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.

blr_step_p_forward(model, ...) # S3 method for default blr_step_p_forward(model, penter = 0.3, details = FALSE, ...) # S3 method for blr_step_p_forward plot(x, model = NA, print_plot = TRUE, ...)

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 |

`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 with the least AIC; an object of class `glm`

number of steps

variables added to the model

akaike information criteria

bayesian information criteria

deviance

predictors

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.

Other variable selection procedures:
`blr_step_aic_backward()`

,
`blr_step_aic_both()`

,
`blr_step_aic_forward()`

,
`blr_step_p_backward()`

if (FALSE) { # 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 }