Evaluate quantiles from trained QRNN model

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Description

Evaluate a fitted QRNN model or ensemble of models, resulting in a list containing the predicted quantiles.

Usage

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qrnn.predict(x, parms)

Arguments

x

covariate matrix with number of rows equal to the number of samples and number of columns equal to the number of variables.

parms

list containing QRNN input-hidden and hidden-output layer weight matrices and other parameters from qrnn.fit.

Value

a list with number of elements equal to that of parms, each containing a column matrix of predicted quantiles.

See Also

qrnn.fit

Examples

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data(sinc)
x <- sinc$x
y <- sinc$y
y[y < -0.1] <- -0.1

set.seed(100)
parms <- qrnn.fit(x = x, y = y, n.hidden = 5, tau = 0.5, lower = -0.1,
                  iter.max = 500, n.trials = 1)
p <- qrnn.predict(x = x, parms = parms)

matplot(x, cbind(y, p), type=c("p", "l"), pch = 1, lwd = c(1, 2))