stepadj: Multipicity correction for Stepwise Selected models
In someMTP: Some Multiple Testing Procedures

Description Usage Arguments Details Value Author(s) References See Also Examples

Corrects the p-value due to model selection. It works with models of class glm and selected with function step {stats\).

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step.adj(object, MC = 1000, scope = NULL, scale = 0, 
         direction = c("both", "backward", "forward"), 
         trace = 0, keep = NULL, steps = 1000, k = 2)

`object`	object of class `glm`. Note that `formula` have to write by variables name like `y~var1+var2+var3`, `data` is a data.frame (see example below), `offset` is not yet implemented, avoid its use, `glm(formula, data, family=gaussian)` produce the same result of lm(formula, data), then linear model can be allways performed
`MC`	number of random permutations for the dependent variable
`scope`	as in function `step`
`scale`	as in function `step`
`direction`	as in function `step`
`trace`	as in function `step`
`keep`	as in function `step`
`steps`	as in function `step`
`k`	as in function `step`, other arguments are not implemented yet.

It performs anova function (stats library) on the model selected by function step vs the null model with the only intercept and it corrects for multiplicity. For lm models and gaussian glm models it computes a F-test, form other models it uses Chisquare-test (see also anova.glm and anova.lm help).

An anova table with an extra column reporting the corrected p-value

Livio Finos and Chiara Brombin

L. Finos, C. Brombin, L. Salmaso (2010). Adjusting stepwise p-values in generalized linear models. Communications in Statistics - Theory and Methods.

glm, anova

set.seed(17)
y=rnorm(10)
x=matrix(rnorm(50),10,5)
#define a data.frame to be used in the glm function
DATA=data.frame(y,x)
#fit the model on a toy dataset
mod=glm(y~X1+X2+X3+X4+X5,data=DATA)

#select the model using function step
mod.step=step(mod, trace=0)
#test the selected model vs the null model
anova(glm(y~1, data=DATA),mod.step,test="F")

#step.adj do the same, but it also provides multiplicity control
step.adj(mod,MC=101, trace=0)

Analysis of Deviance Table

Model 1: y ~ 1
Model 2: y ~ X1 + X2 + X3 + X5
  Resid. Df Resid. Dev Df Deviance     F   Pr(>F)   
1         9     6.1606                              
2         5     0.5852  4   5.5754 11.91 0.009072 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Analysis of Deviance Table on Selected Model 
with Multiplicity Correction (weak FWE-control)
based on 101 Random permutations

Model 1: y ~ 1
Model 2: y ~ X1 + X2 + X3 + X5
  Resid. Df Resid. Dev Df Deviance     F    Pr(>F)  sw.adj P
1         9     6.1606                                      
2         5     0.5852  4   5.5754 11.91 0.0090721  0.089109