R: summary method for class sarlm

summary.sarlm {spdep} | R Documentation

## summary method for class sarlm

### Description

Methods used for presenting the results of estimating spatial SAR models.

### Usage

    ## S3 method for class 'sarlm'
    summary(object, correlation = FALSE, Nagelkerke = FALSE, Hausman=FALSE, adj.se=FALSE, ...)
    ## S3 method for class 'sarlm'
    print(x, ...)
    ## S3 method for class 'summary.sarlm'
    print(x, digits = max(5, .Options$digits - 3),
    	signif.stars = FALSE, ...)

### Arguments

`object` | `sarlm` object from `lagsarlm` or `errorsarlm`
`correlation` | logical; if 'TRUE', the correlation matrix of the estimated parameters including sigma is returned and printed (default=FALSE)
`Nagelkerke` | if TRUE, the Nagelkerke pseudo R-squared is reported
`Hausman` | if TRUE, the results of the Hausman test for error models are reported
`adj.se` | if TRUE, adjust the coefficient standard errors for the number of fitted coefficients
`x` | `sarlm` object from `lagsarlm` or `errorsarlm` in `print.sarlm`, summary object from `summary.sarlm` for `print.summary.sarlm`
`digits` | the number of significant digits to use when printing
`signif.stars` | logical. If TRUE, "significance stars" are printed for each coefficient.
`...` | further arguments passed to or from other methods

### Value

The summary function `summary.sarlm` returns the `sarlm` object augmented with a coefficient matrix with probability values for coefficient asymptotic standard errors for type="error" and for type="lag" or "mixed" when object\$ase=TRUE, or a coefficient matrix with probability values for likelihood ratio tests between the model as reported and models with independent variables dropped in turn.

### Author(s)

Roger Bivand Roger.Bivand@nhh.no

### References

Cliff, A. D., Ord, J. K. 1981 Spatial processes, Pion; Ord, J. K. 1975 Estimation methods for models of spatial interaction, Journal of the American Statistical Association, 70, 120-126; Anselin, L. 1988 Spatial econometrics: methods and models. (Dordrecht: Kluwer); Anselin, L. 1995 SpaceStat, a software program for the analysis of spatial data, version 1.80. Regional Research Institute, West Virginia University, Morgantown, WV (www.spacestat.com); Anselin L, Bera AK (1998) Spatial dependence in linear regression models with an introduction to spatial econometrics. In: Ullah A, Giles DEA (eds) Handbook of applied economic statistics. Marcel Dekker, New York, pp. 237-289; Nagelkerke NJD (1991) A note on a general definition of the coefficient of determination. Biometrika 78: 691-692.

### See Also

`errorsarlm`, `lagsarlm`, `summary.lm`

### Examples

    data(oldcol)
    COL.mix.eig <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD,
     nb2listw(COL.nb), type="mixed", method="eigen")
    summary(COL.mix.eig, correlation=TRUE, Nagelkerke=TRUE)
    COL.mix.M <- lagsarlm(CRIME ~ INC + HOVAL, data=COL.OLD,
     nb2listw(COL.nb), type="mixed", method="Matrix")
    summary(COL.mix.M, correlation=TRUE, Nagelkerke=TRUE)
    COL.errW.eig <- errorsarlm(CRIME ~ INC + HOVAL, data=COL.OLD,
      nb2listw(COL.nb, style="W"), method="eigen")
    summary(COL.errW.eig, correlation=TRUE, Nagelkerke=TRUE, Hausman=TRUE)

[Package spdep version 0.6-13 Index]