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***** To join INSNA, visit http://www.insna.org *****Dear Matthew,Those two models are very different in both the underlying assumptions and the specifications. The first one is a SAR model which assumes that the errors are multivariate normal distributed, hence, no iid observations, and thus, the likelihood function is different from what you would observe in a plain OLS. Also, the spautolm includes the autocorrelation term when you specify listwd, so, if discrep_net_prop_more.W is your exposure terms, it means that you are including it twice but in the wrong way. Instead, you should type something like:spautolm(formula = BLQFMHEDWM.W ~ 1, listw = w.list.trunc,family = "SAR", zero.policy = TRUE)For example. The autocorrelation term is the Lambda coefficient which is reported afterward.The lm, on the other hand, runs an OLS model in which the errors are assumed to be iid making that type of model (including an exposure term) not valid as your estimates will be biased by construction (unless you are using a lagged exposure). A good reference on spatial econometrics is here:https://www.cairn.info/
resume.php?ID_ARTICLE=REI_123_(LeSage, 2008) 0019HIHOn Tue, Feb 20, 2018 at 2:43 PM, Meisel, Matthew <[log in to unmask]> wrote:***** To join INSNA, visit http://www.insna.org *****______________________________Dear all,My mentor has a large sociocentric network dataset (N = 1342) and I am trying to analyze the data using network autocorrelation models. When I run a model in R using the spautolm function and then I run the same model using the lm function, I get incredibly different results (the effects are significant but in different directions), and I have no idea on why these two methods would provide such vastly different results. See the models below. FYI, the predictor is dichotomous and the outcome is continuous. If you have any thoughts, please let me know, cause I do not know which results to trust.Best,MattCall: spautolm(formula = BLQFMHEDWM.W ~ discrep_net_prop_more.W, listw = w.list.trunc,family = "SAR", zero.policy = TRUE)Residuals:Min 1Q Median 3Q Max-5.71857 -1.19085 -0.49541 0.55372 15.64806Coefficients:Estimate Std. Error z value Pr(>|z|)(Intercept) 1.64796 0.40402 4.0789 4.524e-05discrep_net_prop_more.W -1.30912 0.18460 -7.0916 1.325e-12Lambda: 0.83807 LR test value: 425.13 p-value: < 2.22e-16Numerical Hessian standard error of lambda: 0.030919Log likelihood: -2680.358ML residual variance (sigma squared): 5.0308, (sigma: 2.2429)Number of observations: 1178Number of parameters estimated: 4AIC: 5368.7Call: lm(formula = BLQFMHEDWM.W ~ discrep_net_prop_more.W, data = subject)Residuals:Min 1Q Median 3Q Max-2.2335 -1.7280 -1.7280 0.7665 18.2720Coefficients:Estimate Std. Error t value Pr(>|t|)(Intercept) 1.72799 0.08878 19.465 <2e-16 ***discrep_net_prop_more.W 0.50554 0.23578 2.144 0.0322 *---Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1Residual standard error: 2.823 on 1176 degrees of freedomMultiple R-squared: 0.003894, Adjusted R-squared: 0.003047F-statistic: 4.597 on 1 and 1176 DF, p-value: 0.03223-- ______________________________ _________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.______________________________ ______________________________ _________ SOCNET is a service of INSNA, the professional association for social network researchers (http://www.insna.org). To unsubscribe, send an email message to [log in to unmask] containing the line UNSUBSCRIBE SOCNET in the body of the message.