quantileFitLoc#

Purpose#

Perform local linear or quadratic quantile regression.

Format#

q = quantileFitLoc(y, x[, tau[, xstar]][, qCtl])#

q = quantileFitLoc(dataset, formula[, tau[, xstar]][, qCtl])

Parameters:

y (Nx1 vector) – dependent variable.
x (NxK matrix or sparse matrix or N-dimensional array) – independent variables
dataset (string) – name of dataset.
formula (string) –
formula string of the model.

E.g "y ~ X1 + X2", ‘y’ is the name of dependent variable, ‘X1’ and ‘X2’ are names of independent variables;

E.g "y ~ .", ‘.’ means including all variables except dependent variable ‘y’;

E.g "y ~ -1 + X1 + X2", ‘-1’ means no intercept model.
tau (Mx1 vector) – Optional argument, quantile levels. Default = { 0.05, 0.5, 0.95 };
xstar (P*1 vector) – Optional argument, quantile points. Default = seqa(0, 1/(50-1), 50).

qCtl (struct) –

Optional argument. instance of the qfitControl structure containing the following members:

qCtl.bandwidth	scalar, the multiplicative factor of the bandwidth. Default = 1.
qCtl.reg_type	scalar, the regression type. Default = 1. 1: Linear regression. 2: Quadratic regression.
qCtl.varnames	string array, variable names. Default = `{"X1", "X2", ..., "XK"}`.
qCtl.verbose	scalar, print results Default = 1. 1: Printing on. 0: No printing.
qCtl.const	scalar, include constant in regression. Default = 1. 1: a constant term will be added. 0: no constant term will be added.

Returns:

q (PxM matrix) – estimated quantile \(Y|X=xstar\)

Examples#

new;
cls;

// Set random number generator seed for
// repeatable random numbers
rndseed 4893;

N = 1000;
X = rndu(N, 1);
Y = sin(9*X) + (rndu(N, 1) - 0.5);

// Call quantileFitLoc
q = quantileFitLoc(Y, X);

Source#

quantilefit.src