HI,
I hope this helps you,
set.seed(1)
dat1<-data.frame(X1=rnorm(25,15),X2=rnorm(25,5),X3=runif(25,0.4),X4=rnorm(25,12),Y=rnorm(25,35))
ColNam<-names(dat1)
ColNam
#[1] "X1" "X2" "X3" "X4" "Y"
ColNam<-ColNam[!ColNam %in% "Y"]
n<-length(ColNam)
ColNam
#[1] "X1" "X2" "X3" "X4"
id1<-unlist(lapply(1:n,function(x)combn(1:n,x,simplify=F)),recursive=F)
f1<-lapply(id1,function(x)
paste("Y~",paste(ColNam[x],collapse="+")))
res1<- lapply(f1,function(x) lm(as.formula(x),data=dat1))
summary(res1[[1]])
Call:
lm(formula = as.formula(x), data = dat1)
Residuals:
Min 1Q Median 3Q Max
-1.46344 -0.78252 -0.08563 0.68324 1.49788
Coefficients:
Estimate Std. Error t value Pr(>|t|)
(Intercept) 34.26057 3.03804 11.277 7.56e-11 ***
X1 0.05828 0.19991 0.292 0.773
---
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Residual standard error: 0.9305 on 23 degrees of freedom
Multiple R-squared: 0.003682, Adjusted R-squared: -0.03964
F-statistic: 0.085 on 1 and 23 DF, p-value: 0.7732
A.K.
----- Original Message -----
From: zel7223 <jmichel.for...@hotmail.fr>
To: r-help@r-project.org
Cc:
Sent: Thursday, August 9, 2012 9:28 AM
Subject: [R] All combinations possible in a mutliple regression
Hi,
I want to use four independent variables to predict the output of one
dependent variable using a linear model lm. I want to compare all possible
combinations of the 4 independent variables, including singles, pairs and
triples.
I was thinking of using the AIC test to compare all models and pick the best
one.
The model looks like this :
lm(Y ~ X1 + X2 + X3 + X4)
Thanks for your help
Cheers
Jean-Michel Fortin
UOttawa
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