blr_step_p_forward {blorr} | R Documentation |

## Stepwise forward regression

### Description

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

### Usage

```
blr_step_p_forward(model, ...)
## Default S3 method:
blr_step_p_forward(model, penter = 0.3, details = FALSE, ...)
## S3 method for class 'blr_step_p_forward'
plot(x, model = NA, print_plot = TRUE, ...)
```

### Arguments

`model` |
An object of class |

`...` |
Other arguments. |

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

`details` |
Logical; if |

`x` |
An object of class |

`print_plot` |
logical; if |

### Value

`blr_step_p_forward`

returns an object of class `"blr_step_p_forward"`

.
An object of class `"blr_step_p_forward"`

is a list containing the
following components:

`model` |
model with the least AIC; an object of class |

`steps` |
number of steps |

`predictors` |
variables added to the model |

`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.

Kutner, MH, Nachtscheim CJ, Neter J and Li W., 2004, Applied Linear Statistical Models (5th edition). Chicago, IL., McGraw Hill/Irwin.

### See Also

Other variable selection procedures:
`blr_step_aic_backward()`

,
`blr_step_aic_both()`

,
`blr_step_aic_forward()`

,
`blr_step_p_backward()`

### Examples

```
## Not run:
# stepwise forward regression
model <- glm(honcomp ~ female + read + science, data = hsb2,
family = binomial(link = 'logit'))
blr_step_p_forward(model)
# stepwise forward regression plot
model <- glm(honcomp ~ female + read + science, data = hsb2,
family = binomial(link = 'logit'))
k <- blr_step_p_forward(model)
plot(k)
# final model
k$model
## End(Not run)
```

*blorr*version 0.3.0 Index]