variances); or equivalently, when the elements of weights are positive integers $w_i$ , that each response $y_i$ is the mean of $w_i$ unit-weight observations (including the case that there are $w_i$ observations equal to $y_i$ and the data have been summarized).

hyperblm calls the lower level function hyperblmFit for the actual numerical computations.

All of weights, subset and offset are evaluated in the same way as variables in formula, that is first in data and then in the environment of formula.

hyperblmFit uses a two-stage alternating optimization routine. The quality of parameter start values (especially the error distribution parameters) is crucial to the routine's convergence. The user can specify the start values via the paramStart argument, otherwise the function finds reliable start values by calling the hyperbFitStand function.

startMethod in the argument list is the optimization method for function hyperbFitStandStart which finds the start values for function hyperbFitStand. It is set to "Nelder-Mead" by default due to the robustness of this optimizer. The "BFGS" method is also implemented as it is relatively fast to converge. Since "BFGS" method is a quasi-Newton method it will not as robust and for some data will not achieve convergence.

startStarts is the method used to find the start values for function hyperbFitStandStart which includes:

"BN": A method from Barndorff-Nielsen (1977) based on estimates of $psi$ and $gamma$ the absolute slopes of the left and right asymptotes to the log density function
"FN": Based on a fitted normal distribution as it is a limit of the hyperbolic distribution
"SL": Based on a fitted skew-Laplace distribution for which the log density has the form of two straight line with absolute slopes $1/alpha$ , $1/beta$
"MoM": A method of moment approach
"US": User specified

method is the method used in stage one of the two-stage alternating optimization routine. As the startMethod, it is set to "Nelder-Mead" by default. Besides "BFGS","nlm"

is also implemented as a alternative. Since BFGS method is a quasi-Newton method it will not as robust and for some data will not achieve convergence.

If the maximum of the ratio the change of the individual coefficients is smaller than tolerance then the routine assumes convergence, otherwise if the alternating iteration number exceeds maxiter

with the maximum of the ratio the change of the individual coefficients larger than tolerance, the routine is considered not to have converged.

Returns

hyperblm returns an object of class "hyperblm" which is a list containing:

coefficients: A named vector of regression coefficients.
distributionParams: A named vector of fitted hyperbolic error distribution parameters.
fitted.values: The fitted values from the model.
residuals: The remainder after subtracting fitted values from response.
mle: The maximum likelihood value of the model.
method: The optimization method for stage one.
paramStart: The start values of parameters that the user specified (only where relevant).
residsParamStart: The start values of parameters obtained by hyperbFitStand (only where relevant).
call: The matched call.
terms: The terms object used.
contrasts: The contrasts used (only where relevant).
xlevels: The levels of the factors used in the fitting (only where relevant).
offset: The offset used (only where relevant)
xNames: The names of each explanatory variables. If explanatory variables don't have names then they will be named x.
yVec: The response vector.
xMatrix: The explanatory variables matrix.
iterations: Number of two-stage alternating iterations to convergence.
convergence: The convergence code for two stage optimization: 0 is the system converged, 1 is first stage does not converge, 2 is second stage does not converge, 3 is the both stages do not converge.
breaks: The cell boundaries found by a call the hist.

References

Barndorff-Nielsen, O. (1977) Exponentially decreasing distributions for the logarithm of particle size, Proc. Roy. Soc. Lond., A353 , 401--419.

Prause, K. (1999). The generalized hyperbolic models: Estimation, financial derivatives and risk measurement. PhD Thesis, Mathematics Faculty, University of Freiburg.

Trendall, Richard (2005). hypReg: A Function for Fitting a Linear Regression Model in R with Hyperbolic Error. Masters Thesis, Statistics Faculty, University of Auckland.

Paolella, Marc S. (2007). Intermediate Probability: A Computational Approach. pp. 415 -Chichester: Wiley.

Scott, David J. and , Diethelm and Chalabi, Yohan, (2011). Fitting the Hyperbolic Distribution with R: A Case Study of Optimization Techniques. In preparation.

Stryhn, H. and Christensen, J. (2003). Confidence intervals by the profile likelihood method, with applications in veterinary epidemiology. ISVEE X.

Author(s)

David Scott d.scott@auckland.ac.nz , Xinxing Li xli053@aucklanduni.ac.nz

Examples


### stackloss data example
## Not run:

 airflow <- stackloss[, 1]
 temperature <- stackloss[, 2]
 acid <- stackloss[, 3]
 stack <- stackloss[, 4]

 hyperblm.fit <- hyperblm(stack ~ airflow + temperature + acid)

 coef.hyperblm(hyperblm.fit)
 plot.hyperblm(hyperblm.fit, breaks = 20)
 summary.hyperblm(hyperblm.fit, hessian = FALSE)
## End(Not run)

GeneralizedHyperbolic package Read PDF manual

Maintainer: David Scott
License: GPL (>= 2)
Last published: 2025-03-29
https://r-forge.r-project.org/projects/rmetrics/

Downloads (last 30 days):

hyperblm function