This package implements the quantile regression neural network (QRNN) (Taylor, 2000; Cannon, 2011; Cannon, 2018), which is a flexible nonlinear form of quantile regression. The goal of quantile regression is to estimate conditional quantiles of a response variable that depend on covariates in some form of regression equation. The QRNN adopts the multi-layer perceptron neural network architecture. The implementation follows from previous work on the estimation of censored regression quantiles, thus allowing predictions for mixed discrete-continuous variables like precipitation (Friederichs and Hense, 2007). A differentiable approximation to the quantile regression cost function is adopted so that a simplified form of the finite smoothing algorithm (Chen, 2007) can be used to estimate model parameters. This approximation can also be used to force the model to solve a standard least squares regression problem or an expectile regression problem (Cannon, 2018). Weight penalty regularization can be added to help avoid overfitting, and ensemble models with bootstrap aggregation are also provided.
An optional monotone constraint can be invoked, which guarantees monotonic
non-decreasing behaviour of model outputs with respect to specified covariates
(Zhang, 1999). The input-hidden layer weight matrix can also be constrained
so that model relationships are strictly additive (see gam.style;
Cannon, 2018). Borrowing strength by using a composite model for multiple
regression quantiles (Zou et al., 2008; Xu et al., 2017) is also possible
(see composite.stack). Weights can be applied to individual
cases (Jiang et al., 2012).
Applying the monotone constraint in combination with the composite model allows
one to simultaneously estimate multiple non-crossing quantiles (Cannon, 2018);
the resulting monotone composite QRNN (MCQRNN) is provided by the
mcqrnn.fit and mcqrnn.predict wrapper functions.
Examples for qrnn.fit and qrnn2.fit show how the
same functionality can be achieved using the low level composite.stack
and fitting functions.
QRNN models with a single layer of hidden nodes can be fitted using the
qrnn.fit function. Predictions from a fitted model are made using
the qrnn.predict function. Note: a single hidden layer
is usually sufficient for most modelling tasks. With added monotonicity
constraints, a second hidden layer may sometimes be beneficial
(Lang, 2005; Minin et al., 2010). QRNN models with two hidden layers are
available using the qrnn2.fit and
qrnn2.predict functions. For non-crossing quantiles, the
mcqrnn.fit and mcqrnn.predict wrappers also allow
models with one or two hidden layers to be fitted and predictions to be made
from the fitted models.
In general, mcqrnn.fit offers a convenient, single function for
fitting multiple quantiles simultaneously. Note, however, that default
settings in mcqrnn.fit and other model fitting functions are
not optimized for general speed, memory efficiency, or accuracy and should be
adjusted for a particular regression problem as needed. In particular, the
approximation to the quantile regression cost function eps.seq, the
number of trials n.trials, and number of iterations iter.max
can all influence fitting speed (and accuracy), as can changing the
optimization algorithm via method. Non-crossing quantiles are
implemented by stacking multiple copies of the x and y data,
one copy per value of tau. Depending on the dataset size, this can
lead to large matrices being passed to the optimization routine. In the
adam method, the minibatch size can be adjusted to help
offset this cost. Model complexity is determined via the number of hidden
nodes, n.hidden and n.hidden2, as well as the optional weight
penalty penalty; values of these hyperparameters are crucial to
obtaining a well performing model.
If models for multiple quantiles have been fitted, for example by
mcqrnn.fit or multiple calls to either qrnn.fit
or qrnn2.fit, the (experimental) dquantile
function and its companion functions are available to create proper
probability density, distribution, and quantile functions
(Quiñonero-Candela et al., 2006; Cannon, 2011). These can be useful for
assessing probabilistic calibration and evaluating model performance.
Finally, the function gam.style can be used to visualize and
investigate fitted covariate/response relationships from qrnn.fit
(Plate et al., 2000).
| Package: | qrnn |
| Type: | Package |
| License: | GPL-2 |
| LazyLoad: | yes |
Cannon, A.J., 2011. Quantile regression neural networks: implementation in R and application to precipitation downscaling. Computers & Geosciences, 37: 1277-1284. doi:10.1016/j.cageo.2010.07.005
Cannon, A.J., 2018. Non-crossing nonlinear regression quantiles by monotone composite quantile regression neural network, with application to rainfall extremes. Stochastic Environmental Research and Risk Assessment, 32(11): 3207-3225. doi:10.1007/s00477-018-1573-6
Chen, C., 2007. A finite smoothing algorithm for quantile regression. Journal of Computational and Graphical Statistics, 16: 136-164.
Friederichs, P. and A. Hense, 2007. Statistical downscaling of extreme precipitation events using censored quantile regression. Monthly Weather Review, 135: 2365-2378.
Jiang, X., J. Jiang, and X. Song, 2012. Oracle model selection for nonlinear models based on weighted composite quantile regression. Statistica Sinica, 22(4): 1479-1506.
Lang, B., 2005. Monotonic multi-layer perceptron networks as universal approximators. International Conference on Artificial Neural Networks, Artificial Neural Networks: Formal Models and Their Applications-ICANN 2005, pp. 31-37.
Minin, A., M. Velikova, B. Lang, and H. Daniels, 2010. Comparison of universal approximators incorporating partial monotonicity by structure. Neural Networks, 23(4): 471-475.
Plate, T., J. Bert, J. Grace, and P. Band, 2000. Visualizing the function computed by a feedforward neural network. Neural Computation, 12(6): 1337-1354.
Quiñonero-Candela, J., C. Rasmussen, F. Sinz, O. Bousquet, B. Scholkopf, 2006. Evaluating Predictive Uncertainty Challenge. Lecture Notes in Artificial Intelligence, 3944: 1-27.
Taylor, J.W., 2000. A quantile regression neural network approach to estimating the conditional density of multiperiod returns. Journal of Forecasting, 19(4): 299-311.
Xu, Q., K. Deng, C. Jiang, F. Sun, and X. Huang, 2017. Composite quantile regression neural network with applications. Expert Systems with Applications, 76, 129-139.
Zhang, H. and Zhang, Z., 1999. Feedforward networks with monotone constraints. In: International Joint Conference on Neural Networks, vol. 3, p. 1820-1823. doi:10.1109/IJCNN.1999.832655
Zou, H. and M. Yuan, 2008. Composite quantile regression and the oracle model selection theory. The Annals of Statistics, 1108-1126.