predict.hqreg: Model predictions based on "hqreg" object.

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

Description

This function returns fitted values, coefficients and more from a fitted "hqreg" object.

Usage

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## S3 method for class 'hqreg'
predict(object, X, lambda, type = c("response","coefficients","nvars"), 
    exact = FALSE, ...)
## S3 method for class 'hqreg'
coef(object, lambda, exact = FALSE, ...)

Arguments

object

Fitted "hqreg" model object.

X

Matrix of values at which predictions are to be made. Used only for type = "response".

lambda

Values of the regularization parameter lambda at which predictions are requested. Default is the entire sequence used to create the model.

type

Type of prediction. "response" returns the fitted values; "coefficients" returns the coefficients; "nvars" returns the number of nonzero coefficients at each value of lambda.

exact

If exact=FALSE (default), then the function uses linear interpolation to make predictions for values of lambda that do not coincide with those used to fit the model. If exact=TRUE, and predictions are requested at values of lambda not included in the original fit, the model is refit on a lambda sequence consisting object$lambda and the new ones before predictions are made.

...

Not used. Other arguments to predict.

Value

The object returned depends on type.

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

See Also

hqreg

Examples

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X = matrix(rnorm(1000*100), 1000, 100)
beta = rnorm(10)
eps = 4*rnorm(1000)
y = drop(X[,1:10] %*% beta + eps) 
fit = hqreg(X, y, method = "quantile", tau = 0.7)
predict(fit, X[1:5,], lambda = c(0.05, 0.01))
predict(fit, X[1:5,], lambda = 0.05, exact = TRUE)
predict(fit, X[1:5,], lambda = 0.05, type = "nvars")
coef(fit, lambda = 0.05)


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