Hi R-listers,
Savings regression results after a loop is straightforward. But what about when
you have nested loops?
I am running a regression of the form
lm(y~1+x+M+ D[,i] + D[,j] + D[,k])
where x is the variable of interest. M and D are vectors with other covariates.
Vectors "M" and "x" are included in every regression. Then i loop over the
columns of D to use all unique combinations of
covariates in that matrix and save the results for variable "x" in each run.
This is the code :
(due to the random numbers it will produce 10 similar betas,t.values etc...)
M<-matrix(rnorm(100),100,3)
D<-matrix(rnorm(100),100,5)
y<-matrix(rnorm(100),100,1)
x<-matrix(rnorm(100),100,1)
beta<-NULL
t.value<-NULL
sd<-NULL
i<-1
while(i<=ncol(D)){
j<-i+1
while(j<=ncol(D)){
k<-j+1
while(k<=ncol(D)){
Res<-lm(y~1+x+M+ D[,i] + D[,j] + D[,k])
beta<-cbind(summary(Res)$coef[2])
t.value<-cbind(summary(Res)$coef[2,3])
sd<-cbind(summary(Res)$coef[2,2])
k<-k+1
}
j<-j+1
}
i<-i+1
}
If i looped over only, say k, then something like:
beta[k]<-cbind(summary(Res)$coef[2])
would have been sufficient...but what now that there are loops over i,j,k?
Maybe "while" is a bad idea??
I would appreciate your answers!
Best Regards,
Dimitrios Soudis
Ph.D. Candidate
Faculty of Economics
U. of Groningen
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