Description Usage Arguments Value Examples

monte-carlo integration of prediction functions

1 2 3 4 |

`data` |
a |

`vars` |
a character vector corresponding to a strict subset of the columns in |

`n` |
an integer vector of length two giving the resolution of the uniform or random grid on |

`model` |
an object which can be passed to |

`uniform` |
logical indicating whether to create the grid on |

`points` |
a named list which gives specific points for |

`int.points` |
a integer vector giving indices of the points in |

`aggregate.fun` |
what function to aggregate the predictions with. this function takes a single argument |

`predict.fun` |
what function to generate predictions using |

`weight.fun` |
a function to construct weights for |

a `data.table`

with columns for predictions and `vars`

.

1 2 3 4 5 6 7 | ```
X = replicate(3, rnorm(100))
y = X %*% runif(3)
data = data.frame(X, y)
fit = lm(y ~ ., data)
marginalPrediction(data.frame(X), "X2", c(10, 25), fit,
aggregate.fun = function(x) c("mean" = mean(x), "variance" = var(x)))
``` |

mmpf documentation built on Oct. 24, 2018, 9:04 a.m.

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