updates to the paper

auto/paper.el

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+(TeX-add-style-hook
+ "paper"
+ (lambda ()
+   (TeX-run-style-hooks
+    "latex2e"
+    "IEEEtran"
+    "IEEEtran10"))
+ :latex)
+

model.r.orig

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+########################
+## Modeling code file ##
+########################
+
+## load packages
+library(lme4)
+
+## load data
+load('all_data')
+all_data$desert <- as.numeric(all_data$desert)
+
+model_data <- subset(all_data, TOTAL.POPULATION > 0)
+
+
+######################
+## Complete pooling ##
+######################
+
+cp <- glm(desert ~ CTA_counts + vacant_counts, family = 'binomial', data = model_data)
+summary(cp)
+################
+## No pooling ##
+################
+
+np <- glm(desert ~ CTA_counts + vacant_counts + Neighborhood, family = 'binomial', data = model_data)
+summary(np)
+
+#####################
+## Partial pooling ##
+#####################
+
+pp <- glmer(desert ~ CTA_counts + vacant_counts + (1 | Neighborhood), data = model_data, family = 'binomial')
+summary(pp)
+
+##################
+## Hierarchical ##
+##################
+
+## mlm <- glmer(desert ~ CTA_counts + vacant_counts + Diabetes.related + (1 | Neighborhood),
+##              data = model_data,
+##              family = 'binomial')
+## summary(mlm)
+
+## mlm_2 <- glmer(desert ~ CTA_counts + vacant_counts + Diabetes.related + Below.Poverty.Level + (1 | Neighborhood),
+##              data = model_data,
+##              family = 'binomial')
+## summary(mlm_2)
+
+
+## rescale variables
+exclude <- c('Neighborhood', 'TRACT_BLOC','STATEFP10', 'COUNTYFP10', 'TRACTCE10', 'BLOCKCE10', 'GEOID10', 'NAME10', 'Longitude', 'Latitude', 'Community.Area.y', 'nearest_supermarket', 'Community.Area.x', 'store_counts', 'desert')
+potential_covariates <- setdiff(names(all_data), exclude)
+model_data_scale <- model_data
+for (var in potential_covariates) {model_data_scale[var] <- as.numeric(scale(model_data[var]))}
+
+## mlm_c <- glmer(desert ~ CTA_counts + vacant_counts + Diabetes.related + (1 | Neighborhood),
+##              data = model_data_scale,
+##              family = 'binomial')
+## summary(mlm_c)
+
+## mlm_c_2 <- glmer(desert ~ CTA_counts + vacant_counts + Diabetes.related + Below.Poverty.Level + (1 | Neighborhood),
+##              data = model_data_scale,
+##              family = 'binomial')
+## summary(mlm_c_2)
+
+## mlm_c_3 <- glmer(desert ~ CTA_counts + vacant_counts + Diabetes.related + Below.Poverty.Level + NHB_p + PER.CAPITA.INCOME + (1 | Neighborhood),
+##              data = model_data_scale,
+##              family = 'binomial')
+## summary(mlm_c_3)
+
+## mlm_c_4 <- glmer(desert ~ CTA_counts + vacant_counts + Diabetes.related + Below.Poverty.Level + NHB_p + PER.CAPITA.INCOME + HISP_p + (1 | Neighborhood),
+##              data = model_data_scale,
+##              family = 'binomial')
+## summary(mlm_c_4)
+
+## mlm_c_5 <- glmer(desert ~ CTA_counts + vacant_counts + Diabetes.related + Below.Poverty.Level + NHB_p + PER.CAPITA.INCOME + HISP_p + TOTAL.POPULATION + (1 | Neighborhood),
+##              data = model_data_scale,
+##              family = 'binomial')
+## summary(mlm_c_5)
+
+## mlm_c_6 <- glmer(desert ~ CTA_counts + vacant_counts + Dependency + NHB_p  + TOTAL.POPULATION + (1 | Neighborhood),
+##              data = model_data_scale,
+##              family = 'binomial')
+## summary(mlm_c_6)
+
+## mlm_c_7 <- glmer(desert ~ CTA_counts + vacant_counts + Dependency  + HISP_p  + TOTAL.POPULATION + (1 | Neighborhood),
+##              data = model_data_scale,
+##              family = 'binomial')
+## summary(mlm_c_7)
+
+## mlm_c_8 <- glmer(desert ~ CTA_counts + vacant_counts + Dependency + Cancer..All.Sites. + HISP_p + TOTAL.POPULATION + (1 | Neighborhood),
+##              data = model_data_scale,
+##              family = 'binomial')
+## summary(mlm_c_8)
+
+## mlm_c_9 <- glmer(desert ~ CTA_counts + vacant_counts + Dependency + Cancer..All.Sites. + NHB_p +  TOTAL.POPULATION + (1 | Neighborhood),
+##              data = model_data_scale,
+##              family = 'binomial')
+## summary(mlm_c_9)
+
+## mlm_c_10 <- glmer(desert ~ CTA_counts + vacant_counts + Dependency + Cancer..All.Sites. + Diabetes.related + TOTAL.POPULATION + (1 | Neighborhood),
+##              data = model_data_scale,
+##              family = 'binomial')
+## summary(mlm_c_10)
+
+cor(model_data[, c('Diabetes.related', 'NHB_p', 'NHW_p', 'HISP_p', 'PER.CAPITA.INCOME')])
+
+search_covariates <- setdiff(potential_covariates, c('vacant_counts', 'CTA_counts', 'Community.Area.Number'))
+
+best_model <- glmer(desert ~ vacant_counts + CTA_counts + (1 | Neighborhood),
+                    data = model_data_scale,
+                    family = 'binomial')
+
+in_vars <- c()
+out_vars <- search_covariates
+
+library(parallel)
+library(doMC)
+
+registerDoMC(detectCores() - 3)
+
+old_aic <- AIC(best_model)
+
+fit_model <- function(i) {
+    print(paste('fitting:',i))
+    form <- as.formula(paste('desert ~ vacant_counts + CTA_counts +',
+                             paste(c(in_vars, out_vars[i]), collapse = '+'),
+                             '+(1|Neighborhood)'))
+    model <- glmer(form, data = model_data_scale, family = 'binomial')
+    return(c(i, AIC(model)))
+}
+
+while(TRUE) {
+    search_results <- foreach(i=1:length(out_vars), .combine = 'rbind') %dopar% fit_model(i)
+    min_aic <- which.min(search_results[,2])
+    if (min_aic < old_aic) {
+        print(paste('ADDING:',out_vars[min_aic]))
+        in_vars <- c(in_vars, out_vars[min_aic])
+        out_vars <- setdiff(out_vars, out_vars[min_aic])
+        print(search_results[min_aic,])
+    }
+    else {
+        break
+    }
+}
+print(paste('Final model:', in_vars, collapse = ', '))
+
+<<<<<<< HEAD
+model <- glmer(desert ~ CTA_counts + vacant_counts + Gonorrhea.in.Females + Cancer..All.Sites. + TOTAL.POPULATION + NHAS +
+                   Dependency +Childhood.Lead.Poisoning + Prenatal.Care.Beginning.in.First.Trimester + Gonorrhea.in.Males
+               + NHAM_p + Multiple.Race.. + Stroke..Cerebrovascular.Disease. + Firearm.related + Tuberculosis + NHW_p +
+               Teen.Birth.Rate + No.High.School.Diploma + Lung.Cancer +
+               (1|Neighborhood),
+               data = model_data_scale,
+               family = 'binomial')
+summary(model)
+=======
+form <- as.formula(paste('desert ~ vacant_counts + CTA_counts +',
+                         paste(in_vars, collapse = '+'),
+                         '+(1|Neighborhood)'))
+final_model <- glmer(form, data = model_data_scale, family = 'binomial')
+save(final_model, 'final_model')
+>>>>>>> fb724787ed67089995bb241b17b3acfa6bf91121

paper.aux

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+\relax 
+\@writefile{toc}{\contentsline {section}{\numberline {I}Introduction}{1}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {\unhbox \voidb@x \hbox {I-A}}Food Deserts}{1}}
+\@writefile{toc}{\contentsline {section}{\numberline {II}Methods}{1}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {\unhbox \voidb@x \hbox {II-A}}Data Gathering and Manipulation}{1}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {\unhbox \voidb@x \hbox {II-A}1}Block level data}{1}}
+\@writefile{toc}{\contentsline {paragraph}{\numberline {\unhbox \voidb@x \hbox {II-A}1a} Crimes 2001 - present}{1}}
+\@writefile{toc}{\contentsline {paragraph}{\numberline {\unhbox \voidb@x \hbox {II-A}1b} 311 Service Requests: Vacant Buildings}{1}}
+\@writefile{toc}{\contentsline {paragraph}{\numberline {\unhbox \voidb@x \hbox {II-A}1c} CTA Ridership: Avg. Weekly Boardings during October 2010}{1}}
+\@writefile{toc}{\contentsline {paragraph}{\numberline {\unhbox \voidb@x \hbox {II-A}1d} Census Block Population }{1}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {\unhbox \voidb@x \hbox {II-A}2}Neighborhood level data}{1}}
+\@writefile{toc}{\contentsline {paragraph}{\numberline {\unhbox \voidb@x \hbox {II-A}2a} Public Health Statistics: selected public health indicators by Chicago community area}{1}}
+\@writefile{toc}{\contentsline {paragraph}{\numberline {\unhbox \voidb@x \hbox {II-A}2b} Census Data: Selected socioeconomic indicators }{1}}
+\@writefile{toc}{\contentsline {paragraph}{\numberline {\unhbox \voidb@x \hbox {II-A}2c} Race by Community Area }{2}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {\unhbox \voidb@x \hbox {II-B}}Generalized Linear Models}{2}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {\unhbox \voidb@x \hbox {II-C}}Hierarchical Models}{2}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {\unhbox \voidb@x \hbox {II-D}}Models Fit}{2}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {\unhbox \voidb@x \hbox {II-D}1}Complete Pooling}{2}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {\unhbox \voidb@x \hbox {II-D}2}No Pooling}{2}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {\unhbox \voidb@x \hbox {II-D}3}Partial pooling}{2}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {\unhbox \voidb@x \hbox {II-D}4}Hierarchical}{2}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {\unhbox \voidb@x \hbox {II-E}}Model Comparison}{2}}
+\@writefile{toc}{\contentsline {section}{\numberline {III}Results}{2}}
+\@writefile{toc}{\contentsline {subsection}{\numberline {\unhbox \voidb@x \hbox {III-A}}Model Parameters}{2}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {\unhbox \voidb@x \hbox {III-A}1}No Pooling}{2}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {\unhbox \voidb@x \hbox {III-A}2}Complete Pooling}{2}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {\unhbox \voidb@x \hbox {III-A}3}Partial Pooling}{2}}
+\@writefile{toc}{\contentsline {subsubsection}{\numberline {\unhbox \voidb@x \hbox {III-A}4}Hierarchical}{2}}
+\@writefile{toc}{\contentsline {section}{\numberline {IV}Conclusions}{2}}