Description Usage Arguments Details Value Author(s) References Examples

Similar to other predict methods, this functions predicts fitted values and class labels from a fitted `KERE`

object.

1 2 |

`object` |
fitted |

`kern` |
the built-in kernel classes in KERE. Objects can be created by calling the rbfdot, polydot, tanhdot, vanilladot, anovadot, besseldot, laplacedot, splinedot functions etc. (see example.) |

`x` |
the original design matrix for training |

`newx` |
matrix of new values for |

`...` |
other parameters to |

The fitted *α_0 + K * α* at newx is returned as a size `nrow(newx)*length(lambda)`

matrix for various lambda values where the `KERE`

model was fitted.

The fitted *α_0 + K * α* is returned as a size `nrow(newx)*length(lambda)`

matrix. The row represents the index for observations of newx. The column represents the index for the lambda sequence.

Yi Yang, Teng Zhang and Hui Zou

Maintainer: Yi Yang <[email protected]>

Y. Yang, T. Zhang, and H. Zou. "Flexible Expectile Regression in Reproducing Kernel Hilbert Space." ArXiv e-prints: stat.ME/1508.05987, August 2015.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 | ```
# create data
N <- 100
X1 <- runif(N)
X2 <- 2*runif(N)
X3 <- 3*runif(N)
SNR <- 10 # signal-to-noise ratio
Y <- X1**1.5 + 2 * (X2**.5) + X1*X3
sigma <- sqrt(var(Y)/SNR)
Y <- Y + X2*rnorm(N,0,sigma)
X <- cbind(X1,X2,X3)
# set gaussian kernel
kern <- rbfdot(sigma=0.1)
# define lambda sequence
lambda <- exp(seq(log(0.5),log(0.01),len=10))
# run KERE
m1 <- KERE(x=X, y=Y, kern=kern, lambda = lambda, omega = 0.5)
# create newx for prediction
N1 <- 5
X1 <- runif(N1)
X2 <- 2*runif(N1)
X3 <- 3*runif(N1)
newx <- cbind(X1,X2,X3)
# make prediction
p1 <- predict.KERE(m1, kern, X, newx)
p1
``` |

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