The function creates a reclassification table and provides statistics.

1 | ```
reclassification(data, cOutcome, predrisk1, predrisk2, cutoff)
``` |

`data` |
Data frame or matrix that includes the outcome and predictors variables. |

`cOutcome` |
Column number of the outcome variable. |

`predrisk1` |
Vector of predicted risks of all individuals using initial model. |

`predrisk2` |
Vector of predicted risks of all individuals using updated model. |

`cutoff` |
Cutoff values for risk categories.
Define the cut-off values as |

The function creates a reclassification table and computes the
categorical and continuous net reclassification improvement (`NRI`

) and
integrated discrimination improvement (`IDI`

). A reclassification table
indicates the number of individuals who move to another risk category or remain
in the same risk category as a result of updating the risk model. Categorical `NRI`

equal to
`x%`

means that compared with individuals without outcome,
individuals with outcome were almost `x%`

more likely to move up a category than down.
The function also computes continuous `NRI`

, which does not require any discrete
risk categories and relies on the proportions of individuals with outcome
correctly assigned a higher probability and individuals without outcome
correctly assigned a lower probability by an updated model compared with the
initial model.
`IDI`

equal to `x%`

means that the difference in average
predicted risks between the individuals with and without the outcome
increased by `x%`

in the updated model.
The function requires predicted risks estimated by using two separate risk
models. Predicted risks can be obtained using the functions
`fitLogRegModel`

and `predRisk`

or be imported from other methods or packages.

The function returns the reclassification table, separately for individuals with and without the outcome of interest and the following measures:

`NRI (Categorical)` |
Categorical Net Reclassification Improvement with 95% CI and |

`NRI (Continuous)` |
Continuous Net Reclassification Improvement with 95% CI and |

`IDI` |
Integrated Discrimination Improvement with 95% CI and |

Cook NR. Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation 2007;115(7):928-935.

Pencina MJ, D'Agostino RB Sr, D'Agostino RB Jr, Vasan RS. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat Med 2008;27(2):157-172; discussion 207-212.

`plotDiscriminationBox`

, `predRisk`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 | ```
# specify dataset with outcome and predictor variables
data(ExampleData)
# specify column number of the outcome variable
cOutcome <- 2
# fit logistic regression models
# all steps needed to construct a logistic regression model are written in a function
# called 'ExampleModels', which is described on page 4-5
riskmodel1 <- ExampleModels()$riskModel1
riskmodel2 <- ExampleModels()$riskModel2
# obtain predicted risks
predRisk1 <- predRisk(riskmodel1)
predRisk2 <- predRisk(riskmodel2)
# specify cutoff values for risk categories
cutoff <- c(0,.10,.30,1)
# compute reclassification measures
reclassification(data=ExampleData, cOutcome=cOutcome,
predrisk1=predRisk1, predrisk2=predRisk2, cutoff)
``` |

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