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wombat:sterrors [2018/09/14] kmeyer |
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| - assuming large samples, and | - assuming large samples, and | ||
| - a series of approximations - see the //Technical Details// section of the manual. | - a series of approximations - see the //Technical Details// section of the manual. | ||
| - | In some cases, this approximation simply fails. Reasons for this may be, for instance, that your sample is very small, or that you are dealing with a model which is overparameterised. Please consult the statistical literature on maximum likelihood estimation for background information. | + | In some cases, this approximation simply fails. Typically, this is the case when the average information matrix (from which sampling covariances are derived) is not `safely' positive definite. Reasons for this may be, for instance, that your sample is very small, or that you are dealing with a model which is over-parameterised. Please consult the statistical literature on maximum likelihood estimation for background information. |
| - | The latter includes multivariate analyses where some covariance matrices have eigenvalues which are effectively zero. In that scenario, it may help to fit a reduced rank model. Otherwise, you should attempt to use a `better' data set (i..e. one which supports the question you are asking) - if that is not feasible you may simply have to accept that the approximation of standard errors does not always work. | + | The latter includes multivariate analyses where some covariance matrices have eigenvalues which are effectively zero. In that scenario, it may help to fit a reduced rank model. Otherwise, you should attempt to use a `better' data set (i.e. one which supports the question you are asking) - if that is not feasible you may simply have to accept that the __approximation__ of standard errors does not always work. |
| Alternatively, you could try the sampling based approximation of standard errors - note though that this may alos be problematic if your model is overparameterised. | Alternatively, you could try the sampling based approximation of standard errors - note though that this may alos be problematic if your model is overparameterised. | ||
