Re: [R] help on model selection - step()

Prof Brian Ripley Mon, 11 Aug 2008 00:19:53 -0700

On Mon, 11 Aug 2008, Rodrigo Gazaffi wrote:

dears R-users,
I'm interested in model selection problem, and i have faced some problems
that i would like to ask for help.


well,
this is a very small example with 4 variable (just one var. is the response
- z) with 100 individuals
i would like to do a stepwise search, for the "best" model, and a use BIC
criteria.

I know when I have a lot of variables, let's say 120, I know, it's not wise,
consider the full model, so starting from "y~1", i can stop the search with
the option steps.
but when i have the IC with a negative value, is there any way that a can
stop the search?

Not in the existing function. The absolute size of AIC (or BIC) has nomeaning for a linear model fit (since this is true of the log-likelihood-- it depends on the scale of measurement).

But R is Open Source so you can modify step() in any way you like, even todo nonsensical things.

for example: form this data set
the first step gives AIC=3.6, and the 2nd gives -9.03, IS THERE ANY WAY that
a could say, "stop here, the previous one is the best for me"... like here,
my model would be with no variable.
I know that example, looks like silly but a have bigger data, that this
happens in thirtieth iteration, what's why i would like some help

i used the step(),  is there other function that could stop this besides
step()?

cheers,
Rodrigo Gazaffi



x1 <- c( 0.3718,  0.3718,  0.3718,  0.3718,  0.3718,  0.3718,  0.3718,
0.3718, -1.0000,  0.3718,  0.3718,  0.3718,  0.3718,  0.3718,  0.3718,
0.3718,  0.3718,  0.3718,  0.3718,  0.3718,  0.3718,  0.3718,  0.0713,
0.1774,  0.3570,  0.3718,  0.3718,  0.3718, -1.0000,  0.3718, -1.0000,
0.1774,  0.3718,  0.3718,  0.0709,  0.1774, -1.0000, -1.0000,  0.3718,
0.3718,  0.0713,  0.0709,  0.3718,  0.3718,  0.3718,  0.3718,  0.2614,
0.2614, -0.9995, -1.0000,  0.1774,  0.3718, -1.0000, -1.0000,  0.1774,
0.3718,  0.1774,  0.3718,  0.3718, -1.0000,  0.3718,  0.3718,  0.3718,
0.3718,  0.3718, -1.0000,  0.3718,  0.3718,  0.3718,  0.3718,  0.0709,
0.0710,  0.3718,  0.3718,  0.3718,  0.3718,  0.3718,  0.0709,  0.3718,
0.0709,  0.0709,  0.3718,  0.0709,  0.3570,  0.3718,  0.3718,  0.3718,
0.0709,  0.3718,  0.3718,  0.3718, -1.0000,  0.3718,  0.3718,  0.3718,
-1.0000,  0.3718,  0.3718,  0.3718,  0.3718)

x2 <- c( 0.3898, -0.9995,  0.3898,  0.3898,  0.3898,  0.1978,  0.3898,
-0.9997, -1.0000, -1.0000,  0.3898,  0.3898,  0.3898,  0.3898, -1.0000,
0.1978, -1.0000,  0.3898,  0.3898, -1.0000,  0.1978,  0.3898,  0.3898,
0.3898,  0.1978, -0.9995,  0.3792, -1.0000, -1.0000,  0.3898,  0.0837,
0.0837,  0.0837,  0.3898,  0.0837,  0.3898,  0.3898,  0.0837,  0.3898,
0.0837,  0.0837, -1.0000, -1.0000,  0.3898,  0.0841,  0.1976, -1.0000,
0.2467,  0.1978,  0.3842,  0.3898,  0.3848,  0.2766,  0.3898,  0.3898,
0.3898, -1.0000, -0.9995,  0.3898,  0.3898,  0.0837,  0.3898, -1.0000,
0.1978,  0.3898,  0.2766,  0.3898,  0.3898,  0.3898,  0.2766,  0.3898,
0.3866,  0.1978,  0.3898, -1.0000, -1.0000,  0.3898,  0.3898,  0.3898,
0.3898,  0.3898,  0.1978,  0.0841, -1.0000,  0.0837,  0.3898,  0.3898,
-1.0000,  0.3898,  0.3898, -1.0000,  0.3898,  0.3898,  0.0837,  0.3898,
0.3898,  0.1976,  0.3898,  0.3898,  0.3898)

x3 <- c( 0.9999,  0.9999,  0.9999,  1.0000, -0.9999,  0.9999, -0.9999,
0.9999, -0.9999, -1.0000, -1.0000, -0.9999, -0.9980, -0.9999, -0.9999,
-1.0000, -0.9999, -0.9999, -0.9999,  1.0000, -1.0000,  1.0000, -1.0000,
-1.0000, -1.0000, -0.9980,  1.0000, -0.9999, -1.0000, -1.0000, -0.9999,
-0.9999,  0.9999,  1.0000, -0.9999, -1.0000,  1.0000,  0.9999,  1.0000,
-0.9999,  0.9999, -1.0000, -1.0000, -0.9999,  0.8356,  0.8356, -0.3241,
0.8356,  0.8353,  0.8356,  1.0000, -1.0000, -1.0000, -1.0000, -1.0000,
-1.0000, -0.9999,  0.9999,  1.0000, -0.9980,  0.9999,  1.0000, -1.0000,
1.0000, -0.9999,  1.0000,  0.9999, -1.0000,  1.0000, -1.0000,  0.9999,
0.9999, -1.0000, -1.0000,  1.0000, -1.0000, -1.0000,  1.0000,  1.0000,
1.0000, -0.9999,  1.0000, -1.0000,  1.0000, -1.0000,  1.0000, -1.0000,
1.0000,  1.0000,  1.0000, -1.0000, -0.9999, -0.8547, -1.0000, -0.7851,
0.8356, -1.0000, -0.9999, -0.9999,  1.0000)

z  <- c( -0.006548414, -1.035584950, -0.006548414,  0.180549138,
0.741841793,  1.770878329, -0.848487398, -1.035584950, -2.251719037,
0.461195465,  2.051524656,  1.116036897, -0.193645966,  0.274097913,
0.180549138,  0.274097913,  0.274097913,  0.835390569,  0.928939345,
-1.316231277,  0.087000362,  0.741841793,  1.116036897,  0.180549138,
-0.193645966,  0.274097913,  0.274097913,  1.490232001, -1.222682502,
1.303134449,  0.367646689, -0.100097190, -0.006548414, -1.035584950,
1.490232001,  0.648293017, -2.064621485, -2.625914141,  1.022488121,
-0.006548414, -1.222682502, -0.567841070, -0.942036174,  0.461195465,
1.770878329,  0.461195465, -1.503328829, -1.035584950, -0.848487398,
-0.567841070,  1.396683225,  2.051524656, -0.942036174, -0.754938622,
-1.596877605,  0.648293017, -0.287194742, -0.567841070,  0.461195465,
-0.474292294, -0.100097190, 0.287194742,  0.554744241, -0.006548414,
1.209585673, -1.409780053,  0.928939345,  0.928939345, -0.006548414,
1.396683225, -0.380743518,  0.928939345,  1.490232001,  1.770878329,
-1.129133726, -0.848487398, -0.380743518,  0.274097913, -1.409780053,
-0.100097190,  0.367646689, -0.474292294,  0.554744241, -2.251719037,
0.087000362, -0.848487398,  0.741841793, -2.064621485, -0.006548414,
0.461195465, -0.100097190, -0.006548414,  0.648293017, -0.287194742,
0.928939345, -0.193645966, -0.474292294, -0.006548414, -1.035584950,
0.461195465)

step(lm(z
~1),scope=list(lower=~1,upper=~x1+x2+x3),direction="both",k=log(length(z)))
#########
Start:  AIC=3.6
z ~ 1

      Df Sum of Sq    RSS    AIC
+ x1    1    15.671 83.329 -9.028
+ x2    1    12.390 86.610 -5.165
+ x3    1     7.403 91.597  0.433
<none>              99.000  3.600

Step:  AIC=-9.03
z ~ x1

      Df Sum of Sq     RSS     AIC
+ x2    1    13.675  69.654 -22.348
+ x3    1     7.078  76.251 -13.299
<none>               83.329  -9.028
- x1    1    15.671  99.000   3.600

Step:  AIC=-22.35
z ~ x1 + x2

      Df Sum of Sq     RSS     AIC
+ x3    1     8.930  60.723 -31.463
<none>               69.654 -22.348
- x2    1    13.675  83.329  -9.028
- x1    1    16.956  86.610  -5.165

Step:  AIC=-31.46
z ~ x1 + x2 + x3

      Df Sum of Sq     RSS     AIC
<none>               60.723 -31.463
- x3    1     8.930  69.654 -22.348
- x2    1    15.527  76.251 -13.299
- x1    1    16.669  77.392 -11.813

Call:
lm(formula = z ~ x1 + x2 + x3)

Coefficients:
(Intercept)           x1           x2           x3
   -0.2015       0.9000       0.7269      -0.3083

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--
Brian D. Ripley,                  [EMAIL PROTECTED]
Professor of Applied Statistics,  http://www.stats.ox.ac.uk/~ripley/
University of Oxford,             Tel:  +44 1865 272861 (self)
1 South Parks Road,                     +44 1865 272866 (PA)
Oxford OX1 3TG, UK                Fax:  +44 1865 272595

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Re: [R] help on model selection - step()

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