summary.sarlm(spdep)
summary.sarlm()所属R语言包：spdep
summary method for class sarlm
汇总类sarlm方法
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Methods used for presenting the results of estimating spatial SAR models.
用于呈现空间SAR模型估计的结果，所用的方法。
用法----------Usage----------
## S3 method for class 'sarlm'
summary(object, correlation = FALSE, Nagelkerke = FALSE, Hausman=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, ...)
参数----------Arguments----------
参数：object
sarlm object from lagsarlm or errorsarlm
sarlm的对象lagsarlm或errorsarlm
参数：correlation
logical; if 'TRUE', the correlation matrix of the estimated parameters including sigma is returned and printed (default=FALSE)
逻辑的相关矩阵估计的参数，包括西格玛TRUE，则返回并打印（默认值= FALSE）
参数：Nagelkerke
if TRUE, the Nagelkerke pseudo R-squared is reported
如果为true，Nagelkerke伪R平方报道
参数：Hausman
if TRUE, the results of the Hausman test for error models are reported
如果为true，误差模型的Hausman检验结果报告
参数：x
sarlm object from lagsarlm or errorsarlm in print.sarlm, summary object from summary.sarlm for print.summary.sarlm
sarlm的对象lagsarlm或errorsarlm在print.sarlm，总结对象从summary.sarlmprint.summary.sarlm
参数：digits
the number of significant digits to use when printing
打印时所使用的数量显著位数
参数：signif.stars
logical. If TRUE, "significance stars" are printed for each coefficient.
逻辑。如果是TRUE，“意义明星”打印每个系数。
参数：...
further arguments passed to or from other methods
进一步的参数传递给其他方法
值----------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.
汇总函数summary.sarlm返回sarlm对象扩充的系数矩阵的概率值系数渐近标准误差用于type =“错误”和“滞后”或“混合”时，对象\ $ ASE = TRUE，或系数矩阵模型之间的似然比检验报告和独立变量的模型依次下降的概率值。
（作者）----------Author(s)----------
Roger Bivand <a href="mailto:Roger.Bivand@nhh.no">Roger.Bivand@nhh.no</a>
参考文献----------References----------
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
参见----------See Also----------
errorsarlm, lagsarlm,
errorsarlm，lagsarlm，
实例----------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)
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注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
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发表于 2012-9-30 14:46:01
