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Last data update: 2014.03.03

R: Stepwise Model Selection

stepwise

R Documentation

Stepwise Model Selection

Description

This function is a front end to the stepAIC function in the MASS package.

Usage

stepwise(mod, 
    direction = c("backward/forward", "forward/backward", "backward", "forward"), 
    criterion = c("BIC", "AIC"), ...)

Arguments

`mod`	a model object of a class that can be handled by `stepAIC`.
`direction`	if `"backward/forward"` (the default), selection starts with the full model and eliminates predictors one at a time, at each step considering whether the criterion will be improved by adding back in a variable removed at a previous step; if `"forward/backwards"`, selection starts with a model including only a constant, and adds predictors one at a time, at each step considering whether the criterion will be improved by removing a previously added variable; `"backwards"` and `"forward"` are similar without the reconsideration at each step.
`criterion`	for selection. Either `"BIC"` (the default) or `"AIC"`. Note that `stepAIC` labels the criterion in the output as `"AIC"` regardless of which criterion is employed.
`...`	arguments to be passed to `stepAIC`.

Value

The model selected by stepAIC.

Author(s)

John Fox jfox@mcmaster.ca

References

W. N. Venables and B. D. Ripley Modern Applied Statistics Statistics with S, Fourth Edition Springer, 2002.

Examples

# adapted from ?stepAIC in MASS
if (require(MASS)){
data(birthwt)
bwt <- with(birthwt, {
    race <- factor(race, labels = c("white", "black", "other"))
    ptd <- factor(ptl > 0)
    ftv <- factor(ftv)
    levels(ftv)[-(1:2)] <- "2+"
    data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0),
               ptd, ht = (ht > 0), ui = (ui > 0), ftv)
})
birthwt.glm <- glm(low ~ ., family = binomial, data = bwt)
print(stepwise(birthwt.glm, trace = FALSE))
print(stepwise(birthwt.glm, direction="forward/backward"))
}

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

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'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(RcmdrMisc)
Loading required package: car
Loading required package: sandwich
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/RcmdrMisc/stepwise.Rd_%03d_medium.png", width=480, height=480)
> ### Name: stepwise
> ### Title: Stepwise Model Selection
> ### Aliases: stepwise
> ### Keywords: models
> 
> ### ** Examples
> 
> # adapted from ?stepAIC in MASS
> if (require(MASS)){
+ data(birthwt)
+ bwt <- with(birthwt, {
+     race <- factor(race, labels = c("white", "black", "other"))
+     ptd <- factor(ptl > 0)
+     ftv <- factor(ftv)
+     levels(ftv)[-(1:2)] <- "2+"
+     data.frame(low = factor(low), age, lwt, race, smoke = (smoke > 0),
+                ptd, ht = (ht > 0), ui = (ui > 0), ftv)
+ })
+ birthwt.glm <- glm(low ~ ., family = binomial, data = bwt)
+ print(stepwise(birthwt.glm, trace = FALSE))
+ print(stepwise(birthwt.glm, direction="forward/backward"))
+ }
Loading required package: MASS

Direction:  backward/forward
Criterion:  BIC 


Call:  glm(formula = low ~ lwt + ptd + ht, family = binomial, data = bwt)

Coefficients:
(Intercept)          lwt      ptdTRUE       htTRUE  
    1.01737     -0.01728      1.40677      1.89397  

Degrees of Freedom: 188 Total (i.e. Null);  185 Residual
Null Deviance:	    234.7 
Residual Deviance: 210.1 	AIC: 218.1

Direction:  forward/backward
Criterion:  BIC 

Start:  AIC=239.91
low ~ 1

        Df Deviance    AIC
+ ptd    1   221.90 232.38
+ lwt    1   228.69 239.17
<none>       234.67 239.91
+ ui     1   229.60 240.08
+ smoke  1   229.81 240.29
+ ht     1   230.65 241.13
+ age    1   231.91 242.40
+ race   2   229.66 245.39
+ ftv    2   232.09 247.81

Step:  AIC=232.38
low ~ ptd

        Df Deviance    AIC
<none>       221.90 232.38
+ age    1   217.30 233.02
+ lwt    1   217.50 233.22
+ ht     1   217.66 233.39
+ ui     1   219.12 234.85
+ smoke  1   219.33 235.05
+ race   2   217.02 237.99
+ ftv    2   217.88 238.84
- ptd    1   234.67 239.91

Call:  glm(formula = low ~ ptd, family = binomial, data = bwt)

Coefficients:
(Intercept)      ptdTRUE  
     -1.057        1.463  

Degrees of Freedom: 188 Total (i.e. Null);  187 Residual
Null Deviance:	    234.7 
Residual Deviance: 221.9 	AIC: 225.9
> 
> 
> 
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> 
> dev.off()
null device 
          1 
>