Description Usage Arguments Details Value Examples

Estimates of prediction model performance

1 2 |

`observed` |
Number of observations in the data set. |

`LP` |
Calculated linear predictor for all indiviuals in the data set based on the prediction model being validated. |

`plots` |
Logical. If TRUE, calibration plots are returned. |

`title` |
Optional title string for calibration plots. |

`weight` |
Optional weights for weighted analyses. |

This function calculates simple measures of prediction model performance, currently Harrell's c-index, O:E ratios and optional calibration plots.

This function is currently only compatible with logistic regression models.

`appraisal`

returns a matrix of performance estimates and
optional calibration plot

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## Example 1: validation of a simple prediction rule. Unweighted analysis
covmat <- matrix(c(0.2,0,0,0.2), nrow=2)
set.seed(123)
d <- datafy(obs = 100, means = c(0,0), covmat = covmat,
var.names = c("X1", "X2", "Y"), genmod = c(-1.5, 1, 1))
m <- glm(d$Y ~ d$X1 + d$X2, family=binomial)
set.seed(456)
covmat <- matrix(c(0.4,0,0,0.4), nrow=2)
v <- datafy(obs = 100, means = c(0.0,0.0), covmat = covmat,
var.names = c("X1", "X2", "Y"), genmod = c(-2, 1.2, 1.2))
LP <- m$coef[1] + m$coef[2]*v$X1 + m$coef[3]*v$X2
appraisal(obs = v$Y, LP = LP, plots = TRUE, title = "Calibration plot")
``` |

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