R: Fit a robust regression model with Huber or quantile loss...

hqreg {hqreg}

R Documentation

Fit a robust regression model with Huber or quantile loss penalized by lasso or elasti-net

Description

Fit solution paths for Huber loss regression or quantile regression penalized by lasso or elastic-net over a grid of values for the regularization parameter lambda.

Usage

hqreg(X, y, method = c("huber", "quantile", "ls"),
    gamma = IQR(y)/10, tau = 0.5, alpha = 1, nlambda = 100, lambda.min = 0.05, lambda, 
    preprocess = c("standardize", "rescale"), screen = c("ASR", "SR", "none"), 
    max.iter = 10000, eps = 1e-7, dfmax = ncol(X)+1, penalty.factor = rep(1, ncol(X)), 
    message = FALSE)

Arguments

`X`	Input matrix.
`y`	Response vector.
`method`	The loss function to be used in the model. Either "huber" (default), "quantile", or "ls" for least squares (see `Details`).
`gamma`	The tuning parameter of Huber loss, with no effect for the other loss functions. Huber loss is quadratic for absolute values less than gamma and linear for those greater than gamma. The default value is IQR(y)/10.
`tau`	The tuning parameter of the quantile loss, with no effect for the other loss functions. It represents the conditional quantile of the response to be estimated, so must be a number between 0 and 1. It includes the absolute loss when tau = 0.5 (default).
`alpha`	The elastic-net mixing parameter that controls the relative contribution from the lasso and the ridge penalty. It must be a number between 0 and 1. `alpha=1` is the lasso penalty and `alpha=0` the ridge penalty.
`nlambda`	The number of lambda values. Default is 100.
`lambda.min`	The smallest value for lambda, as a fraction of lambda.max, the data derived entry value. Default is 0.05.
`lambda`	A user-specified sequence of lambda values. Typical usage is to leave blank and have the program automatically compute a `lambda` sequence based on `nlambda` and `lambda.min`. Specifying `lambda` overrides this. This argument should be used with care and supplied with a decreasing sequence instead of a single value. To get coefficients for a single `lambda`, use `coef` or `predict` instead after fitting the solution path with `hqreg` or performing k-fold CV with `cv.hqreg`.
`preprocess`	Preprocessing technique to be applied to the input. Either "standardize" (default) or "rescale"(see `Details`). The coefficients are always returned on the original scale.
`screen`	Screening rule to be applied at each `lambda` that discards variables for speed. Either "ASR" (default), "SR" or "none". "SR" stands for the strong rule, and "ASR" for the adaptive strong rule. Using "ASR" typically requires fewer iterations to converge than "SR", but the computing time are generally close. Note that the option "none" is used mainly for debugging, which may lead to much longer computing time.
`max.iter`	Maximum number of iterations. Default is 10000.
`eps`	Convergence threshold. The algorithms continue until the maximum change in the objective after any coefficient update is less than `eps` times the null deviance. Default is `1E-7`.
`dfmax`	Upper bound for the number of nonzero coefficients. The algorithm exits and returns a partial path if `dfmax` is reached. Useful for very large dimensions.
`penalty.factor`	A numeric vector of length equal to the number of variables. Each component multiplies `lambda` to allow differential penalization. Can be 0 for some variables, in which case the variable is always in the model without penalization. Default is 1 for all variables.
`message`	If set to TRUE, hqreg will inform the user of its progress. This argument is kept for debugging. Default is FALSE.

Details

The sequence of models indexed by the regularization parameter lambda is fit using a semismooth Newton coordinate descent algorithm. The objective function is defined to be

\frac{1}{n} \sum loss_i + \lambda\textrm{penalty}.

For method = "huber",

loss(t) = \frac{t^2}{2\gamma} I(|t|\le \gamma) + (|t| - \frac{\gamma}{2};) I(|t|> \gamma)

for method = "quantile",

loss(t) = t (\tau - I(t<0));

for method = "ls",

loss(t) = \frac{t^2}{2}

In the model, "t" is replaced by residuals.

The program supports different types of preprocessing techniques. They are applied to each column of the input matrix X. Let x be a column of X. For preprocess = "standardize", the formula is

x' = \frac{x-mean(x)}{sd(x)};

for preprocess = "rescale",

x' = \frac{x-min(x)}{max(x)-min(x)}.

The models are fit with preprocessed input, then the coefficients are transformed back to the original scale via some algebra. To fit a model for raw data with no preprocessing, use hqreg_raw.

Value

The function returns an object of S3 class "hqreg", which is a list containing:

`call`	The call that produced this object.
`beta`	The fitted matrix of coefficients. The number of rows is equal to the number of coefficients, and the number of columns is equal to `nlambda`. An intercept is included.
`iter`	A vector of length `nlambda` containing the number of iterations until convergence at each value of `lambda`.
`saturated`	A logical flag for whether the number of nonzero coefficients has reached `dfmax`.
`lambda`	The sequence of regularization parameter values in the path.
`alpha`	Same as above.
`gamma`	Same as above. `NULL` except when `method = "huber"`.
`tau`	Same as above. `NULL` except when `method = "quantile"`.
`penalty.factor`	Same as above.
`method`	Same as above.
`nv`	The variable screening rules are accompanied with checks of optimality conditions. When violations occur, the program adds in violating variables and re-runs the inner loop until convergence. `nv` is the number of violations.

Author(s)

Congrui Yi <congrui-yi@uiowa.edu>

References

Yi, C. and Huang, J. (2016) Semismooth Newton Coordinate Descent Algorithm for Elastic-Net Penalized Huber Loss Regression and Quantile Regression, https://arxiv.org/abs/1509.02957
Journal of Computational and Graphical Statistics, accepted in Nov 2016
http://www.tandfonline.com/doi/full/10.1080/10618600.2016.1256816

Examples

X = matrix(rnorm(1000*100), 1000, 100)
beta = rnorm(10)
eps = 4*rnorm(1000)
y = drop(X[,1:10] %*% beta + eps)

# Huber loss
fit1 = hqreg(X, y)
coef(fit1, 0.01)
predict(fit1, X[1:5,], lambda = c(0.02, 0.01))

# Quantile loss
fit2 = hqreg(X, y, method = "quantile", tau = 0.2)
plot(fit2)

# Squared loss
fit3 = hqreg(X, y, method = "ls", preprocess = "rescale")
plot(fit3, xvar = "norm")

[Package hqreg version 1.4 Index]