Hi Dongwoo: I tried the following example:
> erin1 <- summary(COL.mix.eig, correlation=TRUE, Nagelkerke=TRUE) > names(erin1) [1] "type" "rho" "coefficients" "rest.se" [5] "LL" "s2" "SSE" "parameters" [9] "logLik_lm.model" "AIC_lm.model" "method" "call" [13] "residuals" "opt" "tarX" "tary" [17] "y" "X" "fitted.values" "se.fit" [21] "similar" "ase" "rho.se" "LMtest" [25] "resvar" "zero.policy" "aliased" "listw_style" [29] "interval" "fdHess" "optimHess" "insert" [33] "trs" "LLNullLlm" "timings" "f_calls" [37] "hf_calls" "intern_classic" "coeftitle" "Coef" [41] "NK" "Wald1" "correlation" "correltext" [45] "LR1" > erin1$LMtest [,1] [1,] 0.2891926 > and it does indeed have the LMtest result. Or were you looking for the formula, please? Thanks, Erin ________________________________________ From: r-sig-geo-boun...@r-project.org [r-sig-geo-boun...@r-project.org] on behalf of Dongwoo Kang [dwkang1...@gmail.com] Sent: Tuesday, July 09, 2013 3:47 PM To: r-sig-geo@r-project.org Subject: [R-sig-Geo] Question about LM test for residual autocorrelation in R Dear list, Hello, I am Dongwoo Kang. I am studying Spatial Econometric modeling. I've faced one question while using *spdep* package in R. I want to ask your help for my qeustion. While estimating my empirical models, I want to test whether residuals of my spatial regression models (SEM, SAR, SARAR, SDM estimated by maximum likelihood) still have spatial autocorrelation pattern. I think I have two options, 1) Moran's I test using *"moran.mc"* function in R, 2) Lagrange multiplier diagnostics with LMerr option using *"lm.LMtests"* function in R. But I also find that for SAR, SDM, *"summary.sarlm"* function returns "LM test for residual autocorrelation" by default. However, this LM test is not given for SER and SARAR. At first, I thought that "Lagrange multiplier diagnostics" and "LM test for residual autocorrelation" in "*summary.sarlm*" function are same tests. But in my empirical results, they give me different statistics (please see below example). -----< example >--------------------------------------------------------------------------- > summary.sarlm(sar2, Nagelkerke=T) ... Log likelihood: -3533.378 for lag model ML residual variance (sigma squared): 224.88, (sigma: 14.996) Nagelkerke pseudo-R-squared: 0.76166 Number of observations: 853 Number of parameters estimated: 29 AIC: 7124.8, (AIC for lm: 7393.3) LM test for residual autocorrelation test value: 6.8391, p-value: 0.0089184 > > lm.LMtests(sar2$residuals, listw=w100.listw, test=c("LMerr")) Lagrange multiplier diagnostics for spatial dependence data: residuals: sar2$residuals weights: w100.listw LMErr = 3.7108, df = 1, p-value = 0.05406 ------------------------------------------------------------------------------------------------------- I try to find formulation of "LM test for residual autocorrelation" given by *"summary.sarlm"* function but I couldn't. Would you tell me where I can get some documents or explanations about "LM test for residual autocorrelation" given by *"summary.sarlm"*? I also want to know why "LM test for residual autocorrelation" is not provided in SER and SARAR models. Thank you very much for your time. Best regards, Dongwoo Kang [[alternative HTML version deleted]] _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo _______________________________________________ R-sig-Geo mailing list R-sig-Geo@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-geo