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

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

model | An object of class |
---|---|

... | Other inputs. |

prem | p value; variables with p more than |

details | Logical; if |

x | An object of class |

print_plot | logical; if |

`blr_step_p_backward`

returns an object of class `"blr_step_p_backward"`

.
An object of class `"blr_step_p_backward"`

is a list containing the
following components:

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

total number of steps

variables removed from 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.

Other variable selection procedures:
`blr_step_aic_backward()`

,
`blr_step_aic_both()`

,
`blr_step_aic_forward()`

,
`blr_step_p_forward()`

if (FALSE) { # stepwise backward regression model <- glm(honcomp ~ female + read + science + math + prog + socst, data = hsb2, family = binomial(link = 'logit')) blr_step_p_backward(model) # stepwise backward regression plot model <- glm(honcomp ~ female + read + science + math + prog + socst, data = hsb2, family = binomial(link = 'logit')) k <- blr_step_p_backward(model) plot(k) # final model k$model }