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Fit Analyses

Covratio

Covratio measures the effect of observations on the covariance matrix of the parameter estimates. For linear models,
C_{i} = \frac{|
 s^2_{(i)}
 ({X_{(i)}'}
 X_{(i)})^{-1} |}{| s^2
 ({X'}X)^{-1} | }
where X(i) is the X matrix without the ith observation.

Values of Ci near 1 indicate that the observation has little effect on the precision of the estimates. Observations with {|{C_{i}}-1| {\ge}3p/n} suggest a need for further investigation.

For generalized linear models,

C_{i} = \frac{|
 \hat{ \phi}_{(i)}
 ({X_{(i)}'} W_{(i)}
 X_{(i)})^{-1} |}{| \hat{ \phi}
 ({X'}W{X})^{-1} | }
where W(i) is the W matrix without the ith observation, W = Wo when the full Hessian is used, and W = We when Fisher's scoring method is used.

The Covratio statistics are stored in variables named C_yname for each response variable, where yname is the response variable name.

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