fitting of all possible models

Dear Lukas,

allthough I'm  intrigued by the purpose of what you are trying to do,  
as mentioned by some of the other persons on this list, I liked the  
challenge to write such a function.

I came up with the following during some train-traveling this morning:


tum <- function(x)
	{
		tum <- matrix(data=NA, nrow=2^x, ncol=x)
		
		for (i in 1:x)
			{
				tum[,i] <- c(rep(NA,2^i/2),rep(i+1,2^i/2))
			}
		
		return(tum)
	}

###

all.models <- function(model)
	{
		npred <- length(model$coefficients) - 1
		matr.model <- tum(npred)
		output <- matrix(data=NA, nrow=2^npred, ncol=1)
	
		for (t in 2:2^npred)
		{
			preds <- names(model$coefficients)
			interc <- names(model$coefficients)[1]
			form <- as.formula(paste(". ~", paste(preds[na.omit(matr.model 
[t,])],collapse="+")))

			model2 <- update(model, form)
			output[t,] <- mean(resid(model2)^2)
		}
	
	return(output)
	
	}

##


As you can see, I used a helper-function (tum, "the ultimate matrix")  
to the actual function. Also, I wrote it using lm instead of glm, but  
I suppose that you can easily alter that. As well, the function now  
returns just some regular fit-measurement. But that is not all that  
essential, I think.

The main point is: it works! Using this on my G4 mac, with a lm of 10  
predictors and 18 cases, it returns the output quite fast (<1 minute).

I hope you can put this to use. It needs some easy adapting to your  
specific needs, but I don't expect that to be a problem. If you need  
help with that, please contact me.

I'd appreciate to hear from you, if this function is helpful in any way.

sincerely,

Rense Nieuwenhuis





On Feb 27, 2007, at 8:46 , Indermaur Lukas wrote:

  

Hi,
Fitting all possible models (GLM) with 10 predictors will result in  
loads of (2^10 - 1) models. I want to do that in order to get the  
importance of variables (having an unbalanced variable design) by  
summing the up the AIC-weights of models including the same  
variable, for every variable separately. It's time consuming and  
annoying to define all possible models by hand.

Is there a command, or easy solution to let R define the set of all  
possible models itself? I defined models in the following way to  
process them with a batch job:

# e.g. model 1
preference<- formula(Y~Lwd + N + Sex + YY)
# e.g. model 2
preference_heterogeneity<- formula(Y~Ri + Lwd + N + Sex + YY)
etc.
etc.


I appreciate any hint
Cheers
Lukas





???
Lukas Indermaur, PhD student
eawag / Swiss Federal Institute of Aquatic Science and Technology
ECO - Department of Aquatic Ecology
?berlandstrasse 133
CH-8600 D?bendorf
Switzerland

Phone: +41 (0) 71 220 38 25
Fax    : +41 (0) 44 823 53 15
Email: lukas.indermaur at eawag.ch
www.lukasindermaur.ch

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