Description Usage Arguments Value References
Train a penalized mixture of experts model by IPOPT.
1 2 3 4 5 6 7 8 9 10 11 12 13  pmoe(X, ...)
## S3 method for class 'formula'
pmoe(formula, data, subset, na.action, ...)
## Default S3 method:
pmoe(X, y, colsGating = 1:ncol(X),
colsExperts = 1:ncol(X), interceptGating = TRUE,
interceptExperts = TRUE, offsetGating = NULL, offsetExperts = NULL,
J = 2, lambda, alpha = 1, penalty = c("ungrouped", "grouped"),
type.multinomial = c("ungrouped", "grouped"), model = c("binomial",
"multinomial"), standardize = FALSE, genetic = FALSE,
ipopt.max.iter = 500, ipopt.tol = 1e06, ...)

X 
(Required if no 
formula 
A 
data 
A 
subset 
A subset... 
na.action 
... 
y 
(Required if no 
colsGating 
Names or indices of columns in 
colsExperts 
Names or indices of columns in 
interceptGating 
Logical. Does the gating model include an intercept? If 
interceptExperts 
Logical. Does the expert model include an intercept? If 
offsetGating 
Offset term for the gating model. 
offsetExperts 
Offset term for the expert model. 
J 
The number of experts / mixture components. Defaults to 2. 
lambda 
Penalty parameter. Can be a scalar or a vector of length 
alpha 
Mixing parameter for the elastic net penalty. Can be a scalar or a vector of length 
penalty 
... 
type.multinomial 

model 
... 
standardize 
Logical. Should the columns of 
genetic 
Logical. 
ipopt.max.iter 
The maximum number of IPOPT iterations. 
ipopt.tol 
Tolerance for IPOPT convergence. 
... 
Further arguments. 
An object of class pmoe
.
Waechter, A. and Biegler, L. T. (2006), On the Implementation of an InteriorPoint Filter LineSearch Algorithm for LargeScale Nonlinear Programming, Mathematical Programming, 106, 2557.
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