Dear TY, Considering that you used different methods -- maximum-likelihood factor analysis in R and principal components analysis in SAS -- the results are quite similar (although the three rotated factors/components come out in different orders).
I hope this helps, John > -----Original Message----- > From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On > Behalf Of Tae-Young Heo > Sent: March-31-09 7:07 AM > To: r-help@r-project.org > Subject: [R] Factor Analysis Output from R and SAS > > Dear Users, > > I ran factor analysis using R and SAS. However, I had different outputs from > R and SAS. > Why they provide different outputs? Especially, the factor loadings are > different. > I did real dataset(n=264), however, I had an extremely different from R and > SAS. > Why this things happened? Which software is correct on? > > Thanks in advance, > > - TY > > #R code with example data > > # A little demonstration, v2 is just v1 with noise, > # and same for v4 vs. v3 and v6 vs. v5 > # Last four cases are there to add noise > # and introduce a positive manifold (g factor) > v1 <- c(1,1,1,1,1,1,1,1,1,1,3,3,3,3,3,4,5,6) > v2 <- c(1,2,1,1,1,1,2,1,2,1,3,4,3,3,3,4,6,5) > v3 <- c(3,3,3,3,3,1,1,1,1,1,1,1,1,1,1,5,4,6) > v4 <- c(3,3,4,3,3,1,1,2,1,1,1,1,2,1,1,5,6,4) > v5 <- c(1,1,1,1,1,3,3,3,3,3,1,1,1,1,1,6,4,5) > v6 <- c(1,1,1,2,1,3,3,3,4,3,1,1,1,2,1,6,5,4) > m1 <- cbind(v1,v2,v3,v4,v5,v6) > cor(m1) > # v1 v2 v3 v4 v5 v6 > #v1 1.0000000 0.9393083 0.5128866 0.4320310 0.4664948 0.4086076 > #v2 0.9393083 1.0000000 0.4124441 0.4084281 0.4363925 0.4326113 > #v3 0.5128866 0.4124441 1.0000000 0.8770750 0.5128866 0.4320310 > #v4 0.4320310 0.4084281 0.8770750 1.0000000 0.4320310 0.4323259 > #v5 0.4664948 0.4363925 0.5128866 0.4320310 1.0000000 0.9473451 > #v6 0.4086076 0.4326113 0.4320310 0.4323259 0.9473451 1.0000000 > > factanal(m1, factors=3) # varimax is the default > > > # Output from R > > #Call: > #factanal(x = m1, factors = 3) > > #Uniquenesses: > # v1 v2 v3 v4 v5 v6 > #0.005 0.101 0.005 0.224 0.084 0.005 > > #Loadings: > # Factor1 Factor2 Factor3 > #v1 0.944 0.182 0.267 > #v2 0.905 0.235 0.159 > #v3 0.236 0.210 0.946 > #v4 0.180 0.242 0.828 > #v5 0.242 0.881 0.286 > #v6 0.193 0.959 0.196 > > # Factor1 Factor2 Factor3 > #SS loadings 1.893 1.886 1.797 > #Proportion Var 0.316 0.314 0.300 > #Cumulative Var 0.316 0.630 0.929 > > #The degrees of freedom for the model is 0 and the fit was 0.4755 > > /* SAS code with example data*/ > > data fact; > input v1-v6; > datalines; > 1 1 3 3 1 1 > 1 2 3 3 1 1 > 1 1 3 4 1 1 > 1 1 3 3 1 2 > 1 1 3 3 1 1 > 1 1 1 1 3 3 > 1 2 1 1 3 3 > 1 1 1 2 3 3 > 1 2 1 1 3 4 > 1 1 1 1 3 3 > 3 3 1 1 1 1 > 3 4 1 1 1 1 > 3 3 1 2 1 1 > 3 3 1 1 1 2 > 3 3 1 1 1 1 > 4 4 5 5 6 6 > 5 6 4 6 4 5 > 6 5 6 4 5 4 > ; > run; > > proc factor data=fact rotate=varimax method=p nfactors=3; > var v1-v6; > run; > > /* Output from SAS*/ > > The FACTOR > Procedure > Initial Factor Method: Principal > Components > > Prior Communality Estimates: > ONE > > > > Eigenvalues of the Correlation Matrix: > Total = 6 Average = 1 > > Eigenvalue Difference > Proportion Cumulative > > 1 3.69603077 2.62291629 > 0.6160 0.6160 > 2 1.07311448 0.07234039 > 0.1789 0.7949 > 3 1.00077409 0.83977061 > 0.1668 0.9617 > 4 0.16100348 0.12004232 > 0.0268 0.9885 > 5 0.04096116 0.01284515 > 0.0068 0.9953 > 6 0.02811601 > 0.0047 1.0000 > > 3 factors will be retained by the > NFACTOR criterion. > > > > Factor Pattern > > Factor1 > Factor2 Factor3 > > v1 0.79880 > 0.54995 -0.17614 > v2 0.77036 > 0.56171 -0.24862 > v3 0.79475 > -0.07685 0.54982 > v4 0.75757 > -0.08736 0.59785 > v5 0.80878 > -0.45610 -0.33437 > v6 0.77771 > -0.48331 -0.36933 > > > Variance Explained by Each > Factor > > Factor1 Factor2 > Factor3 > > 3.6960308 1.0731145 > 1.0007741 > > > Final Communality Estimates: Total > = 5.769919 > > v1 v2 v3 > v4 v5 v6 > 0.97154741 0.97078498 0.93983835 > 0.93897798 0.97394719 0.97482345 > > > > The FACTOR Procedure > Rotation Method: Varimax > > Orthogonal Transformation > Matrix > > 1 > 2 3 > > 1 0.58233 > 0.57714 0.57254 > 2 -0.64183 > 0.75864 -0.11193 > 3 -0.49895 > -0.30229 0.81220 > > > Rotated Factor Pattern > > Factor1 > Factor2 Factor3 > > v1 0.20008 > 0.93148 0.25272 > v2 0.21213 > 0.94590 0.17626 > v3 0.23781 > 0.23418 0.91019 > v4 0.19893 > 0.19023 0.92909 > v5 0.93054 > 0.22185 0.24253 > v6 0.94736 > 0.19384 0.19939 > > > Variance Explained by Each > Factor > > Factor1 Factor2 > Factor3 > > 1.9445607 1.9401828 > 1.8851759 > > > Final Communality Estimates: Total > = 5.769919 > > v1 v2 v3 > v4 v5 v6 > > 0.97154741 0.97078498 0.93983835 > 0.93897798 0.97394719 0.97482345 > > [[alternative HTML version deleted]] > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
