As Bert suggests, the help file explains the differences between princomp 
and prcomp.  Look under the Details section
?princomp
?prcomp

# the first PC using princomp
a <- princomp(USArrests, cor=TRUE)
a1 <- a$scores[, 1]

# the first PC using prcomp
# use center=TRUE and scale.=TRUE for the equivalent of cor=TRUE in 
princomp()
b <- prcomp(USArrests, center=TRUE, scale.=TRUE)
b1 <- b$x[, 1]

# the results are nearly the same
plot(a1, b1)
abline(0, 1, col="red")
abline(lsfit(a1, b1))

Jean


Bert Gunter <gunter.berton@gene.com> wrote on 08/22/2012 11:42:57 PM:
> 
> How about reading the Help files?
> 
> -- Bert
> 
> On Wed, Aug 22, 2012 at 8:34 PM, jpm miao <miaojpm@gmail.com> wrote:
> > Hi ,
> >
> >    To my knowledge, there're two functions that can do principal 
component
> > analysis, princomp and prcomp.
> >
> >    I don't really know the difference; the only thing I know is that 
when
> > the sample size < number of variable, only prcomp will work. Could 
someone
> > tell me the difference or where I can find easy-to-read reference?
> >
> >    To access the first PC using princomp:
> >      Mpca<-princomp(M, cor=T)
> >      Mpca$scores[,1]
> >
> >     How can I access the first PC using prcomp?
> >     Mpca<-prcomp(M)
> >
> >     Is there an option for "cor=T"?
> >     In case where both functions work, will the results be the same?
> >
> >     Thanks,
> >
> > Miao
> 
> 
> 
> -- 
> 
> Bert Gunter
> Genentech Nonclinical Biostatistics
> 
> Internal Contact Info:
> Phone: 467-7374
> Website:
> http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/
> pdb-biostatistics/pdb-ncb-home.htm

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