Hi,
perhaps that is what you want?
> df <- as.data.frame(matrix(runif(100),ncol=10))
> form <- as.formula(paste(names(df)[length(df)], "~ ."))
> lm(form,data=df)
Call:
lm(formula = form, data = df)
Coefficients:
(Intercept) V1 V2 V3 V4
-1.367 -2.920 3.631 -7.259 -3.704
V5 V6 V7 V8 V9
4.225 3.049 4.522 2.496 -0.578
> form <- as.formula(paste(names(df)[length(df)],
"~",paste(names(df)[3],names(df)[4],sep="+")))
> lm(form,data=df)
Call:
lm(formula = form, data = df)
Coefficients:
(Intercept) V3 V4
0.652 0.360 -0.448
Regards,Christian
Hi!
I have a dataset with some 300+ variables and 2000+ records. I'd like to grind
through a bunch of analyses on the variables by using a script, but can't
figure out how to refer to variable names properly. For some of the simpler
stuff I use various "apply" functions, but for others (like t-tests etc) I need
by command procedures. I've tried various flavors of "for(var in
names(Dataset)){...}" but this does not work consistently. Actually, "for(var
in names(Dataset){print var}, seems to work perfectly, giving a list of
variable names, but "for(var in names(Dataset)){mean(var, na.rm=T) or for(var
in names(Dataset)){glm(var~var1+var2+var3....} do not.
Any suggestions about how best to go about this?
Thanks
Jon
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