About lvm

lvm · ‎11-07-2014

I strongly disagree with this statement from loredana.cornea.

lvm · ‎11-05-2014

There can be many reasons for nonconvergence, and it would be not for the reason you are worried about. It is not clear if it is getting close to convergence (just needing more steps) or is stuck far from the solution locus. It could be stuck on the inner or outer iteration of the doubly iterative RSPL method. I suggest you increase the maximum number of iterations and try different optimization techniques. Add nloptions maxiter=1000 maxfunc=1000 technique=NRRIDG; Or try TRUREG or CONGRA for technique. For binary data, you could also change your estimation to method=LAPLACE on the procedure statement. But this might be v-e-r-y slow with the number of levels you have.

lvm · ‎11-04-2014

As Steve says, there is no reason to rescale the weights. Just use 1/(individual_variance). Using repeated /type=un will just give you a single variance (even though you specified unstructed). THis is because you do not have a subject= option. Without this option, each observation is a single subject. If your goal is to have a different weight for each canopy position, then use the code given by Steve: repeated / group = canopy_position;

lvm · ‎10-28-2014

By the way, a program with random chain provider(chain) id(provider*chain); will run must faster and use less memory if written as: random int provider id*provider / sub=chain; These have equivalent meanings, but the later works better computationally.

lvm · ‎10-28-2014

If the dataset is too large for PROC GLIMMIX for binary data, you should try the %hpglimmix macro. http://www.jstatsoft.org/v58/i08 This is written for large scale problems and nonnormal data. It is not a sas product (not distributed by sas), but a macro written for sas. It will take you some time to get used to the syntax (unless you used the old/obsolete %glimmix macro). You can contact the senior author of the article for specific assistance.

lvm · ‎10-27-2014

This fully explains your results. You have a high covariance between the two means. The SE of the difference is: sqrt(.001502 + .001502 - 2*.001487) = .00548. The large block variance is at least partly responsible. Because of the large variability between batches, the precision of an individual mean averaged across blocks is low (high SE), but the difference between means has high precision. This is exactly why one includes a batch variance in a model.

lvm · ‎10-26-2014

Put in the NOINT option on the model statement and rerun. It is easier to see the variances/covariances of the speed means directly in the covb matrix without the intercept (speed needs to stay as the first term in the model).

lvm · ‎10-24-2014

The SE of a difference is more complex than you think when variables are correlated. By definition, with a random block effect and a nonzero block variance, your means are correlated. The variance of a difference of two means is, in general: var(mu1-m2) = var(mu1) + var(mu2) - 2*cov(mu1,mu2) SE(mu1-mu2) is just the square root of this, and var(mu1), etc., are the squares of the individual SEs. You probably have a very large block variance. Ignoring the repeated measures, the covariance of any two randomly selected observations in the same block have a covariance equal to the block variance. Taking out the block variance is moving some of the total variability into the indiviudal mean SEs. You don't want this because it gives an incorrect measure of the uncertainty of the mean estimates (not taking design into account). Put in covb and corrb as options in the model statement to see the var-covariance matrix of the parameter estimates.

lvm · ‎10-22-2014

The thought of using an unstructured 12x12 matrix for a repeated measure is intimidating. You have a awful lot of parameters, even for 1200 observations. Did you try method=mivque0, just to see what the G matrix looks like with a noniterative method (if this works)? By the way, the warning you get about the Hession NPD is not the same as G being NPD. http://support.sas.com/resources/papers/proceedings12/332-2012.pdf

lvm · ‎10-17-2014

Your distribution is normal, which is in the exponential family. But the binomial and Poisson do not have a free scale (variance) parameter. The normal, gamma, beta, and others do have a scale parameter. This is a HUGE difference, for many reasons. Find the several articles/books by Walter Stroup. Overdispersion is a concept only for distributions without a free scale parameter. My earlier response is correct.

lvm · ‎10-17-2014

It will be much better if you use PROC MIXED for your repeated measures, based on the information you supplied. Here is an old article, but it very nicely explains the transition from GLM to MIXED, and why GLM is not flexible enough for many applications. You have to change to a stacked data format. http://www.ats.ucla.edu/stat/sas/library/mixedglm.pdf Once you get the hang of this, you would be ready to look at doubly repeated measures: http://www2.sas.com/proceedings/sugi29/188-29.pdf http://www2.sas.com/proceedings/sugi31/187-31.pdf

lvm · ‎10-17-2014

For normal data (or any distribution with a free scale parameter), Gen. chi-squared/df does not need to be 1. It can be any value. For simple situations (variance component models), this statistic is the same as the residual variance.

lvm · ‎10-17-2014

Also, you said that you have 6 times, but your last G matrix is for 3 times.

lvm · ‎10-17-2014

Anytime one uses UN, one is not guaranteed of a PD matrix. A warning is given in the Log. For your example, I don't see how you are going from your outputted G matrix of UN@CS to the G matrix your are inputting to the next run.

lvm · ‎10-15-2014

Anyone who is confused by acronyms for "linear models" should read Stroup's excellent Global Forum paper. http://support.sas.com/resources/papers/proceedings11/349-2011.pdf

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