Description Usage Arguments Value See Also
Smooth approximation to the tilted absolute value cost function used to fit a QRNN model. Optional left censoring, monotone constraints, and additive constraints are supported.
1 2 
weights 
weight vector of length returned by 
x 
covariate matrix with number of rows equal to the number of samples and number of columns equal to the number of variables. 
y 
response column matrix with number of rows equal to the number of samples. 
n.hidden 
number of hidden nodes in the QRNN model. 
w 
vector of weights with length equal to the number of samples;

tau 
desired tauquantile. 
lower 
left censoring point. 
monotone 
column indices of covariates for which the monotonicity constraint should hold. 
additive 
force additive relationships. 
eps 
epsilon value used in the approximation functions. 
Th 
hidden layer transfer function; use 
Th.prime 
derivative of the hidden layer transfer function 
penalty 
weight penalty for weight decay regularization. 
unpenalized 
column indices of covariates for which the weight penalty should not be applied to inputhidden layer weights. 
numeric value indicating tilted absolute value cost function, along with attribute containing vector with gradient information.
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