# appraisal: appraisal In rpajou/predsims: Tools for prediction model simulation studies

## Description

Estimates of prediction model performance

## Usage

 ```1 2``` ```appraisal(observed, LP, plots = TRUE, title = "", weight = rep(1, length(observed))) ```

## Arguments

 `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.

## Details

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.

## Value

`appraisal` returns a matrix of performance estimates and optional calibration plot

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12``` ```## 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") ```

rpajou/predsims documentation built on May 25, 2017, 5:12 p.m.