blr_step_p_both {blorr} | R Documentation |

## Stepwise regression

### Description

Build regression model from a set of candidate predictor variables by entering and removing predictors based on p values, in a stepwise manner until there is no variable left to enter or remove any more.

### Usage

```
blr_step_p_both(model, ...)
## Default S3 method:
blr_step_p_both(model, pent = 0.1, prem = 0.3, details = FALSE, ...)
## S3 method for class 'blr_step_p_both'
plot(x, model = NA, print_plot = TRUE, ...)
```

### Arguments

`model` |
An object of class |

`...` |
Other arguments. |

`pent` |
p value; variables with p value less than |

`prem` |
p value; variables with p more than |

`details` |
Logical; if |

`x` |
An object of class |

`print_plot` |
logical; if |

### Value

`blr_step_p_both`

returns an object of class `"blr_step_p_both"`

.
An object of class `"blr_step_p_both"`

is a list containing the
following components:

`model` |
final model; an object of class |

`orders` |
candidate predictor variables according to the order by which they were added or removed from the model |

`method` |
addition/deletion |

`steps` |
total number of steps |

`predictors` |
variables retained in the model (after addition) |

`aic` |
akaike information criteria |

`bic` |
bayesian information criteria |

`dev` |
deviance |

`indvar` |
predictors |

### References

Chatterjee, Samprit and Hadi, Ali. Regression Analysis by Example. 5th ed. N.p.: John Wiley & Sons, 2012. Print.

### Examples

```
## Not run:
# stepwise regression
model <- glm(y ~ ., data = stepwise)
blr_step_p_both(model)
# stepwise regression plot
model <- glm(y ~ ., data = stepwise)
k <- blr_step_p_both(model)
plot(k)
# final model
k$model
## End(Not run)
```

*blorr*version 0.3.0 Index]