Description Usage Arguments Details Value Author(s) References See Also Examples

This function makes predictions from a specified covariate matrix for a fit of the class "flam" with user-specified tuning parameters.

1 2 |

`object` |
an object of the class "flam". |

`new.x` |
the covariate matrix for which to make predictions - the number of columns should match that of |

`lambda` |
the desired value for the tuning parameter lambda. This does not need to be a value in |

`alpha` |
the desired value for the tuning parameter alpha. This does not need to be a value in |

`...` |
additional arguments to be passed. These are ignored in this function. |

It is likely that `new.x[,i]`

contains values not contained in `object$x[,i]`

. Predictions for that particular case are taken to be a linear interpolation of the nearest neighboring values in `object$x[,i]`

, i.e., the closest smaller value and the closest larger value.

A vector containing the fitted y values for `new.x`

.

Ashley Petersen

Petersen, A., Witten, D., and Simon, N. (2014). Fused Lasso Additive Model. arXiv preprint arXiv:1409.5391.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
#See ?'flam-package' for a full example of how to use this package
#generate data
set.seed(1)
data <- sim.data(n = 100, scenario = 1, zerof = 0, noise = 1)
#fit model for a range of tuning parameters
flam.out <- flam(x = data$x, y = data$y)
#we can make predictions for a covariate matrix with new observations
#choose desired alpha and lambda
alpha <- flam.out$all.alpha[15]; lambda <- flam.out$all.lambda[15]
#new.x with 20 observations and the same number of features as flam.out$x
new.data <- sim.data(n = 20, scenario = 1, zerof = 0, noise = 1)
new.x <- new.data$x
#make predictions
y.hat <- predict(flam.out, new.x = new.x, lambda = lambda, alpha = alpha)
#which can be compared to the true y
plot(new.data$y, y.hat, xlab="y", ylab=expression(hat(y)))
abline(0,1,lty=2)
#can also make predictions for any alpha and lambda:
predict(flam.out, new.x = new.x, lambda = 2, alpha = 0.9)
``` |

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