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.

blr_step_p_both(model, ...)
# S3 method for default
blr_step_p_both(model, pent = 0.1, prem = 0.3,
details = FALSE, ...)
# S3 method for blr_step_p_both
plot(x, model = NA, ...)

## Arguments

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

... |
Other arguments. |

pent |
p value; variables with p value less than `pent` will enter
into the model. |

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_both` . |

## 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:

orderscandidate predictor variables according to the order by which they were added or removed from the model

methodaddition/deletion

stepstotal number of steps

predictorsvariables retained in the model (after addition)

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.

## 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)
# }