A class that extends the results of generalized linear models,
objects, for spatial stream networks by adding influence diagnostics and cross-validation
predictions to each observation.
Objects can be created by functions in the form
x is a glmssn-class object.
Objects of class
influenceSSN contain 4 list items and have the exact same
structure as glmssn-class objects. A
influenceSSN object retains
object as the second list item. When
residuals(x) is used for a glmssn object,
the data for which the model was fit is stored in point.data data.frame of the
observed points. This data.frame contains the response variable for the model,
and is appended by the following columns:
1 2 3 4 5 6 7 8 9 10 11 12
obsval ## The response value that was used to fit the model _fit_ _resid_ ## The raw residuals _resid.stand_ ## Standardized residuals; calculated by dividing the raw ## residuals by the corresponding standard errors _resid.student_ ## Studentized residuals _leverage_ ## Leverage _CooksD_ ## Cook's D _resid.crossv_ ## Cross-validation residuals _CrossValPred_ ## Cross-validation predictions _CrossValStdErr_ ## Estimated cross-validation standard errors.
Jay Ver Hoef support@SpatialStreamNetworks.com
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