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, ...)

## Arguments

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

... |
Other arguments. |

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

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

x |
An object of class `blr_step_p_forward` . |

## 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:

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

stepsnumber of steps

predictorsvariables added to 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.

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

## See also

## 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
# }