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diff --git a/R Scripts/lm.R b/R Scripts/lm.R
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+library(pROC)
+library(igraph)
+setwd('~/Documents/Cascade Project')
+load('Raw Data/lcc.RData')
+load('Results/lcc_bw.RData')
+
+is_vic = V(lcc)$vic
+vics = which(V(lcc)$vic==T)
+nonvics = which(V(lcc)$vic==F)
+thresh = length(vics)/length(union(vics,nonvics))
+
+nbhdVics = function(neighbs){return(sum(is_vic[neighbs[-1]]))}
+#setdiff(is_vic order-(order-1) to get only those at the given order
+n1s = neighborhood(lcc,1,V(lcc))
+n2s = neighborhood(lcc,2,V(lcc))
+n1vics = unlist(lapply(n1s,nbhdVics))
+n2vics = unlist(lapply(n2s,nbhdVics))
+
+data = data.frame(vic         = V(lcc)$vic,
+                  sex         = V(lcc)$sex,
+                  race        = V(lcc)$race,
+                  age         = V(lcc)$age,
+                  gang.member = V(lcc)$gang.member,
+                  gang.name   = V(lcc)$gang.name,
+#                   fname       = V(lcc)$faction.name,
+                  deg         = degree(lcc),
+                  bw          = bw,
+                  n1v         = n1vics,
+                  n2v         = n2vics
+  )
+data$age = as.numeric(data$age)
+data$gname = as.factor(data$gname)
+
+train = 1:dim(data)[1]; test = 1:dim(data)[1]
+# train = sort(union(sample(vics,length(vics)*0.8),sample(nonvics,length(nonvics)*0.8)))
+# test = sort((1:length(is_vic))[-train])
+
+formula = vic ~ sex + race + age + gmember + gname + deg + bw + n1v + n2v
+formula = vic ~ sex + race + age + gmember + gname
+formula = vic ~ deg + bw
+formula = vic ~ n1v + n2v
+
+glm.fit = glm(formula, data=data, family=binomial)
+glm.probs = predict(glm.fit, newdata=data, type='response')
+roc = roc(data$vic[test],glm.probs,ci=TRUE)
+plot(roc,print.auc=TRUE)
+roc$auc
+
+lm.fit = lm(formula, data=data)
+lm.probs = predict(lm.fit, newdata=data, type='response')
+p = 1/(1+exp(-lm.probs))
+z = log(lm.probs/(1-lm.probs))
+logit = 1/(1+exp(-z))
+
+save(glm.probs, file='Results/lm-probs.RData')
+
+##########
+lm.pred = lm.probs>thresh
+tab = table(lm.pred,data$vic)
+tab
+(tab[1,1]+tab[2,2])/sum(tab)
+
+propensity = as.numeric(pred)
+similarity = 1 - abs(propensity[u]-propensity[v])
+
+###############
+# Random forest approach -- not as good
+library(randomForest)
+data$vic = factor(data$vic)
+rf.fit = randomForest(formula,data=data[train,],do.trace=50,ntree=20)
+rf.probs = (predict(rf.fit,newdata=data[test,],type='prob'))[,1]
+roc = roc(data$vic[test],rf.probs,ci=TRUE)
+plot(roc,print.auc=TRUE)
+