![]() ![]() They prefer a variance-covariance matrix. All of this makes the correlation matrix very appealing, but for most algorithms its is not helpful at all. Since a variable cannot correlate with itself, the value here is 0. Now, if you remember correctly, then you know that a correlation is an association between two or more variables that can range from -1 to 1, where 0 means no association whatsoever. A correlation matrix is a scaled variance-covariance matrix. Variance-covariance sounds impressive, but is really nothing new as most of you have been introduced early to its most famous transformation - correlation. I have posted a lot about Mixed Models, and a key part of a mixed model is the variance-covariance matrix that gets introduced in the random part of the model. They are not truly independent as a larger mean often leads to a much larger variance, but they are easy to conceive and estimate. It makes sense, since many processes in the world can be approximated by a Normal distribution, and if they cannot then they surely can via the Central Limit Theorem.Īnother aspect that makes the Normal distribution so enticing is that it consists of two parameters - mean and variance - that are independently estimated. The first distribution most people are made familiar with is the Normal - or Gaussian - distribution. Drawing and plotting observations from a Multivariate Normal Distribution using R
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