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01MasterScript.R
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01MasterScript.R
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###########################General Description##################################
# Updated version of the R code for the analysis in my PhD final paper
# Currently under review.
# Update: 15 January 2021
# The scrip here includes modeling the relations among:
# Greenspace exposure, Years of Potential Life lost, and social deprivation.
# Non-spatial and spatial regression models have been utilized here
# The models considered the spatial decencies and auto-correlations
############## LOAD THE PACKAGES ##############################################
#Spatial packages
library(sf)
library(sp)
library(spdep)
library(spatialreg)
library(tmap)
#non-spatial packages
library(tidyverse)
library(car)
library(MASS)
library(broom)
library(DescTools)
library(mediation)
library(mvtnorm)
library(sandwich)
#Plotting related
library(RColorBrewer)
library(ggplot2)
library(jtools)
library(ggstance)
library(sjPlot)
#Clear list and set seed
rm(list=ls())
set.seed(123)
############## DATA, Inputs and exploration #######################################
#Data file
CompositeLSOA <- read_sf(dsn = "inputs", layer = "CompositefulldataF")
plot(CompositeLSOA)
#key variable
YPLL <- CompositeLSOA$Yrpotlife
CompositeLSOA$YPLL <- YPLL
summary (CompositeLSOA$Yrpotlife)
#map key variable
tm_shape(CompositeLSOA) +
tm_fill("YPLL", title = "YPLL per 1000 people", palette = "-RdBu") +
tm_borders(alpha = 0.1) +
tm_layout(main.title = "YPLL Distribution", main.title.size = 0.9 ,
legend.position = c("left", "bottom"), legend.title.size = 0.8) +
tm_scale_bar(breaks= 10, text.size = 0.7,position=c("right", "bottom")) +
tm_compass(type="4star", position=c("left", "top"), show.labels = 1)
#drop the geometry to use it as main data frame
CorreCompositeLSOA <- CompositeLSOA %>%
st_drop_geometry() %>%
select(Compositem, GVILSOA, AccessS, AvailComN)
#check correlations
cor(CorreCompositeLSOA)
#Neighboirhoods and spatial weight metrix
compositeLSOA_nbq <- poly2nb(CompositeLSOA, queen=TRUE) #Queen’s neighborhood
summary(compositeLSOA_nbq)
compositeLSOA_nbq_w <- nb2listw(compositeLSOA_nbq, style="W") #Queen’s neighborhood wights
summary(compositeLSOA_nbq_w)
######################01 Non-spatial models for all LSOA######################################
#Dependent Yrpotlife
#01.1 Model for NDVI
modelLSOA_NDVI_yUnadj <- glm(Yrpotlife~ NDVIm,
family = Gamma(link = "identity"), data= CompositeLSOA)
summary(modelLSOA_NDVI_yUnadj, Nagelkerke=T)
modelLSOA_NDVI_y <- glm(Yrpotlife~ NDVIm + Income_Sco + Crime_Scor +SHDiv,
family = Gamma (link = "identity"), data= CompositeLSOA)
summary(modelLSOA_NDVI_y, Nagelkerke=T)
#plot coeff
plot_summs(modelLSOA_NDVI_yUnadj, modelLSOA_NDVI_y, scale = TRUE)
#test autocorrelations in the residuals
moran.mc(modelLSOA_NDVI_y$residuals, compositeLSOA_nbq_w, 999)
#01.2 Model for green availability
#unadjusted
modelLSOA_GAVI_yUnadj <- glm(Yrpotlife~ AvailComN,
family = Gamma(link = "identity"), data= CompositeLSOA)
summary(modelLSOA_GAVI_yUnadj, Nagelkerke=T)
PseudoR2(modelLSOA_GAVI_yUnadj, c("McFadden", "Nagel"))
#adjusted
modelLSOA_GAVI_y <- glm(Yrpotlife~ AvailComN + Income_Sco + Crime_Scor +SHDiv,
family = Gamma (link = "identity"), data= CompositeLSOA)
summary(modelLSOA_GAVI_y)
summ(modelLSOA_GAVI_y)
PseudoR2(modelLSOA_GAVI_y, c("McFadden", "Nagel"))
moran.mc(modelLSOA_GAVI_y$residuals, compositeLSOA_nbq_w, 999)
#Test Lagrange Multiplier diagnostics for spatial dependence
LMtestGAVI <- lm.LMtests (modelLSOA_GAVI_y, compositeLSOA_nbq_w, test=c("LMerr", "LMlag","SARMA"))
summary(LMtestGAVI)
# 01.3 Model for green accessibility
modelLSOA_GAC_yUnadj <- glm(Yrpotlife~ AccessS,
family = Gamma(link = "identity"), data= CompositeLSOA)
summary(modelLSOA_GAC_yUnadj)
#adjusted
modelLSOA_GAC_y <- glm(Yrpotlife~ AccessS + Income_Sco + Crime_Scor + SHDiv,
family = Gamma(link = "identity"), data= CompositeLSOA)
summary(modelLSOA_GAC_y)
PseudoR2(modelLSOA_GAC_y, c("McFadden", "Nagel"))
moran.mc(modelLSOA_GAC_y$residuals, compositeLSOA_nbq_w, 999)
#Test Lagrange Multiplier diagnostics for spatial dependence
LMtestGAC <- lm.LMtests (modelLSOA_GAC_y, compositeLSOA_nbq_w, test=c("LMerr", "LMlag","SARMA"))
summary(LMtestGAC)
#01.4 Model for green visibility
modelLSOA_VGVI_y <- glm(Yrpotlife~ Income_Sco + Crime_Scor + SHDiv + GVILSOA,
family = Gamma(link = "identity"), data= CompositeLSOA)
summary(modelLSOA_VGVI_y)
PseudoR2(modelLSOA_VGVI_y, c("McFadden", "Nagel"))
moran.mc(modelLSOA_VGVI_y$residuals, compositeLSOA_nbq_w, 999)
LMtestVGVI <- lm.LMtests (modelLSOA_VGVI_y, compositeLSOA_nbq_w, test=c("LMerr", "LMlag","SARMA"))
summary(LMtestVGVI)
#01.5 Model for green composite greenness index
modelLSOA_CEGI_y <- glm(Yrpotlife~ Income_Sco + Crime_Scor + SHDiv+ Compositem,
family = Gamma(link = "identity"), data= CompositeLSOA)
summary(modelLSOA_CEGI_y)
PseudoR2(modelLSOA_CEGI_y, c("McFadden", "Nagel"))
LMtestCGEI <- lm.LMtests (modelLSOA_CEGI_y, compositeLSOA_nbq_w, test=c("LMerr", "LMlag","SARMA"))
summary(LMtestCGEI)
plot(modelLSOA_CEGI_y$residuals)
vif(modelLSOA_CEGI_y)
summ(modelLSOA_CEGI_y)
moran.mc(modelLSOA_CEGI_y$residuals, compositeLSOA_nbq_w, 999)
CompositeLSOA$resmodelLSOA_CEGI_y <- residuals(modelLSOA_CEGI_y)
tm_shape(CompositeLSOA) +
tm_fill("resmodelLSOA_CEGI_y", title = "GLM Residuals", palette = "-RdBu") +
tm_borders(alpha = 0.1) +
tm_layout(main.title = "GLM model Residuals", main.title.size = 0.9 ,
legend.position = c("left", "bottom"), legend.title.size = 0.8)
######################################################################################
#--------------------------SPATIAL MODELS For all LSOA-------------------------------####
#INDEPENDENT
# Avilability: AvailComN
# Accessibility: AccessS
# Visibility: GVILSOA
# Composite: Compositem
# Possible Confounding adjustment
# Income Deprivation: Income_Sco
# Air pollution: pm10_mean_
# Eduction Deprivation: Education_ (highly multi-colinar with Income)
# Crime Score: Crime_Scor (Not high VIF, but has effect of model residual)
# Barriers to Housing: Barriers_H (Somewhat multicolinar)
# Diversity: SHDiv
#CompositeLSOA$Crime_Scor
#CompositeLSOA$Comilldis
#Dependent: Yrpotlife
#Model for NDVI
Spatial_LAG_NDVI_yUnadj <- sacsarlm (Yrpotlife ~ NDVIm,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_NDVI_yUnadj, Nagelkerke=T)
moran.mc(Spatial_LAG_NDVI_yUnadj$residuals, compositeLSOA_nbq_w, 999)
#adjusted
Spatial_LAG_NDVI_y <- sacsarlm (Yrpotlife ~ NDVIm + Income_Sco + Crime_Scor + SHDiv,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_NDVI_y, Nagelkerke=T)
moran.mc(Spatial_LAG_NDVI_y$residuals, compositeLSOA_nbq_w, 999)
#try plot the model coeff
plot_summs(Spatial_LAG_NDVI_yUnadj, Spatial_LAG_NDVI_y, coefs = c("NDVIm"), scale = TRUE, robust = list("HC0", "HC3"))
#Model for green availability
Spatial_LAG_GAVI_yUnadj <- sacsarlm (Yrpotlife ~ AvailComN,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
confint(Spatial_LAG_GAVI_yUnadj, level = 0.95)
summary(Spatial_LAG_GAVI_yUnadj, Nagelkerke=T)
#for standardized beta
Spatial_LAG_GAVI_yUnadjSc <- sacsarlm (scale(Yrpotlife) ~ scale(AvailComN),
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_GAVI_yUnadjSc, Nagelkerke=T)
moran.mc(Spatial_LAG_GAVI_yUnadj$residuals, compositeLSOA_nbq_w, 999)
#adjusted
Spatial_LAG_GAVI_y <- sacsarlm (Yrpotlife ~ Income_Sco + Crime_Scor + SHDiv + AvailComN,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_GAVI_y, Nagelkerke=T)
confint(Spatial_LAG_GAVI_y, level = 0.95)
#for standardized beta
Spatial_LAG_GAVI_ySc <- sacsarlm (scale(Yrpotlife) ~ scale(Income_Sco) + scale(Crime_Scor) + scale(SHDiv) + scale(AvailComN),
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_GAVI_ySc, Nagelkerke=T)
moran.mc(Spatial_LAG_GAVI_y$residuals, compositeLSOA_nbq_w, 999)
#Model for green accessibility
Spatial_LAG_GAC_yUnadj <- sacsarlm (Yrpotlife ~ AccessS,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
confint(Spatial_LAG_GAC_yUnadj, level = 0.95)
summary(Spatial_LAG_GAC_yUnadj , Nagelkerke=T)
moran.mc(Spatial_LAG_GAC_yUnadj$residuals, compositeLSOA_nbq_w, 999)
Spatial_LAG_GAC_y <- sacsarlm (Yrpotlife ~ Income_Sco + Crime_Scor + SHDiv + AccessS,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
confint(Spatial_LAG_GAC_y, level = 0.95)
summary(Spatial_LAG_GAC_y, Nagelkerke=T)
moran.mc(Spatial_LAG_GAC_y$residuals, compositeLSOA_nbq_w, 999)
#Model for green visibility
Spatial_LAG_VGVI_yUnadj <- sacsarlm (Yrpotlife ~ GVILSOA,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_VGVI_yUnadj, Nagelkerke=T)
confint(Spatial_LAG_VGVI_yUnadj, level = 0.95)
moran.mc(Spatial_LAG_VGVI_yUnadj$residuals, compositeLSOA_nbq_w, 999)
Spatial_LAG_VGVI_y <- sacsarlm (Yrpotlife ~ Income_Sco + Crime_Scor + SHDiv + GVILSOA,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_VGVI_y, Nagelkerke=T)
confint(Spatial_LAG_VGVI_y, level = 0.95)
moran.mc(Spatial_LAG_VGVI_y$residuals, compositeLSOA_nbq_w, 999)
#Model for green composite greenness index
#Specification and Estimation of Spatial Autoregressive Models/ Spatial autoregressive combined model (both side take account of lag)
Spatial_LAG_CEGI_yUnadj <- sacsarlm (Yrpotlife ~ Compositem,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_CEGI_yUnadj, Nagelkerke=T)
confint(Spatial_LAG_CEGI_yUnadj, level = 0.95)
moran.mc(Spatial_LAG_CEGI_yUnadj$residuals, compositeLSOA_nbq_w, 999)
Spatial_LAG_CEGI_yscal <- sacsarlm (scale(Yrpotlife) ~ scale(Income_Sco) + scale (Crime_Scor) + scale(SHDiv) + scale (Compositem),
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_CEGI_yscal, Nagelkerke=T)
Spatial_LAG_CEGI_y <- sacsarlm (Yrpotlife ~ Income_Sco + Crime_Scor+ SHDiv + Compositem,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_CEGI_y, Nagelkerke=T)
confint(Spatial_LAG_CEGI_y, level = 0.95)
Spatial_LAG_CEGI_ySc <- sacsarlm (scale(Yrpotlife) ~ scale(Income_Sco) + scale(Crime_Scor) + scale(SHDiv)+ scale(Compositem),
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_CEGI_ySc, Nagelkerke=T)
moran.mc(Spatial_LAG_CEGI_y$residuals, compositeLSOA_nbq_w, 999)
Spatial_LAG_CEGI_ylm <- lm (Yrpotlife ~ Income_Sco + Crime_Scor+ SHDiv + Compositem,
data = CompositeLSOA)
summary (Spatial_LAG_CEGI_ylm )
library(reghelper)
beta (modelLSOA_CEGI_y)
######## plotting the effect sizes with 95% ci ########
Spatial_LAG_CEGI_ylmrespre <- Spatial_LAG_CEGI_ylm [["model"]]
write.csv(Spatial_LAG_CEGI_ylmrespre, "C:\\Users\\ME\\Desktop\\linearCGEI.csv")
groupX <- list(Opportunity = c("Availability", "Accessibility"),
Total = c("GVILSOA", "Compositem"))
colorx <- c("blue","red","blue", "red","blue", "red","blue", "red")
plot_coefs(Spatial_LAG_GAVI_yUnadj, Spatial_LAG_GAVI_y,Spatial_LAG_GAC_yUnadj,
Spatial_LAG_GAC_y, Spatial_LAG_VGVI_yUnadj, Spatial_LAG_VGVI_y, Spatial_LAG_CEGI_yUnadj, Spatial_LAG_CEGI_y,
coefs = c("Availability" = "AvailComN", "Accessibility" = "AccessS", "Visibility" = "GVILSOA","Composite"= "Compositem"),
scale = TRUE, robust = TRUE, point.shape = FALSE,
show.p = TRUE, groups = groupX,
facet.rows = 2,
legend.title = "Exposure Models", show.legend = FALSE,
model.names = c("Available Unadj", "Available Adj", "Access-U", "Access-A", "VGVI-U", "VGVI-A", "Comp-U", "Comp-A"),
colors = colorx)
effect_plot (model = Spatial_LAG_CEGI_ylm, pred = Compositem, interval = TRUE)
export_summs (Spatial_LAG_VGVI_yUnadj, Spatial_LAG_VGVI_y)
plot_coefs(Spatial_LAG_GAVI_yUnadj, Spatial_LAG_GAVI_y, Spatial_LAG_VGVI_yUnadj, Spatial_LAG_VGVI_y,
coefs = c("AvailComN", "AccessS", "GVILSOA", "Compositem"),
scale = TRUE, robust = TRUE, colors = "Rainbow", point.shape = FALSE
)
confint(Spatial_LAG_VGVI_y, level = 0.95)
Spatial_LAG_CEGI_ysub <- Spatial_LAG_CEGI_ylm [c(1:12)]
plot_model(modelLSOA_CEGI_y)
groupX2 <- list("Population Level" = c("Availability", "Accessibility", "Visibility", "Composite"),
"Sub Group Composite" = c("Group5", "Group4", "Group3", "Group2", "Group1"))
colorx2 <- c("#377eb8","#4daf4a", "#377eb8","#4daf4a", "#377eb8","#4daf4a", "#377eb8","#4daf4a",
"#377eb8","#4daf4a", "#377eb8","#4daf4a", "#377eb8","#4daf4a", "#377eb8","#4daf4a",
"#377eb8","#4daf4a")
plot_coefs (Spatial_LAG_GAVI_yUnadj, Spatial_LAG_GAVI_y,Spatial_LAG_GAC_yUnadj,
Spatial_LAG_GAC_y, Spatial_LAG_VGVI_yUnadj, Spatial_LAG_VGVI_y, Spatial_LAG_CEGI_yUnadj, Spatial_LAG_CEGI_y,
LSOA_IMD_1_2SReu, LSOA_IMD_1_2SRe, LSOA_IMD_3_4SReu,LSOA_IMD_3_4SRe,
LSOA_IMD_5_6SReu, LSOA_IMD_5_6SRe, LSOA_IMD_7_8SReu, LSOA_IMD_7_8SRe, LSOA_IMD_9_10SReu, LSOA_IMD_9_10SRe,
coefs = c("Availability" = "AvailComN", "Accessibility" = "AccessS", "Visibility" = "GVILSOA","Composite"= "Compositem","Group5" = "Group5", "Group4" = "Group4",
"Group3" = "Group3", "Group2" = "Group2", "Group1" = "Group1"),
scale = TRUE, robust = TRUE, point.shape = FALSE,
groups = groupX2,
facet.rows = 2, facet.label.pos = "right",
legend.title = "Exposure Models",
model.names = c("Available Unadj", "Available Adj", "Access-U", "Access-A", "VGVI-U", "VGVI-A", "Comp-U", "Comp-A",
"Group5u", "Group4u", "Group3u", "Group2u", "Group1u",
"Group5a", "Group4a", "Group3a", "Group2a", "Group1a"),
colors = colorx2) + theme(axis.title = element_text(family = "serif"),
plot.title = element_text(family = "serif"),
legend.title = element_text(family = "serif"),
panel.background = element_rect(fill = "aliceblue"),
panel.grid.minor = element_line(colour = "grey90", size = 0.2),
strip.text = element_text(family = "serif")) + theme(axis.text = element_text(size = 12,
colour = "black"), axis.text.x = element_text(size = 12),
axis.text.y = element_text(family = "sans",
size = 12, colour = "black"), panel.background = element_rect(fill = "white"),
legend.position = "none")
#errorsarlm#################################################################
Spatial_LAG_CEGI_ySE <- errorsarlm (Yrpotlife ~ Income_Sco + Crime_Scor+ SHDiv + AvailComN,
data = CompositeLSOA, compositeLSOA_nbq_w)
summary(Spatial_LAG_CEGI_ySE, Nagelkerke=T)
moran.mc(Spatial_LAG_CEGI_ySE$residuals, compositeLSOA_nbq_w, 999)
#test air poll####################################################################
Spatial_LAG_CEGI_yIn <- sacsarlm (Yrpotlife ~ Income_Sco + Crime_Scor+ SHDiv + pm10_mean_ + Compositem,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_CEGI_yIn, Nagelkerke=T)
moran.mc(Spatial_LAG_CEGI_yIn$residuals, compositeLSOA_nbq_w, 999)
Spatial_LAG_GAVI_yPM <- sacsarlm (Yrpotlife ~ Income_Sco + Crime_Scor + SHDiv + pm10_mean_ + AvailComN,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_GAVI_yPM, Nagelkerke=T)
moran.mc(Spatial_LAG_GAVI_yPM$residuals, compositeLSOA_nbq_w, 999)
Spatial_LAG_GAC_yPM <- sacsarlm (Yrpotlife ~ Income_Sco + Crime_Scor + SHDiv + pm10_mean_ + AccessS,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_GAC_yPM, Nagelkerke=T)
moran.mc(Spatial_LAG_GAC_yPM$residuals, compositeLSOA_nbq_w, 999)
Spatial_LAG_VGVI_yPM <- sacsarlm (Yrpotlife ~ Income_Sco + Crime_Scor + SHDiv + pm10_mean_ + GVILSOA,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_VGVI_yPM, Nagelkerke=T)
moran.mc(Spatial_LAG_VGVI_yPM$residuals, compositeLSOA_nbq_w, 999)
W <- as(compositeLSOA_nbq_w, "CsparseMatrix")
trMC <- trW(W, type="MC")
im<-impacts(Spatial_LAG_CEGI_y, tr=trMC, R= 999)
summary(im, zstats=T)
Spatial_LAG_CEGI_y$residuals <- residuals(Spatial_LAG_CEGI_y)
plot(Spatial_LAG_CEGI_y$residuals)
bptest.sarlm (Spatial_LAG_CEGI_y)
set.seed(123)
moran.mc(Spatial_LAG_CEGI_y$residuals, compositeLSOA_nbq_w, 999)
CompositeLSOA$Spatial_LAG_CEGI__y_cres <- residuals(Spatial_LAG_CEGI_y)
tm_shape(CompositeLSOA) +
tm_fill("Spatial_LAG_CEGI__y_cres", title = "Residuals", palette = "-RdBu") +
tm_borders(alpha = 0.1) +
tm_layout(main.title = "SAC lag model Residuals", main.title.size = 0.9,
legend.position = c("left", "bottom"), legend.title.size = 0.8)
#######################testmediation################################################
greenPM10 <- sacsarlm (pm10_mean_ ~ Compositem + Income_Sco + Crime_Scor + SHDiv,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(greenPM10, Nagelkerke=T)
Spatial_LAG_CEGI_yGLM <- sacsarlm (YPLL ~ Compositem + Income_Sco + Crime_Scor + SHDiv + pm10_mean_,
data = CompositeLSOA, compositeLSOA_nbq_w, type = "sac")
summary(Spatial_LAG_CEGI_yGLM, Nagelkerke=T)
med.output1 <- mediate (greenPM10, Spatial_LAG_CEGI_yGLM, treat = "Compositem", mediator = "pm10_mean_", robustSE = TRUE, sims = 1000)
summary(med.output1)
plot (med.output1)
greenPM102 <- glm (pm10_mean_ ~ Compositem + Income_Sco + Crime_Scor + SHDiv,
family = Gamma(link = "identity"), data = CompositeLSOA)
summary(greenPM102, Nagelkerke=T)
Spatial_LAG_CEGI_y2 <- glm (Yrpotlife ~ Compositem + Income_Sco + Crime_Scor+ SHDiv + pm10_mean_,
family = Gamma(link = "identity"), data = CompositeLSOA)
summary(Spatial_LAG_CEGI_y2, Nagelkerke=T)
med.output2 <- mediate(greenPM102, Spatial_LAG_CEGI_y2, treat = "Compositem", mediator = "pm10_mean_", robustSE = TRUE, sims = 1000)
summary(med.output2)
plot(med.output2)
###################SubGroups###################
# Avilability: AvailComN
# Accessibility: AccessS
# Visibility: GVILSOA
# Composite: Compositem
#---------------IMD_1_2 Analyses--------------#
#Data
LSOA_IMD_1_2 <- read_sf(dsn = "inputs", layer = "Quintile_group_5")
LSOA_IMD_1_2re <- LSOA_IMD_1_2 %>%
rename (Group5 = Compositem)
#nh
LSOA_IMD_1_2_nbq <- poly2nb(LSOA_IMD_1_2, queen=TRUE) #Queen’s neighborhood
LSOA_IMD_1_2_nbq_w <- nb2listw(LSOA_IMD_1_2_nbq, style = "W", zero.policy = TRUE) #Queen’s neighborhood wights
#non-spatial model
model_IMD1_2 <- glm(Yrpotlife~ Compositem + Income_Sco+ Crime_Scor+ SHDiv,
family = Gamma(link = "identity"), data= LSOA_IMD_1_2)
summary(model_IMD1_2)
moran.mc(model_IMD1_2$residuals, LSOA_IMD_1_2_nbq_w, zero.policy = TRUE, 999)
#spatial model
LSOA_IMD_1_2Sav <- sacsarlm (Yrpotlife ~AvailComN + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_1_2, LSOA_IMD_1_2_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_1_2Sav, Nagelkerke=T)
LSOA_IMD_1_2Sac <- sacsarlm (Yrpotlife ~AccessS + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_1_2, LSOA_IMD_1_2_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_1_2Sac, Nagelkerke=T)
LSOA_IMD_1_2Sv <- sacsarlm (Yrpotlife ~GVILSOA + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_1_2, LSOA_IMD_1_2_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_1_2Sv, Nagelkerke=T)
LSOA_IMD_1_2Sua <- sacsarlm (Yrpotlife ~Compositem,
data = LSOA_IMD_1_2, LSOA_IMD_1_2_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_1_2Sua, Nagelkerke=T)
LSOA_IMD_1_2S <- sacsarlm (Yrpotlife ~Compositem + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_1_2, LSOA_IMD_1_2_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_1_2S, Nagelkerke=T)
LSOA_IMD_1_2SRe <- sacsarlm (Yrpotlife ~Group5 + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_1_2re, LSOA_IMD_1_2_nbq_w, type = "sac", zero.policy = TRUE)
LSOA_IMD_1_2SReu <- sacsarlm (Yrpotlife ~Group5,
data = LSOA_IMD_1_2re, LSOA_IMD_1_2_nbq_w, type = "sac", zero.policy = TRUE)
W <- as(LSOA_IMD_1_2_nbq_w, "CsparseMatrix")
trMC <- trW(W, type="MC")
IMD_1_2im <-impacts(LSOA_IMD_1_2S, tr=trMC, R= 999)
summary(IMD_1_2im, zstats=T)
LSOA_IMD_1_2S$residuals <- residuals(LSOA_IMD_1_2S)
plot(LSOA_IMD_1_2S$residuals)
bptest.sarlm (LSOA_IMD_1_2S)
moran.mc(LSOA_IMD_1_2S$residuals, LSOA_IMD_1_2_nbq_w, zero.policy = TRUE, 999)
LSOA_IMD_1_2$LSOA_IMD_1_2Sres <- residuals(LSOA_IMD_1_2S)
tm_shape(LSOA_IMD_1_2) +
tm_fill("LSOA_IMD_1_2Sres", title = "Residuals", style = "fixed", breaks = spa_breaks, palette = "-RdBu") +
tm_borders(alpha = 0.1) +
tm_layout(main.title = "SARAR lag model Residuals", main.title.size = 0.9,
legend.position = c("left", "bottom"), legend.title.size = 0.8)
#---------------IMD_3_4 Analyses--------------#
#Data
LSOA_IMD_3_4 <- read_sf(dsn = "inputs", layer = "Quintile_group_4")
LSOA_IMD_3_4re <- LSOA_IMD_3_4 %>%
rename (Group4 = Compositem)
plot(LSOA_IMD_3_4)
#nh
LSOA_IMD_3_4_nbq <- poly2nb(LSOA_IMD_3_4, queen=TRUE) #Queen’s neighborhood
LSOA_IMD_3_4_nbq_w <- nb2listw(LSOA_IMD_3_4_nbq, style = "W", zero.policy = TRUE) #Queen’s neighborhood wights
summary(LSOA_IMD_3_4_nbq_w)
#non-spatial model
model_IMD_3_4 <- glm(Yrpotlife~ Compositem + Income_Sco + Crime_Scor + SHDiv,
family = Gamma(link = "identity"), data= LSOA_IMD_3_4)
summary(model_IMD_3_4)
plot(model_IMD_3_4)
moran.mc(model_IMD_3_4$residuals, LSOA_IMD_3_4_nbq_w, zero.policy = TRUE, 999)
#spatial model
LSOA_IMD_3_4Sav <- sacsarlm (Yrpotlife ~ AvailComN + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_3_4, LSOA_IMD_3_4_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_3_4Sav, Nagelkerke=T)
LSOA_IMD_3_4Sac <- sacsarlm (Yrpotlife ~ AccessS + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_3_4, LSOA_IMD_3_4_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_3_4Sac, Nagelkerke=T)
LSOA_IMD_3_4Sv <- sacsarlm (Yrpotlife ~ GVILSOA + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_3_4, LSOA_IMD_3_4_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_3_4Sv, Nagelkerke=T)
LSOA_IMD_3_4Sun <- sacsarlm (Yrpotlife ~ Compositem,
data = LSOA_IMD_3_4, LSOA_IMD_3_4_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_3_4Sun, Nagelkerke=T)
LSOA_IMD_3_4S <- sacsarlm (Yrpotlife ~ Compositem + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_3_4, LSOA_IMD_3_4_nbq_w, type = "sac", zero.policy = TRUE)
LSOA_IMD_3_4SRe <- sacsarlm (Yrpotlife ~Group4 + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_3_4re, LSOA_IMD_3_4_nbq_w, type = "sac", zero.policy = TRUE)
LSOA_IMD_3_4SReu <- sacsarlm (Yrpotlife ~Group4,
data = LSOA_IMD_3_4re, LSOA_IMD_3_4_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_3_4S, Nagelkerke=T)
W <- as(LSOA_IMD_3_4_nbq_w, "CsparseMatrix")
trMC <- trW(W, type="MC")
IMD_3_4im <-impacts(LSOA_IMD_3_4S, tr=trMC, R= 999)
summary(IMD_1_2im, zstats=T)
LSOA_IMD_3_4S$residuals <- residuals(LSOA_IMD_3_4S)
plot(LSOA_IMD_3_4S$residuals)
bptest.sarlm (LSOA_IMD_3_4S)
moran.mc(LSOA_IMD_3_4S$residuals, LSOA_IMD_3_4_nbq_w, zero.policy = TRUE, 999)
LSOA_IMD_3_4$LSOA_IMD_3_4Sres <- residuals(LSOA_IMD_3_4S)
tm_shape(LSOA_IMD_3_4) +
tm_fill("LSOA_IMD_3_4Sres", title = "Residuals", style = "fixed", breaks = spa_breaks, palette = "-RdBu") +
tm_borders(alpha = 0.1) +
tm_layout(main.title = "SARAR lag model Residuals", main.title.size = 0.9,
legend.position = c("left", "bottom"), legend.title.size = 0.8)
#---------------IMD_5_6 Analyses--------------#
#Data
LSOA_IMD_5_6 <- read_sf(dsn = "inputs", layer = "Quintile_group_3")
LSOA_IMD_5_6re <- LSOA_IMD_5_6 %>%
rename (Group3 = Compositem)
plot(LSOA_IMD_5_6)
#nh
LSOA_IMD_5_6_nbq <- poly2nb(LSOA_IMD_5_6, queen=TRUE) #Queen’s neighborhood
LSOA_IMD_5_6_nbq_w <- nb2listw(LSOA_IMD_5_6_nbq, style = "W", zero.policy = TRUE) #Queen’s neighborhood wights
summary(LSOA_IMD_5_6_nbq_w)
#non-spatial model
model_IMD_5_6 <- glm(Yrpotlife~ Compositem + Income_Sco + pm10_mean_ + SHDiv,
family = Gamma(link = "identity"), data= LSOA_IMD_5_6)
summary(model_IMD_5_6)
plot(model_IMD_5_6)
moran.mc(model_IMD_5_6$residuals, LSOA_IMD_5_6_nbq_w, zero.policy = TRUE, 999)
#spatial model
LSOA_IMD_5_6Sav <- sacsarlm (Yrpotlife ~ AvailComN + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_5_6, LSOA_IMD_5_6_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_5_6Sav, Nagelkerke=T)
LSOA_IMD_5_6Sac <- sacsarlm (Yrpotlife ~ AccessS + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_5_6, LSOA_IMD_5_6_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_5_6Sac, Nagelkerke=T)
LSOA_IMD_5_6Sac <- sacsarlm (Yrpotlife ~ GVILSOA + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_5_6, LSOA_IMD_5_6_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_5_6Sac, Nagelkerke=T)
LSOA_IMD_5_6Sun <- sacsarlm (Yrpotlife ~ Compositem,
data = LSOA_IMD_5_6, LSOA_IMD_5_6_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_5_6Sun, Nagelkerke=T)
LSOA_IMD_5_6S <- sacsarlm (Yrpotlife ~ Compositem + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_5_6, LSOA_IMD_5_6_nbq_w, type = "sac", zero.policy = TRUE)
LSOA_IMD_5_6SRe <- sacsarlm (Yrpotlife ~ Group3 + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_5_6re, LSOA_IMD_5_6_nbq_w, type = "sac", zero.policy = TRUE)
LSOA_IMD_5_6SReu <- sacsarlm (Yrpotlife ~ Group3,
data = LSOA_IMD_5_6re, LSOA_IMD_5_6_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_5_6S, Nagelkerke=T)
W <- as(LSOA_IMD_5_6_nbq_w, "CsparseMatrix")
trMC <- trW(W, type="MC")
IMD_5_6im <-impacts(LSOA_IMD_5_6S, tr=trMC, R= 999)
summary(IMD_5_6im, zstats=T)
LSOA_IMD_5_6S$residuals <- residuals(LSOA_IMD_5_6S)
plot(LSOA_IMD_5_6S$residuals)
bptest.sarlm (LSOA_IMD_5_6S)
moran.mc(LSOA_IMD_5_6S$residuals, LSOA_IMD_5_6_nbq_w, zero.policy = TRUE, 999)
LSOA_IMD_5_6$LSOA_IMD_5_6Sres <- residuals(LSOA_IMD_5_6S)
tm_shape(LSOA_IMD_5_6) +
tm_fill("LSOA_IMD_5_6Sres", title = "Residuals", style = "fixed", breaks = spa_breaks, palette = "-RdBu") +
tm_borders(alpha = 0.1) +
tm_layout(main.title = "SARAR lag model Residuals", main.title.size = 0.9,
legend.position = c("left", "bottom"), legend.title.size = 0.8)
#---------------IMD_7_8 Analyses--------------#
#Data
LSOA_IMD_7_8 <- read_sf(dsn = "inputs", layer = "Quintile_group_2")
LSOA_IMD_7_8re <- LSOA_IMD_7_8 %>%
rename (Group2 = Compositem)
plot(LSOA_IMD_7_8)
#nh
LSOA_IMD_7_8_nbq <- poly2nb(LSOA_IMD_7_8, queen=TRUE) #Queen’s neighborhood
LSOA_IMD_7_8_nbq_w <- nb2listw(LSOA_IMD_7_8_nbq, style = "W", zero.policy = TRUE) #Queen’s neighborhood wights
summary(LSOA_IMD_7_8_nbq_w)
#non-spatial model
model_IMD_7_8 <- glm(Yrpotlife~ AccessS + Income_Sco + Crime_Scor + SHDiv,
family = Gamma(link = "identity"), data= LSOA_IMD_7_8)
summary(model_IMD_7_8)
plot(model_IMD_7_8)
moran.mc(model_IMD_7_8$residuals, LSOA_IMD_7_8_nbq_w, zero.policy = TRUE, 999)
#spatial model
LSOA_IMD_7_8Sav <- sacsarlm (Yrpotlife ~ AvailComN + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_7_8, LSOA_IMD_7_8_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_7_8Sav, Nagelkerke=T)
LSOA_IMD_7_8Sac <- sacsarlm (Yrpotlife ~ AccessS + Income_Sco + Crime_Scor+ SHDiv,
data = LSOA_IMD_7_8, LSOA_IMD_7_8_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_7_8Sac, Nagelkerke=T)
LSOA_IMD_7_8Sv <- sacsarlm (Yrpotlife ~ GVILSOA + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_7_8, LSOA_IMD_7_8_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_7_8Sv, Nagelkerke=T)
LSOA_IMD_7_8Sun <- sacsarlm (Yrpotlife ~ Compositem,
data = LSOA_IMD_7_8, LSOA_IMD_7_8_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_7_8Sun, Nagelkerke=T)
LSOA_IMD_7_8S <- sacsarlm (Yrpotlife ~ Compositem + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_7_8, LSOA_IMD_7_8_nbq_w, type = "sac", zero.policy = TRUE)
LSOA_IMD_7_8SRe <- sacsarlm (Yrpotlife ~ Group2 + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_7_8re, LSOA_IMD_7_8_nbq_w, type = "sac", zero.policy = TRUE)
LSOA_IMD_7_8SReu <- sacsarlm (Yrpotlife ~ Group2,
data = LSOA_IMD_7_8re, LSOA_IMD_7_8_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_7_8S, Nagelkerke=T)
W <- as(LSOA_IMD_7_8_nbq_w, "CsparseMatrix")
trMC <- trW(W, type="MC")
IMD_7_8im <-impacts(LSOA_IMD_7_8S, tr=trMC, R= 999)
summary(IMD_7_8im, zstats=T)
LSOA_IMD_7_8S$residuals <- residuals(LSOA_IMD_7_8S)
plot(LSOA_IMD_7_8S$residuals)
bptest.sarlm (LSOA_IMD_7_8S)
moran.mc(LSOA_IMD_7_8S$residuals, LSOA_IMD_7_8_nbq_w, zero.policy = TRUE, 999)
LSOA_IMD_7_8$LSOA_IMD_7_8Sres <- residuals(LSOA_IMD_7_8S)
tm_shape(LSOA_IMD_7_8) +
tm_fill("LSOA_IMD_7_8Sres", title = "Residuals", style = "fixed", breaks = spa_breaks, palette = "-RdBu") +
tm_borders(alpha = 0.1) +
tm_layout(main.title = "SARAR lag model Residuals", main.title.size = 0.9,
legend.position = c("left", "bottom"), legend.title.size = 0.8)
#---------------IMD_9_10_Analyses--------------#
#Data
LSOA_IMD_9_10 <- read_sf(dsn = "inputs", layer = "Quintile_group_1")
LSOA_IMD_9_10re <- LSOA_IMD_9_10 %>%
rename (Group1 = Compositem)
plot(LSOA_IMD_9_10)
#nh
LSOA_IMD_9_10_nbq <- poly2nb(LSOA_IMD_9_10, queen=TRUE) #Queen’s neighborhood
LSOA_IMD_9_10_nbq_w <- nb2listw(LSOA_IMD_9_10_nbq, style = "W", zero.policy = TRUE) #Queen’s neighborhood wights
#non-spatial model
model_IMD_9_10 <- glm(Yrpotlife~ Compositem + Income_Sco + pm10_mean_ + SHDiv,
family = Gamma(link = "identity"), data= LSOA_IMD_9_10)
summary(model_IMD_9_10)
vif(model_IMD_9_10)
moran.mc(model_IMD_9_10$residuals, LSOA_IMD_9_10_nbq_w, zero.policy = TRUE, 999)
#spatial model
LSOA_IMD_9_10Sav <- sacsarlm (Yrpotlife ~ AvailComN + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_9_10, LSOA_IMD_9_10_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_9_10Sav, Nagelkerke=T)
LSOA_IMD_9_10Sac <- sacsarlm (Yrpotlife ~ AccessS + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_9_10, LSOA_IMD_9_10_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_9_10Sac, Nagelkerke=T)
LSOA_IMD_9_10Sv <- sacsarlm (Yrpotlife ~ GVILSOA + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_9_10, LSOA_IMD_9_10_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_9_10Sv, Nagelkerke=T)
LSOA_IMD_9_10Sua <- sacsarlm (Yrpotlife ~ Compositem,
data = LSOA_IMD_9_10, LSOA_IMD_9_10_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_9_10Sua, Nagelkerke=T)
LSOA_IMD_9_10S <- sacsarlm (Yrpotlife ~ Compositem + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_9_10, LSOA_IMD_9_10_nbq_w, type = "sac", zero.policy = TRUE)
LSOA_IMD_9_10SRe <- sacsarlm (Yrpotlife ~ Group1 + Income_Sco+ Crime_Scor+ SHDiv,
data = LSOA_IMD_9_10re, LSOA_IMD_9_10_nbq_w, type = "sac", zero.policy = TRUE)
LSOA_IMD_9_10SReu <- sacsarlm (Yrpotlife ~ Group1,
data = LSOA_IMD_9_10re, LSOA_IMD_9_10_nbq_w, type = "sac", zero.policy = TRUE)
summary(LSOA_IMD_9_10S, Nagelkerke=T)
W <- as(LSOA_IMD_9_10_nbq_w, "CsparseMatrix")
trMC <- trW(W, type="MC")
IMD_9_10im <-impacts(LSOA_IMD_9_10S, tr=trMC, R= 999)
summary(IMD_9_10im, zstats=T)
LSOA_IMD_9_10S$residuals <- residuals(LSOA_IMD_9_10S)
plot(LSOA_IMD_9_10S$residuals)
bptest.sarlm (LSOA_IMD_9_10S)
moran.mc(LSOA_IMD_9_10S$residuals, LSOA_IMD_9_10_nbq_w, zero.policy = TRUE, 999)
LSOA_IMD_9_10$LSOA_IMD_9_10Sres <- residuals(LSOA_IMD_9_10S)
tm_shape(LSOA_IMD_9_10) +
tm_fill("LSOA_IMD_9_10Sres", title = "Residuals", style = "fixed", breaks = spa_breaks, palette = "-RdBu") +
tm_borders(alpha = 0.1) +
tm_layout(main.title = "SARAR lag model Residuals", main.title.size = 0.9,
legend.position = c("left", "bottom"), legend.title.size = 0.8)
plotsubmodels <- plot_summs(LSOA_IMD_1_2S, LSOA_IMD_3_4S, LSOA_IMD_5_6S, LSOA_IMD_7_8S, LSOA_IMD_9_10S,