I've tried to contact William Revelle about this but he isn't responding.
In the psych package there is a function called cor.smoother, which determines whether or not a correlation matrix is positive definite. Its explanation is as follows:
"cor.smoother examines all of nvar minors of rank nvar-1 by systematically dropping one variable at a time and finding the eigen value decomposition. It reports those variables, which, when dropped, produce a positive definite matrix. It also reports the number of negative eigenvalues when each variable is dropped. Finally, it compares the original correlation matrix to the smoothed correlation matrix and reports those items with absolute deviations great than cut. These are all hints as to what might be wrong with a correlation matrix."
It is the really the statement in bold that I am hoping someone can interpret in a more understandable way for me?
A belated answer to your question.
Correlation matrices are said to be improper (or more accurately, not positive semi-definite) when at least one of the eigen values of the matrix is less than 0. This can happen if you have some missing data and are using pair-wise complete correlations. It is particularly likely to happen if you are doing tetrachoric or polychoric correlations based upon data sets with some or even a lot of missing data.
(A correlation matrix, R, may be decomposed into a set of eigen vectors (X) and eigen values (lambda) where R = X lambda X’. This decomposition is the basis of components analysis and factor analysis, but that is more than you want to know.)
The cor.smooth function finds the eigen values and then adjusts the negative ones by making them slightly positive (and adjusting the other ones to compensate for this change).
The cor.smoother function attempts to identify the variables that are making the matrix improper. It does this by considering all the matrices generated by dropping one variable at a time and seeing which ones of those are not positive semi-definite (i.e. have eigen values < 0.) Ideally, this will identify one variable that is messing things up.
An example of this is in the burt data set where the sorrow-tenderness correlation was probably mistyped and the .87 should be .81.
cor.smoother(burt) #identifies tenderness and sorrow as likely culprits
I have defined two variables x and y. I want to regress y on x, but the sum of residuals using the lm is non-zero
Here are the variables:
x<-c(1,10,6,4,3,5,8,9,0,3,1,1,12,6,3,11,15,5,10,4)
y<-c(2,3,6,7,8,4,2,1,0,0,6,1,3,5,2,4,1,0,1,9)
gh<-lm(y~x)
sum(gh$residuals)
# [1] 4.718448e-16
I don't understand why the sum of residuals is non-zero. It should be zero by the procedure of OLS.
Thanks
Floating-point numbers have limited precision. Only a finite set of real numbers can be represented exactly as 32- or 64-bit floats; the rest are approximated by rounding them to the nearest number that can be represented exactly.
This means that, while mathematically the residuals should sum up to zero, in computer representation they might not.
I highly recommend What Every Computer Scientist Should Know About Floating-Point Arithmetic.
I am working with financial/economic data in case you are wondering about the large size of some of the coefficients below... My general question has to do with the simulation of parameter coefficients output from a linear random effects model in R. I am attempting to generate a random sample of beta coefficients using the model coefficients and the variance-covariance (VCOV) matrix from the same model in R. My question is: Why am I receiving the error below about the square root of the expected values using the rmvnorm() function from the mvtnorm{} package? How can I deal with this warning/issue?
#Example call: lmer model with random effects by YEAR
#mlm<-lmer(DV~V1+V2+V3+V2*V3+V4+V5+V6+V7+V8+V9+V10+V11+(1|YEAR), data=dat)
#Note: 5 years (5 random effects total)
#LMER call yields the following information:
coef<-as.matrix(c(-28037800,0.8368619,2816347,8681918,-414002.6,371010.7,-26580.84,80.17909,271.417,-239.1172,3.463785,-828326))
sigma<-as.matrix(rbind(c(1834279134971.21,-415.95,-114036304870.57,-162630699769.14,-23984428143.44,-94539802675.96,
-4666823087.67,-93751.98,1735816.34,-1592542.75,3618.67,14526547722.87),
c(-415.95,0.00,41.69,94.17,-8.94,-22.11,-0.55,0.00,0.00,0.00,0.00,-7.97),
c(-114036304870.57,41.69,12186704885.94,12656728536.44,-227877587.40,-2267464778.61,
-4318868.82,8909.65,-355608.46,338303.72,-321.78,-1393244913.64),
c(-162630699769.14,94.17,12656728536.44,33599776473.37,542843422.84,4678344700.91,-27441015.29,
12106.86,-225140.89,246828.39,-593.79,-2445378925.66),
c(-23984428143.44,-8.94,-227877587.40,542843422.84,32114305557.09,-624207176.98,-23072090.09,
2051.16,51800.37,-49815.41,-163.76,2452174.23),
c(-94539802675.96,-22.11,-2267464778.61,4678344700.91,-624207176.98,603769409172.72,90275299.55,
9267.90,208538.76,-209180.69,-304.18,-7519167.05),
c(-4666823087.67,-0.55,-4318868.82,-27441015.29,-23072090.09,90275299.55,82486186.42,-100.73,
15112.56,-15119.40,-1.34,-2476672.62),
c(-93751.98,0.00,8909.65,12106.86,2051.16,9267.90,-100.73,2.54,8.73,-10.15,-0.01,-1507.62),
c(1735816.34,0.00,-355608.46,-225140.89,51800.37,208538.76,15112.56,8.73,527.85,-535.53,-0.01,21968.29),
c(-1592542.75,0.00,338303.72,246828.39,-49815.41,-209180.69,-15119.40,-10.15,-535.53,545.26,0.01,-23262.72),
c(3618.67,0.00,-321.78,-593.79,-163.76,-304.18,-1.34,-0.01,-0.01,0.01,0.01,42.90),
c(14526547722.87,-7.97,-1393244913.64,-2445378925.66,2452174.23,-7519167.05,-2476672.62,-1507.62,21968.29,
-23262.72,42.90,229188496.83)))
#Error begins here:
betas<-rmvnorm(n=1000, mean=coef, sigma=sigma)
#rmvnorm breaks, Error returned:
Warning message: In sqrt(ev$values) : NaNs produced
When I Google the following search string: "rmvnorm, "Warning message: In sqrt(ev$values) : NaNs produced," I saw that:
http://www.nickfieller.staff.shef.ac.uk/sheff-only/mvatasksols6-9.pdf On Page 4 that this error indicates "negative eigen values." Although, I have no idea conceptually or practically what a negative eigen value is or why that they would be produced in this instance.
The second search result: [http://www.r-tutor.com/r-introduction/basic-data-types/complex2 Indicates that this error arises because of an attempt to take the square root of -1, which is "not a complex value" (you cannot take the square root of -1).
The question remains, what is going on here with the random generation of the betas, and how can this be corrected?
sessionInfo() R version 3.0.2 (2013-09-25) Platform:
x86_64-apple-darwin10.8.0 (64-bit)
Using the following packages/versions
mvtnorm_0.9-9994,
lme4_1.1-5,
Rcpp_0.10.3,
Matrix_1.1-2-2,
lattice_0.20-23
You have a huge range of scales in your eigenvalues:
range(eigen(sigma)$values)
## [1] -1.005407e-05 1.863477e+12
I prefer to use mvrnorm from the MASS package, just because it comes installed automatically with R. It also appears to be more robust:
set.seed(1001)
m <- MASS::mvrnorm(n=1000, mu=coef, Sigma=sigma) ## works fine
edit: OP points out that using method="svd" with rmvnorm also works.
If you print the code for MASS::mvrnorm, or debug(MASS:mvrnorm) and step through it, you see that it uses
if (!all(ev >= -tol * abs(ev[1L]))) stop("'Sigma' is not positive definite")
(where ev is the vector of eigenvalues, in decreasing order, so ev[1] is the largest eigenvalue) to decide on the positive definiteness of the variance-covariance matrix. In this case ev[1L] is about 2e12, tol is 1e-6, so this would allow negative eigenvalues up to a magnitude of about 2e6. In this case the minimum eigenvalue is -1e-5, well within tolerance.
Farther down MASS::mvrnorm uses pmax(ev,0) -- that is, if it has decided that the eigenvalues are not below tolerance (i.e. it didn't fail the test above), it just truncates the negative values to zero, which should be fine for practical purposes.
If you insisted on using rmvnorm you could use Matrix::nearPD, which tries to force the matrix to be positive definite -- it returns a list which contains (among other things) the eigenvalues and the "positive-definite-ified" matrix:
m <- Matrix::nearPD(sigma)
range(m$eigenvalues)
## [1] 1.863477e+04 1.863477e+12
The eigenvalues computed from the matrix are not quite identical -- nearPD and eigen use slightly different algorithms -- but they're very close.
range(eigen(m$mat)$values)
## [1] 1.861280e+04 1.863477e+12
More generally,
Part of the reason for the huge range of eigenvalues might be predictor variables that are scaled very differently. It might be a good idea to scale your input data if possible to make the variances more similar to each other (i.e., it will make all of your numerical computations more stable) -- you can always rescale the values once you've generated them
It's also the case that when matrices are very close to singular (i.e. some eigenvalues are very close to zero), small numerical differences can change the sign of the eigenvalues. In particular, if you copy and paste the values, you might lose some precision and cause this problem. Using dput(vcov(fit)) or save(vcov(fit)) to save the variance-covariance matrix at full precision is safer.
if you have no idea what "positive definite" means you might want to read up about it. The Wikipedia articles on covariance matrices and positive definite matrices might be a little too technical for you to start with; this question on StackExchange is closer, but still a little technical. The next entry on my Google journey was this one, which looks about right.
Now i have a 7000*7000 correlation matrix and I have to do PCA on this in R.
I used the
CorPCA <- princomp(covmat=xCor)
, xCor is the correlation matrix
but it comes out
"covariance matrix is not non-negative definite"
it is because i have some negative correlation in that matrix.
I am wondering which inbuilt function in R that i can use to get the result of PCA
One method to do the PCA is to perform an eigenvalue decomposition of the covariance matrix, see wikipedia.
The advantage of the eigenvalue decomposition is that you see which directions (eigenvectors) are significant, i.e. have a noticeable variation expressed by the associated eigenvalues. Moreover, you can detect if the covariance matrix is positive definite (all eigenvalues greater than zero), not negative-definite (which is okay) if there are eigenvalues equal zero or if it is indefinite (which is not okay) by negative eigenvalues. Sometimes it also happens that due to numerical inaccuracies a non-negative-definite matrix becomes negative-definite. In that case you would observe negative eigenvalues which are almost zero. In that case you can set these eigenvalues to zero to retain the non-negative definiteness of the covariance matrix. Furthermore, you can still interpret the result: eigenvectors contributing the significant information are associated with the biggest eigenvalues. If the list of sorted eigenvalues declines quickly there are a lot of directions which do not contribute significantly and therefore can be dropped.
The built-in R function is eigen
If your covariance matrix is A then
eigen_res <- eigen(A)
# sorted list of eigenvalues
eigen_res$values
# slightly negative eigenvalues, set them to small positive value
eigen_res$values[eigen_res$values<0] <- 1e-10
# and produce regularized covariance matrix
Areg <- eigen_res$vectors %*% diag(eigen_res$values) %*% t(eigen_res$vectors)
not non-negative definite does not mean the covariance matrix has negative correlations. It's a linear algebra equivalent of trying to take square root of negative number! You can't tell by looking at a few values of the matrix, whether it's positive definite.
Try adjusting some default values like tolerance in princomp call. Check this thread for example: How to use princomp () function in R when covariance matrix has zero's?
An alternative is to write some code of your own to perform what is called a n NIPLAS analysis. Take a look at this thread on the R-mailing list: https://stat.ethz.ch/pipermail/r-help/2006-July/110035.html
I'd even go as far as asking where did you obtain the correlation matrix? Did you construct it yourself? Does it have NAs? If you constructed xCor from your own data, do you think you can sample the data and construct a smaller xCor matrix? (say 1000X1000). All these alternatives try to drive your PCA algorithm through the 'happy path' (i.e. all matrix operations can be internally carried out without difficulties in diagonalization etc..i.e., no more 'non-negative definite error msgs)
This question already has answers here:
Closed 11 years ago.
Possible Duplicate:
how to generate pseudo-random positive definite matrix with constraints on the off-diagonal elements?
The user wants to impose a unique, non-trivial, upper/lower bound on the correlation between every pair of variable in a var/covar matrix.
For example: I want a variance matrix in which all variables have 0.9 > |rho(x_i,x_j)| > 0.6, rho(x_i,x_j) being the correlation between variables x_i and x_j.
Thanks.
There are MANY issues here.
First of all, are the pseudo-random deviates assumed to be normally distributed? I'll assume they are, as any discussion of correlation matrices gets nasty if we diverge into non-normal distributions.
Next, it is rather simple to generate pseudo-random normal deviates, given a covariance matrix. Generate standard normal (independent) deviates, and then transform by multiplying by the Cholesky factor of the covariance matrix. Add in the mean at the end if the mean was not zero.
And, a covariance matrix is also rather simple to generate given a correlation matrix. Just pre and post multiply the correlation matrix by a diagonal matrix composed of the standard deviations. This scales a correlation matrix into a covariance matrix.
I'm still not sure where the problem lies in this question, since it would seem easy enough to generate a "random" correlation matrix, with elements uniformly distributed in the desired range.
So all of the above is rather trivial by any reasonable standards, and there are many tools out there to generate pseudo-random normal deviates given the above information.
Perhaps the issue is the user insists that the resulting random matrix of deviates must have correlations in the specified range. You must recognize that a set of random numbers will only have the desired distribution parameters in an asymptotic sense. Thus, as the sample size goes to infinity, you should expect to see the specified distribution parameters. But any small sample set will not necessarily have the desired parameters, in the desired ranges.
For example, (in MATLAB) here is a simple positive definite 3x3 matrix. As such, it makes a very nice covariance matrix.
S = randn(3);
S = S'*S
S =
0.78863 0.01123 -0.27879
0.01123 4.9316 3.5732
-0.27879 3.5732 2.7872
I'll convert S into a correlation matrix.
s = sqrt(diag(S));
C = diag(1./s)*S*diag(1./s)
C =
1 0.0056945 -0.18804
0.0056945 1 0.96377
-0.18804 0.96377 1
Now, I can sample from a normal distribution using the statistics toolbox (mvnrnd should do the trick.) As easy is to use a Cholesky factor.
L = chol(S)
L =
0.88805 0.012646 -0.31394
0 2.2207 1.6108
0 0 0.30643
Now, generate pseudo-random deviates, then transform them as desired.
X = randn(20,3)*L;
cov(X)
ans =
0.79069 -0.14297 -0.45032
-0.14297 6.0607 4.5459
-0.45032 4.5459 3.6549
corr(X)
ans =
1 -0.06531 -0.2649
-0.06531 1 0.96587
-0.2649 0.96587 1
If your desire was that the correlations must ALWAYS be greater than -0.188, then this sampling technique has failed, since the numbers are pseudo-random. In fact, that goal will be a difficult one to achieve unless your sample size is large enough.
You might employ a simple rejection scheme, whereby you do the sampling, then redo it repeatedly until the sample has the desired properties, with the correlations in the desired ranges. This may get tiring.
An approach that might work (but one that I've not totally thought out at this point) is to use the standard scheme as above to generate a random sample. Compute the correlations. I they fail to lie in the proper ranges, then identify the perturbation one would need to make to the actual (measured) covariance matrix of your data, so that the correlations would be as desired. Now, find a zero mean random perturbation to your sampled data that would move the sample covariance matrix in the desired direction.
This might work, but unless I knew that this is actually the question at hand, I won't bother to go any more deeply into it. (Edit: I've thought some more about this problem, and it appears to be a quadratic programming problem, with quadratic constraints, to find the smallest perturbation to a matrix X, such that the resulting covariance (or correlation) matrix has the desired properties.)
This is not a complete answer, but a suggestion of a possible constructive method:
Looking at the characterizations of the positive definite matrices (http://en.wikipedia.org/wiki/Positive-definite_matrix) I think one of the most affordable approaches could be using the Sylvester criterion.
You can start with a trivial 1x1 random matrix with positive determinant and expand it in one row and column step by step while ensuring that the new matrix has also a positive determinant (how to achieve that is up to you ^_^).
Woodship,
"First of all, are the pseudo-random deviates assumed to be normally distributed?"
yes.
"Perhaps the issue is the user insists that the resulting random matrix of deviates must have correlations in the specified range."
Yes, that's the whole difficulty
"You must recognize that a set of random numbers will only have the desired distribution parameters in an asymptotic sense."
True, but this is not the problem here: your strategy works for p=2, but fails for p>2, regardless of sample size.
"If your desire was that the correlations must ALWAYS be greater than -0.188, then this sampling technique has failed, since the numbers are pseudo-random. In fact, that goal will be a difficult one to achieve unless your sample size is large enough."
It is not a sample size issue b/c with p>2 you do not even observe convergence to the right range for the correlations, as sample size growths: i tried the technique you suggest before posting here, it obviously is flawed.
"You might employ a simple rejection scheme, whereby you do the sampling, then redo it repeatedly until the sample has the desired properties, with the correlations in the desired ranges. This may get tiring."
Not an option, for p large (say larger than 10) this option is intractable.
"Compute the correlations. I they fail to lie in the proper ranges, then identify the perturbation one would need to make to the actual (measured) covariance matrix of your data, so that the correlations would be as desired."
Ditto
As for the QP, i understand the constraints, but i'm not sure about the way you define the objective function; by using the "smallest perturbation" off some initial matrix, you will always end up getting the same (solution) matrix: all the off diagonal entries will be exactly equal to either one of the two bounds (e.g. not pseudo random); plus it is kind of an overkill isn't it ?
Come on people, there must be something simpler