Description Usage Arguments Value Author(s) See Also Examples

View source: R/thresholdIdentification.R

From an `estimateGraph`

object and a corresponding data set, candidate treshold values are compared on the prediction performance of the corresponding additive Kriging model. The candidate thresholds are chosen by the biggest jumps in `plotDeltaJumps`

together with 0 (the full model) and 1 (the complete additive model). For each of them the Kriging model with corresponding kernel is estimated and the leave-one-out
crossvalidiations on the original data sets are compared on scatterplots and RMSE-values.

1 | ```
thresholdIdentification(g, x, y, n.cand = 3, covtype = "matern5_2", KM = NULL)
``` |

`g` |
object of class |

`x` |
design matrix of input variables corresponding to |

`y` |
vector of output variables of the same length as the columns of |

`n.cand` |
integer, the |

`covtype` |
optional character string specifying the covariance structure to be used. The default is |

`KM` |
optional object of class |

a list including

`delta` |
vector of threshold candidates |

`models` |
list of full model and models with applied thresholds |

`y.cv` |
list of vectors containing crossvalidation predictions for each model |

`RMSE` |
vector of residual mean squared errors for each model |

J. Fruth, M. Jastrow

`plotDeltaJumps`

, `plotGraphChange`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 | ```
############ simple 3-dimensional example with one interaction
### data (usually existing)
x <- matrix(seq(0,1,,20), 20, 3)
x <- apply(x,2,sample)
y <- 2*(x[,1]-0.5) * (x[,2]-0.5) + 0.1*sin(10*x[,3])
### FANVOA graph (usually estimated from a meta model over the data)
g <- list(d=3,
tii = matrix(c(0.0140, 0.0008, 0.0002)),
V = 0.0222,
tii.scaled = matrix(c(0.6976, 0.0432, 0.0113))
)
class(g) <- "graphlist"
### plot complete graph
plot(g, plot.i1=FALSE)
### Compare candidate thresholds on prediction performance
set.seed(1)
comparison <- thresholdIdentification(g, x, y, n.cand = 1)
``` |

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