compute_max_weight {CARRoT} | R Documentation |

## Maximum feasible weight of the predictors

### Description

Function which computes maximal weight (multiplied by the corresponding EPV rule) of a regression according to the rule of thumb applied to the outcome variable. Weight of a regression equals the sum of weights of its predictors.

### Usage

```
compute_max_weight(outi,mode)
```

### Arguments

`outi` |
set of outcomes |

`mode` |
indicates the mode: 'linear' (linear regression), 'binary' (logistic regression), 'multin' (multinomial regression) |

### Details

For continuous outcomes it equals sample size divided by 10, for multinomial it equals the size of the smallest category divided by 10

### Value

returns an integer value of maximum allowed weight multiplied by 10

### References

Peduzzi P, Concato J, Kemper E, Holford TR, Feinstein AR (1996).
“A simulation study of the number of events per variable in logistic regression analysis.”
*Journal of Clinical Epidemiology*, **49**(12), 1373-1379.
ISSN 0895-4356, doi:10.1016/S0895-4356(96)00236-3, http://dx.doi.org/10.1016/S0895-4356(96)00236-3.

### Examples

```
#continuous outcomes
compute_max_weight(runif(100,0,1),'linear')
#binary outcomes
compute_max_weight(rbinom(100,1,0.4),'binary')
```

*CARRoT*version 3.0.2 Index]