![]() \[I= \gamma \Sigma_i\Sigma_jw_įMoran.I(drink, scaled = FALSE, weight = weights1, na.rm = TRUE, rowstandardize = FALSE) #> $observed Results may depend heavily on whether or not you demean your variables of interest, the chosen neighborhood/weight matrix (and hence on distance decay functions and type of standardization of the weight matrix). With respect to Moran’s I, its values are actually quite difficult to compare across different spatial/network settings. A lot of build in functions in different packages of R are not very clear on all the defaults. Spatial autocorrelation measures are actually quite complex. We will start with a calculation of the correlation between the score of actor i and the (mean) score of the alters of i to whom i is connected directly. Yup, we need some spatial autocorrelation measure. “Hey, that sounds like some sort of correlation!” To which my students respond in unison with: We want to know if nodes who are closer to one another in the network are more a like. Our inter-/intra group density and Coleman’s homophily measures describe the extent to which similar people are more likely to be connected. ![]() 12.2.3 From directed to reciprocated ties.11.6.3 Google Scholar Profiles and Publications.6.8 Random Intercept Cross-Lagged Micro-Macro Model RI-CLP-MM.3.5.3 Distinguishing period trends from lifecourse trends.3.5.2 Opinion homophily over within-time.3.1.3 Conditional multinomial logit model.
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