View source: R/do.bootstrap.cress.robust.r

do.bootstrap.cress.robust | R Documentation |

This fuction performs a specified number of bootstrapping iterations using CReSS/SALSA for fitting the count model. See below for details.

```
do.bootstrap.cress.robust(
model.obj,
predictionGrid,
splineParams = NULL,
g2k = NULL,
B,
robust = T,
name = NULL,
seed = 12345,
nCores = 1,
cat.message = TRUE
)
```

`model.obj` |
The best fitting |

`predictionGrid` |
The prediction grid data |

`splineParams` |
The object describing the parameters for fitting the one and two dimensional splines |

`g2k` |
(N x k) matrix of distances between all prediction points (N) and all knot points (k) |

`B` |
Number of bootstrap iterations |

`name` |
Analysis name. Required to avoid overwriting previous bootstrap results. This name is added at the beginning of "predictionboot.RData" when saving bootstrap predictions. |

`seed` |
Set the seed for the bootstrap sampling process. |

`nCores` |
Set the number of computer cores for the bootstrap process to use (default = 1). The more cores the faster the proces but be wary of over using the cores on your computer. If |

`rename` |
A vector of column names for which a new column needs to be created for the bootstrapped data. This defaults to |

The following steps are performed for each iteration:

coefficients are resampled from a multivariate normal distribution defined by MLE and COV from the best fitting count model

predictions are made to the prediction data using the resampled coefficients

The function returns a matrix of bootstrap predictions. The number of rows is equal to the number of rows in predictionGrid. The number of columns is equal to `B`

. The matrix may be very large and so is stored directly into the working directory as a workspace object: '"name"predictionboot.RObj'. The object inside is called `bootPreds`

.

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