I'm using the factanal
function with this simple code:
factor_analyisi<-factanal(CFscale,factors=2,rotate="varimax")
But I get this error:
Error in solve.default(cv) :
system is computationally singular: reciprocal condition number = 1.70441e-20
Why??
This is the head
of CFscale matrix,
Age X6Ckine.CCL21. BCA.1..CXCL13. ENA.78.CXCL5. EOTAXIN.CCL11.
ID2 1.3589518 -0.62230682 0.4234253 -1.0014789 -0.8840086
ID3 -1.0019921 -0.08609578 0.8596906 -0.5711517 -0.7924021
ID4 0.1621975 -0.62285177 -0.4081158 -0.6698831 -0.7941621
ID5 -0.9368626 -0.57273123 -0.3889110 -1.0381312 -0.9192960
ID7 1.1554221 -1.01596723 -0.4453790 -0.3098134 -0.8896405
ID8 -1.2136629 -0.19600220 0.1276134 -0.5717698 -0.7149634
with dim(CFscale) 39 309
Can someone help me?
These types of question are all the same. You can see a glm
version of this here:
In the course of creating a linear model (which a factor based model is) often in the course of the computation, you need to solve something that looks like:
Ab=c
where A
is a coefficient matrix, b
a vector, and c
the result vector. Solving this involves reversing A
, which is essentially your data (not really, but close enough). Inverting a matrix means find an A^-1 such that
A*A^-1=I
and I is a diagonal matrix with 1s on the diagonal. When A is not invertable, it is called singular - and you can't solve the system.
From a practical point of view, this means the problem is in the data . Of course you don't want to hear that, so there are remedies. By problems, it usually means two or more of your variables are highly correlated. Thus I would suggest:
Also this is factor analysis, and you have only 5 variables. That's a very low amount - usually meaning you aren't expecting more than 1 factor, maybe 2. If you get 3, or maybe 4 or more factors, then this isn't the analysis you're looking for - the point is to see if you can reduce your independent variables, and this won't really do that if you don't have 1-2 factors. Since you ran looking for 2 factors, this may not be beneficial.
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