ols_step_all_possible function

All possible regression

Fits all regressions involving one regressor, two regressors, three regressors, and so on. It tests all possible subsets of the set of potential independent variables.

ols_step_all_possible(model, ...)

## Default S3 method:
ols_step_all_possible(model, max_order = NULL, ...)

## S3 method for class 'ols_step_all_possible'
plot(x, model = NA, print_plot = TRUE, ...)

Arguments

• model: An object of class lm.
• ...: Other arguments.
• max_order: Maximum subset order.
• x: An object of class ols_step_all_possible.
• print_plot: logical; if TRUE, prints the plot else returns a plot object.

Returns

ols_step_all_possible returns an object of class "ols_step_all_possible". An object of class "ols_step_all_possible" is a data frame containing the following components:

• mindex: model index

• n: number of predictors

• predictors: predictors in the model

• rsquare: rsquare of the model

• adjr: adjusted rsquare of the model

• rmse: root mean squared error of the model

• predrsq: predicted rsquare of the model

• cp: mallow's Cp

• aic: akaike information criteria

• sbic: sawa bayesian information criteria

• sbc: schwarz bayes information criteria

• msep: estimated MSE of prediction, assuming multivariate normality

• fpe: final prediction error

• apc: amemiya prediction criteria

• hsp: hocking's Sp

Examples

model <- lm(mpg ~ disp + hp, data = mtcars)
k <- ols_step_all_possible(model)
k

# plot
plot(k)

# maximum subset
model <- lm(mpg ~ disp + hp + drat + wt + qsec, data = mtcars)
ols_step_all_possible(model, max_order = 3)

References

Mendenhall William and Sinsich Terry, 2012, A Second Course in Statistics Regression Analysis (7th edition). Prentice Hall