Receives files formerly in mxDistritos, none modified except .gitignore

.gitignore

@@ -5,6 +5,7 @@ code/stata/
 colindanciasMun/
 data/deprecated/
 data/xport/
+data/v-hats-etc/too-big-4-github
 datosBrutos/not-in-git
 datosBrutos/acuerdosCoalicion/not-in-git
 datospibe/
@@ -82,6 +83,13 @@ tramp
 # AUCTeX auto folder
 /auto/
 
+# tex files
+*.aux
+*.bbl
+*.blg
+*.log
+*.out
+
 # cask packages
 .cask/
 dist/

code/v-hat-etc/0readme-use-linzer-mixture-to-get-mg-effect-betahat.r

@@ -0,0 +1,73 @@
+Para interpretar el electo marginal de betahat tendré que importar el método de Linzer.
+
+Intenté usar mean_t(mean_u(r.pan)) +/- media sd pero llego al problema de cómo jugar con morena y oth simultáneamente.
+Linzer usa el mixture model para determinar cómo co-varían las composiciones. Eso es lo que debo usar.
+
+Lo que intenté:
+1. determinar valor típico de v.hat para el municipio --- quizás mean sum_t (v.hat_t) / 10
+2. determinar desviación típica --- +/-5 ó +/- 1sd
+3. hacer sims object con estos valores
+4. montecarlo prediction
+
+rm(list = ls())
+dd <- c("~/Dropbox/data/elecs/MXelsCalendGovt/elecReturns/data/casillas/")
+wd <- c("~/Dropbox/data/elecs/MXelsCalendGovt/redistrict/ife.ine/")
+setwd(wd)
+load("data/too-big-4-github/tmp3.RData")
+
+sel.col <- grep("mean", colnames(yr.means))
+apply(yr.means[,sel.col], 2, mean)
+apply(yr.means[,sel.col], 2, sd)
+
+apply(yr.means[,sel.col], 2, mean) + apply(yr.means[,sel.col], 2, sd)/2
+apply(yr.means[,sel.col], 2, mean) - apply(yr.means[,sel.col], 2, sd)/2
+
+i <- 1
+data.tmp <- extendCoal[[i]]
+summary(mean.regs$pan)
+reg.tmp <- mean.regs[[i]]
+
+r.bar.bar
+
+# std error version
+sims <- with(data.tmp,
+              data.frame(dsameCoal=c(0,1),
+                         dmultiRef=0,
+#                         dmocion= 0,
+                         drefHda=0,
+                         dmajSen=0,
+                         dinSen=0,
+                         legyrR=seq(from=(min(legyrR)-.05), to=(max(legyrR)+.05), length.out = 100),
+                         legyrR2=seq(from=(min(legyrR)-.05), to=(max(legyrR)+.05), length.out = 100)^2,
+                         dreform2010=0,
+                         netApprovR=0.33, # median(netApprovR),
+#                         yr14 = 3,
+                         legis = 2006
+                         )
+              )
+sims2$pr <- predict(fit2e, newdata = sims2, type = "response")
+sims2 <- cbind(sims2, predict(fit2e, newdata = sims2, type="link", se=TRUE))
+sims2 <- within(sims2, {
+  PredictedProb <- plogis(fit)
+  LL <- plogis(fit - (1.96 * se.fit))
+  UL <- plogis(fit + (1.96 * se.fit))
+})
+sims2$legyr <- seq(from=1, to=0, length.out = 100) # for plot
+head(sims2)
+library(ggplot2)
+gr <- "../graphs/"
+#pdf (file = paste(gr, "predictedPr.pdf", sep = ""), width = 7, height = 4)
+ggplot(sims2, aes(x = legyr, y = PredictedProb)) +
+    geom_ribbon(aes(ymin = LL, ymax = UL, fill = factor(dsameCoal)), alpha = .2) +
+    geom_line(aes(colour = factor(dsameCoal)), size=1) +
+    labs(fill = "Coalition chair", colour = "Coalition chair",
+         x = "Legislative year remaining (in months)",
+         y = "Predicted probability") +
+    scale_x_continuous(breaks=seq(from=0, to=1, length.out=7), labels=seq(from=12, to=0, by=-2))
+#dev.off()
+
+
+
+
+
+