best.lqr: Best Fit in Robust Linear Quantile Regression

In lqr: Robust Linear Quantile Regression

Best Fit in Robust Linear Quantile Regression

Description

It finds the best fit distribution in robust linear quantile regression model. It adjusts the Normal, Student's t, Laplace, Slash and Contaminated Normal models. It shows a summary table with the likelihood-based criterion, envelopes plots and the histogram of the residuals with fitted densities for all models. Estimates and full inference are provided for the best model.

Usage

best.lqr(formula,data = NULL,subset = NULL,
              p = 0.5, precision = 10^-6,
              criterion = "AIC")

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.
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).
subset	an optional string specifying a subset of observations to be used in the fitting process. Be aware of the use of double quotes in a proper way when necessary, e.g., in "(sex=='F')".
p	An unique quantile or a set of quantiles related to the quantile regression.
precision	The convergence maximum error permitted. By default is 10^-6.
criterion	Likelihood-based criterion to be used for choosen the best model. It could be AIC, BIC, HQ or loglik (log-likelihood). By default AIC criterion will be used.

Details

The best.fit() function finds the best model only for one quantile. For fitting a grid of quantiles lqr() might be used but the distribution must be provided.

Value

For the best model:

iter	number of iterations.
criteria	attained criteria value.
beta	fixed effects estimates.
sigma	scale parameter estimate for the error term.
nu	Estimate of nu parameter detailed above.
gamma	Estimate of gamma parameter detailed above.
SE	Standard Error estimates.
table	Table containing the inference for the fixed effects parameters.
loglik	Log-likelihood value.
AIC	Akaike information criterion.
BIC	Bayesian information criterion.
HQ	Hannan-Quinn information criterion.
fitted.values	vector containing the fitted values.
residuals	vector containing the residuals.

Author(s)

Christian E. Galarza <[e-mail]>, Luis Benites <[e-mail]> and Victor H. Lachos <[e-mail]>

Maintainer: Christian E. Galarza <[e-mail]>

References

Galarza, C., Lachos, V. H., Cabral, C. R. B., & Castro, C. L. (2017). Robust quantile regression using a generalized class of skewed distributions. Stat, 6(1), 113-130.

Wichitaksorn, N., Choy, S. T., & Gerlach, R. (2014). A generalized class of skew distributions and associated robust quantile regression models. Canadian Journal of Statistics, 42(4), 579-596.

See Also

lqr,Log.lqr,Log.best.lqr,dSKD

Examples

data(crabs,package = "MASS")

#Finding the best model for the 3rd quartile based on BIC
best.lqr(BD~FL,data = crabs, p = 0.75, criterion = "BIC")
 

lqr documentation built on Sept. 11, 2024, 7:23 p.m.