# reclass: Risk reclassification table In ModelGood: Validation of risk prediction models

## Description

Tabulate grouped risks predicted by two different methods, models, algorithms

## Usage

 `1` ```reclass(list, newdata, cuts = seq(0, 100, 25), digits = 1) ```

## Arguments

 `list` A list with two elements. Each element should either be a vector with probabilities, or an object for which `predictStatusProb` can extract predicted risk based on newdata. `newdata` Passed on to `predictStatusProb` `cuts` Risk quantiles to group risk `digits` Number of digits to show for the predicted risks

## Details

All risks are multiplied by 100 before

## Value

reclassification table

## Author(s)

Thomas A. Gerds <[email protected]>

predictStatusProb

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19``` ```set.seed(40) N <- 40 X1 <- rnorm(N) X2 <- rbinom(N,1,.4) X3 <- rnorm(N) expit <- function(x) exp(x)/(1+exp(x)) lp <- expit(X1 + X2 + X3) Y <- factor(rbinom(N,1,lp)) dat <- data.frame(Y=Y,X1=X1,X2=X2,X3=X3) lm1 <- glm(Y~X1,data=dat,family="binomial") lm2 <- glm(Y~X1+X2,data=dat,family="binomial") rc <- reclass(list("lrm.X1"=lm1,"lrm.X1.X2"=lm2),newdata=dat) print(rc) plot(rc) rc2 <- reclass(list("lrm.X1"=lm1,"lrm.X1.X2"=lm2),newdata=dat,cuts=c(0,5,10,50,100)) print(rc2) plot(rc2) ```

### Example output

```         lrm.X1.X2
lrm.X1    0-25% 25-50% 50-75% 75-100%
0-25%       4      1      0       0
25-50%      2     10      3       0
50-75%      0      1     14       0
75-100%     0      0      0       5
lrm.X1.X2
lrm.X1    0-5% 5-10% 10-50% 50-100%
0-5%       0     0      0       0
5-10%      0     0      0       0
10-50%     0     0     17       3
50-100%    0     0      1      19
```

ModelGood documentation built on May 2, 2019, 5 p.m.