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, ...)

## Arguments

model |
An object of class `lm` ; the model should include all
candidate predictor variables. |

... |
Other inputs. |

prem |
p value; variables with p more than `prem` will be removed
from the model. |

details |
Logical; if `TRUE` , will print the regression result at
each step. |

x |
An object of class `blr_step_p_backward` . |

print_plot |
logical; if `TRUE` , prints the plot else returns a plot object. |

## Value

`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:

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

stepstotal number of steps

removedvariables removed from the model

aicakaike information criteria

bicbayesian information criteria

devdeviance

indvarpredictors

## References

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

## See also

## Examples

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
}