Special Problem Codes

#Load the necessary packages

library(readxl) #import the excel file
library(tidyverse)
library(corrplot) #correlation heatmap
library(e1071) #skewness
library(ggplot2)
library(patchwork)
library(GGally)
library(ggcorrplot)
library(sf)
library(modelsummary)
library(dplyr)
library(janitor)

#Import the data
final_dataset <- read_xlsx("FINAL DATASET.xlsx")
head(final_dataset)

#Display the structure of the data
str(final_dataset)

## tibble [7,748 × 15] (S3: tbl_df/tbl/data.frame)
##  $ city_name                 : chr [1:7748] "Alaminos" "Alaminos" "Alaminos" "Alaminos" ...
##  $ Province                  : chr [1:7748] "Pangasinan" "Pangasinan" "Pangasinan" "Pangasinan" ...
##  $ Region                    : chr [1:7748] "I" "I" "I" "I" ...
##  $ Classification            : chr [1:7748] "Component" "Component" "Component" "Component" ...
##  $ Land_area_sqkm            : num [1:7748] 164 164 164 164 164 ...
##  $ week_number               : num [1:7748] 1 2 3 4 5 6 7 8 9 10 ...
##  $ Weekly_AQI                : num [1:7748] 40.1 39.5 38.5 36.6 36.5 ...
##  $ avg_temp                  : num [1:7748] 27.9 27.4 26.9 26.7 27 ...
##  $ avg_rh                    : num [1:7748] 70.4 66.1 63.1 62.6 64.1 ...
##  $ avg_wind                  : num [1:7748] 12.3 14.7 14.1 14 13.4 ...
##  $ total_precip              : num [1:7748] 7.4 3.7 0.3 3.3 0 1.3 0.3 1.8 1.5 0.7 ...
##  $ population_density        : num [1:7748] 611 611 611 611 611 ...
##  $ motor_vehicles_scores     : num [1:7748] 0.101 0.101 0.101 0.101 0.101 ...
##  $ active_establishment_score: num [1:7748] 0.243 0.243 0.243 0.243 0.243 ...
##  $ tree_cover                : num [1:7748] 10.8 10.8 10.8 10.8 10.8 ...

#Display the city names in alphabetical order
sort(unique(final_dataset$city_name))

##   [1] "Alaminos"                 "Angeles City"            
##   [3] "Antipolo"                 "Bacolod City"            
##   [5] "Bacoor"                   "Bago City"               
##   [7] "Baguio"                   "Bais"                    
##   [9] "Balanga"                  "Baliwag"                 
##  [11] "Batac City"               "Batangas"                
##  [13] "Bayawan"                  "Baybay"                  
##  [15] "Bayugan"                  "Biñan"                   
##  [17] "Bislig"                   "Bogo"                    
##  [19] "Borongan"                 "Butuan"                  
##  [21] "Cabadbaran"               "Cabanatuan City"         
##  [23] "Cabuyao"                  "Cadiz"                   
##  [25] "Cagayan De Oro"           "Calaca"                  
##  [27] "Calamba"                  "Calapan"                 
##  [29] "Calbayog City"            "Caloocan City"           
##  [31] "Candon"                   "Canlaon"                 
##  [33] "Carcar"                   "Carmona"                 
##  [35] "Catbalogan"               "Cauayan"                 
##  [37] "Cavite City"              "Cebu City"               
##  [39] "Cotabato"                 "Dagupan"                 
##  [41] "Danao"                    "Dapitan"                 
##  [43] "Dasmariñas"               "Davao"                   
##  [45] "Digos"                    "Dipolog"                 
##  [47] "Dumaguete"                "El Salvador"             
##  [49] "Escalante"                "Gapan"                   
##  [51] "General Santos"           "General Trias"           
##  [53] "Gingoog"                  "Guihulngan"              
##  [55] "Himamaylan"               "Ilagan"                  
##  [57] "Iligan"                   "Iloilo"                  
##  [59] "Imus"                     "Iriga"                   
##  [61] "Isabela"                  "Kabankalan"              
##  [63] "Kidapawan"                "Koronadal"               
##  [65] "La Carlota"               "Lamitan"                 
##  [67] "Laoag"                    "Lapu-Lapu"               
##  [69] "Las Piñas"                "Legazpi"                 
##  [71] "Ligao"                    "Lipa"                    
##  [73] "Lucena"                   "Maasin"                  
##  [75] "Mabalacat"                "Makati"                  
##  [77] "Malabon"                  "Malaybalay"              
##  [79] "Malolos"                  "Mandaluyong"             
##  [81] "Mandaue"                  "Manila"                  
##  [83] "Marawi"                   "Marikina"                
##  [85] "Masbate City"             "Mati"                    
##  [87] "Meycauayan"               "Muñoz"                   
##  [89] "Muntinlupa"               "Naga(Cebu)"              
##  [91] "Naga(CS)"                 "Navotas"                 
##  [93] "Olongapo"                 "Ormoc"                   
##  [95] "Oroquieta"                "Ozamis"                  
##  [97] "Pagadian"                 "Palayan"                 
##  [99] "Panabo"                   "Parañaque"               
## [101] "Pasay"                    "Pasig"                   
## [103] "Passi"                    "Puerto Princesa"         
## [105] "Quezon City"              "Roxas"                   
## [107] "Sagay"                    "Samal"                   
## [109] "San Carlos (Negros Occ.)" "San Carlos (Pangasinan)" 
## [111] "San Fernando (La Union)"  "San Fernando (Pampanga)" 
## [113] "San Jose"                 "San Jose del Monte"      
## [115] "San Juan"                 "San Pablo"               
## [117] "San Pedro"                "Santa Rosa"              
## [119] "Santiago"                 "Santo Tomas"             
## [121] "Silay"                    "Sipalay"                 
## [123] "Sorsogon City"            "Surigao City"            
## [125] "Tabaco"                   "Tabuk"                   
## [127] "Tacloban"                 "Tacurong"                
## [129] "Tagaytay"                 "Tagbilaran"              
## [131] "Taguig"                   "Tagum"                   
## [133] "Talisay(Cebu)"            "Talisay(Negros Occ.)"    
## [135] "Tanauan"                  "Tandag"                  
## [137] "Tangub"                   "Tanjay"                  
## [139] "Tarlac City"              "Tayabas"                 
## [141] "Toledo"                   "Trece Martires"          
## [143] "Tuguegarao"               "Urdaneta"                
## [145] "Valencia"                 "Valenzuela"              
## [147] "Victorias"                "Vigan"                   
## [149] "Zamboanga"

#Display the Regional names
sort(unique(final_dataset$Region))

##  [1] "BARMM"    "CAR"      "I"        "II"       "III"      "IV-A"    
##  [7] "IX"       "MIMAROPA" "NCR"      "NIR"      "V"        "VI"      
## [13] "VII"      "VIII"     "X"        "XI"       "XII"      "XIII"

#check for missing values per city
missing_by_city <- final_dataset |> 
  group_by(city_name) |> 
  summarise(across(everything(), ~sum(is.na(.))))
missing_by_city

data <-  final_dataset |> 
  select(Weekly_AQI,avg_temp,avg_wind,avg_rh,total_precip,population_density,tree_cover,active_establishment_score,motor_vehicles_scores,Land_area_sqkm)
  
library(psych)

## 
## Attaching package: 'psych'

## The following object is masked from 'package:modelsummary':
## 
##     SD

## The following objects are masked from 'package:ggplot2':
## 
##     %+%, alpha

describe(data)

final_dataset |> 
  group_by(Classification) |> 
  summarise(Count = n()) |> 
  mutate(Percent=round(Count/sum(Count)*100,2))

Weekly Air Quality Index (AQI) across 149 Philippine Cities

#Compute weekly summary statistics
weekly_summary <- final_dataset |> 
  group_by(week_number) |> 
  summarise(
    min_AQI = min(Weekly_AQI, na.rm = TRUE),
    max_AQI = max(Weekly_AQI, na.rm = TRUE),
    mean_AQI = mean(Weekly_AQI, na.rm = TRUE)
  )

ggplot(weekly_summary, aes(x = week_number)) + # AQI Range (Min–Max)
  geom_ribbon(aes(ymin = min_AQI,ymax = max_AQI,
                  fill = "Range (Min–Max)"),
              alpha = 0.25) +
  geom_line(aes(y = mean_AQI,colour = "Average"),
            linewidth = 0.6) + # Average AQI
  scale_colour_manual(name = "Weekly AQI Summary",
                      values = c("Average" = "black")) + # Legend scales
  scale_fill_manual(name = "",
                    values = c( "Range (Min–Max)" = "blue")) +
 scale_x_continuous(breaks = seq(10, 50, by = 10), # Axes
                    labels = c("10", "20", "30", "40", "50"), 
                    limits = c(0, 53), 
                    expand = c(0, 0)) +

  scale_y_continuous(limits = c(0, 120),
                     breaks = c(0, 50, 100),
                     expand = c(0, 0)) +

  labs(
    x = "Week Number",
    y = "Air Quality Index (AQI)"
  ) +
  guides(colour = guide_legend(order = 1), # Legend
         fill = guide_legend(order = 2)) +
  theme_classic(base_size = 16) +
  theme(axis.title = element_text(size = 16),
        axis.text = element_text(size = 14),
        legend.position = "bottom",
        legend.box = "horizontal",
        legend.title = element_text(size = 12),
        legend.text = element_text(size = 12))

This creates a cross-tabulation table to show the percentage distribution of Air Quality Index (AQI) by category by city classification.

#Compute the average AQI per city
city_avg_aqi <- final_dataset |> 
  group_by(city_name, Classification) |> 
  summarise(avg_AQI = mean(Weekly_AQI, na.rm = TRUE)) |> 
  ungroup()

## `summarise()` has regrouped the output.
## ℹ Summaries were computed grouped by city_name and Classification.
## ℹ Output is grouped by city_name.
## ℹ Use `summarise(.groups = "drop_last")` to silence this message.
## ℹ Use `summarise(.by = c(city_name, Classification))` for per-operation
##   grouping (`?dplyr::dplyr_by`) instead.

#Categorize cities by AQI level
city_avg_aqi <- city_avg_aqi |> 
  mutate(AQI_Category = case_when(
    avg_AQI <= 50 ~ "Good",
    avg_AQI <= 100 ~ "Fair",
    avg_AQI <= 150 ~ "Unhealthy for Sensitive Groups",
    avg_AQI <= 200 ~ "Very Unhealthy",
    avg_AQI <= 300 ~ "Acutely Unhealthy",
    avg_AQI > 300 ~ "Emergency",
    TRUE ~ NA_character_
  ))

#Create a cross-tabulation table
aqi_table <- city_avg_aqi |> 
  tabyl(AQI_Category, Classification) |> 
  adorn_totals(where = "row") |> 
  adorn_percentages("col") |> 
  adorn_pct_formatting(digits = 2) |> 
  adorn_ns()

#Display the table
aqi_table

The following code chunks create line graphs to demonstrate the weekly climatic patterns across Philippine cities.

# Weekly Temperature Movement
weekly_summary_temp <- final_dataset |> 
  group_by(week_number) |> 
  summarise(
    min_temp = min(avg_temp, na.rm = TRUE),
    max_temp = max(avg_temp, na.rm = TRUE),
    mean_temp = mean(avg_temp, na.rm = TRUE)
  )

temp <- ggplot(weekly_summary_temp, aes(x = week_number)) +
  geom_ribbon(aes(ymin = min_temp, # AQI Range (Min–Max)
                  ymax = max_temp,
                  fill = "Range (Min–Max)"),
              alpha = 0.25) +
  geom_line(aes(y = mean_temp,
                colour = "Average"),
            linewidth = 0.6) +  # Average AQI

  # Legend scales
  scale_colour_manual(name = "Weekly Summary",
                      values = c("Average" = "black")) +
  scale_fill_manual(name = "",
                    values = c("Range (Min–Max)" = "blue")) +
  # Axes
 scale_x_continuous(breaks = seq(10, 50, by = 10),           
                    labels = c("10", "20", "30", "40", "50"), 
                    limits = c(0, 53),                        
                    expand = c(0, 0)) +

  scale_y_continuous(limits = c(0, 33),
                     breaks = c(0, 10, 20,30),
                     expand = c(0, 0)) +

  labs(x = "Week Number",
       y = "Temperature (Celsius)") +

  # Legend
  guides(colour = guide_legend(order = 1),
         fill = guide_legend(order = 2)) +

  theme_classic(base_size = 16) +

  theme(axis.title = element_text(size = 16),
        axis.text = element_text(size = 16),
        legend.position = "bottom",
        legend.box = "horizontal",
        legend.title = element_text(size = 12),
        legend.text = element_text(size = 12))
temp

#Weekly wind speed movement

weekly_summary_ws <- final_dataset |> 
  group_by(week_number) |> 
  summarise(min_ws = min(avg_wind, na.rm = TRUE),
            max_ws = max(avg_wind, na.rm = TRUE),
            mean_ws = mean(avg_wind, na.rm = TRUE))

wind_speed<- ggplot(weekly_summary_ws, aes(x = week_number)) +
  geom_ribbon(aes(ymin = min_ws,
                  ymax = max_ws,fill = "Range (Min–Max)"),
              alpha = 0.25) + # AQI Range (Min–Max)
  geom_line(aes(y = mean_ws,colour = "Average"), # Average AQI
            linewidth = 0.6) +
  scale_colour_manual(name = "Weekly Summary",
                      values = c("Average" = "black")) +
  scale_fill_manual(name = "",
                    values = c("Range (Min–Max)" = "blue")) +
 scale_x_continuous(breaks = seq(10, 50, by = 10),           
                    labels = c("10", "20", "30", "40", "50"), 
                    limits = c(0, 53),                        
                    expand = c(0, 0)) +
  scale_y_continuous(limits = c(0, 44),
                     breaks = c(0, 15, 30),
                     expand = c(0, 0)) +
  labs(x = "Week Number",
       y = "Wind Speed (km/h)") +
  guides(colour = guide_legend(order = 1),
         fill = guide_legend(order = 2)) +
  theme_classic(base_size = 16) +
  theme(
    axis.title = element_text(size = 16),
    axis.text = element_text(size = 16),
    legend.position = "none",
    legend.box = "horizontal",
    legend.title = element_text(
      size = 12),
    legend.text = element_text(
      size = 12))
wind_speed

#Weekly relative humidity movement
weekly_summary_rh <- final_dataset |> 
  group_by(week_number) |> 
  summarise(min_rh = min(avg_rh, na.rm = TRUE),
    max_rh = max(avg_rh, na.rm = TRUE),
    mean_rh = mean(avg_rh, na.rm = TRUE))

rel_hum <- ggplot(weekly_summary_rh, aes(x = week_number)) +
  geom_ribbon(aes(ymin = min_rh, ymax = max_rh, fill = "Range (Min–Max)"),
              alpha = 0.25) +
  geom_line(aes(y = mean_rh,colour = "Average"),
            linewidth = 0.6) +
  scale_colour_manual(name = "Weekly Summary",
                      values = c("Average" = "black")) +
  scale_fill_manual(name = "",
                    values = c("Range (Min–Max)" = "blue")) +
 scale_x_continuous(
    breaks = seq(10, 50, by = 10),           
    labels = c("10", "20", "30", "40", "50"), 
    limits = c(0, 53),                        
  expand = c(0, 0)) +
  scale_y_continuous(
  limits = c(0, 100),
  breaks = c(0, 20, 40, 60, 80),
  expand = c(0, 0)
) +
  labs(
    x = "Week Number",
    y = "Relative Humidity (%)"
  ) +
  guides(
    colour = guide_legend(order = 1),
    fill = guide_legend(order = 2)
  ) +
  theme_classic(base_size = 16) +
  theme(
    axis.title = element_text(size = 16),
    axis.text = element_text(size = 16),
    legend.position = "none",
    legend.box = "horizontal",
    legend.title = element_text(
      size = 12),
    legend.text = element_text(
      size = 12))
rel_hum

#Weekly precipitation trends
weekly_summary_tp <- final_dataset |> 
  group_by(week_number) |> 
  summarise(
    min_tp = min(total_precip, na.rm = TRUE),
    max_tp = max(total_precip, na.rm = TRUE),
    mean_tp = mean(total_precip, na.rm = TRUE))

tp <- ggplot(weekly_summary_tp, aes(x = week_number)) +
  geom_ribbon(aes(ymin = min_tp,ymax = max_tp,fill = "Range (Min–Max)"),
              alpha = 0.25) +
  geom_line(aes(y = mean_tp,colour = "Average"),
            linewidth = 0.6) +
  scale_colour_manual(
    name = "Weekly Summary",
    values = c(
      "Average" = "black")) +
  scale_fill_manual(
    name = "",
    values = c(
      "Range (Min–Max)" = "blue")) +
 scale_x_continuous(
    breaks = seq(10, 50, by = 10),           
    labels = c("10", "20", "30", "40", "50"), 
    limits = c(0, 53),                        
  expand = c(0, 0)) +
  scale_y_continuous(
    limits = c(0, 550),
    breaks = c(0, 100, 200,300,400,500),
    expand = c(0, 0)) +
  labs(
    x = "Week Number",
    y = "Precipitation (mm)") +
  guides(
    colour = guide_legend(order = 1),
    fill = guide_legend(order = 2)) +
  theme_classic(base_size = 16) +
  theme(
    axis.title = element_text(size = 16),
    axis.text = element_text(size = 16),
    legend.position = "none",
    legend.box = "horizontal",
    legend.title = element_text(
      size = 12),
    legend.text = element_text(
      size = 12))
tp

Scatter plots

#Relationship between Temperature and AQI
plot_data <- final_dataset |> 
  filter(!is.na(avg_temp), !is.na(Weekly_AQI))

# Compute correlation coefficient 
cor_value <- cor(plot_data$avg_temp, plot_data$Weekly_AQI, use = "complete.obs", method = "spearman")

ggplot(plot_data, aes(x = avg_temp, y = Weekly_AQI)) +
  geom_point(color = "steelblue", alpha = 0.4, size = 1.5) +
  theme_classic(base_family = "Times New Roman") +
  labs(
    x = "Temperature (\u00B0C)",
    y = "Air Quality Index (AQI)") +
  annotate(
    "text",
    x = max(plot_data$avg_temp, na.rm = TRUE) * 0.7,   
    y = max(plot_data$Weekly_AQI, na.rm = TRUE) * 0.9, 
    label = paste0("r = ", round(cor_value, 2)),
    size = 6,
    hjust = 0) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5),
    axis.line = element_line(color = "black"),
    axis.title = element_text(size = 16),
    axis.text.y = element_text(size = 16),
     axis.text.x = element_text(size = 16))

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#Relationship between wind speed and AQI
plot_data <- final_dataset |> 
  filter(!is.na(avg_wind), !is.na(Weekly_AQI))

cor_value <- cor(plot_data$avg_wind, plot_data$Weekly_AQI, use = "complete.obs", method = "spearman")

# Create the plot
ggplot(plot_data, aes(x = avg_wind, y = Weekly_AQI)) +
  geom_point(color = "steelblue", alpha = 0.4, size = 1.5) +
  theme_classic() +
  labs(
    title = "",
    subtitle = "",
    x = "Wind Speed (km/h)",
    y = "Air Quality Index (AQI)") +
  
   annotate(
    "text",
    x = max(plot_data$avg_wind, na.rm = TRUE) * 0.7,   
    y = max(plot_data$Weekly_AQI, na.rm = TRUE) * 0.9, 
    label = paste0("r = ", round(cor_value, 2)),
    size = 6,
    hjust = 0) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5),
    axis.line = element_line(color = "black"),
    axis.title = element_text(size = 16),
    axis.text.y = element_text(size = 16),
     axis.text.x = element_text(size = 16))

#Relative Humidity and AQI

plot_data <- final_dataset |> 
  filter(!is.na(avg_rh), !is.na(Weekly_AQI))

cor_value <- cor(plot_data$avg_rh, plot_data$Weekly_AQI, use = "complete.obs",method="spearman")

ggplot(plot_data, aes(x = avg_rh, y = Weekly_AQI)) +
  geom_point(color = "steelblue", alpha = 0.4, size = 1.5) +
  theme_classic(base_family = "Cambria") +
  labs(
    title = "",
    subtitle = "",
    x = "Relative Humidity (%)",
    y = "Air Quality Index (AQI)") +
  annotate(
    "text",
    x = max(plot_data$avg_rh, na.rm = TRUE) * 0.7,   
    y = max(plot_data$Weekly_AQI, na.rm = TRUE) * 0.9, 
    label = paste0("r = ", round(cor_value, 2)),
    size = 6,
    hjust = 0) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5),
    axis.line = element_line(color = "black"),axis.title = element_text(size = 16),
    axis.text.y = element_text(size = 16),
     axis.text.x = element_text(size = 16))

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#Precipitation and AQI
plot_data <- final_dataset |> 
  filter(!is.na(total_precip), !is.na(Weekly_AQI))

cor_value <- cor(plot_data$total_precip, plot_data$Weekly_AQI, use = "complete.obs", method = "spearman")

ggplot(plot_data, aes(x = total_precip, y = Weekly_AQI)) +
  geom_point(color = "steelblue", alpha = 0.4, size = 1.5) +
  theme_classic(base_family = "Cambria") +
  labs(
    title = "",
    subtitle = "",
    x = "Precipitation (mm)",
    y = "Air Quality Index (AQI)"
  ) +
   annotate(
    "text",
    x = max(plot_data$avg_rh, na.rm = TRUE) * 3,   # adjust position
    y = max(plot_data$Weekly_AQI, na.rm = TRUE) * 0.8, # adjust position
    label = paste0("r = ", round(cor_value, 2)),
    size = 6,
    hjust = 0
  ) +
  theme(
    plot.title = element_text(face = "bold", hjust = 0.5),
    plot.subtitle = element_text(hjust = 0.5),
    axis.line = element_line(color = "black"),
    axis.title = element_text(size = 16),
    axis.text.y = element_text(size = 16),
     axis.text.x = element_text(size = 16)
  )

## Warning in grid.Call(C_textBounds, as.graphicsAnnot(x$label), x$x, x$y, : font
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The following code chunks generate bar graphs highlighting the leading cities across each city‑level characteristic.

#Population Density
top_10_density <- final_dataset |> 
  distinct(city_name, population_density) |> 
  slice_max(order_by = population_density, n = 10)

#Create the bar chart
pd <- ggplot(top_10_density, aes(x = reorder(city_name, population_density),
                                 y = population_density)) +
  geom_col(fill = "steelblue") + 
  geom_text(aes(label = scales::comma(population_density)),  
            hjust = -0.1, 
            size = 5.5) +
  coord_flip() + 
  scale_y_continuous(expand = expansion(mult = c(0, 0.2)), 
                     labels = scales::comma) +
  theme_classic() +
  labs(
    title = "",
    x = "",
    y = "Population Density (people/km\u00B2)") +
  theme(
    panel.grid.major.y = element_blank(), 
    plot.title = element_text(face = "bold"),
    axis.title = element_text(size = 16),
    axis.text.y = element_text(size = 16),
     axis.text.x = element_text(size = 16))
pd

#Tree cover
top_10_tree_cover <- final_dataset |> 
  distinct(city_name, tree_cover) |> 
  slice_min(order_by = tree_cover, n = 10)

tc <- ggplot(top_10_tree_cover, aes(x = reorder(city_name, tree_cover),
                                    y = tree_cover)) +
  geom_col(fill = "steelblue", color = "midnightblue") +
  geom_text(aes(label = round(tree_cover, 2)), 
            hjust = -0.1, 
            size = 5.5) +
  coord_flip() + 
  scale_y_continuous(expand = expansion(mult = c(0, 0.15))) +
  theme_classic() +
  labs(
    x = "",
    y = "Tree Cover (%)") +
  theme(
      panel.grid.major.y = element_blank(), 
    plot.title = element_text(face = "bold"),
     axis.title = element_text(size = 16),
    axis.text.y = element_text(size = 16),
     axis.text.x = element_text(size = 16))
tc

#Active Establishment
top_10_acs <- final_dataset |> 
  distinct(city_name, active_establishment_score) |> 
  slice_max(order_by = active_establishment_score, n = 10)

acs <- ggplot(top_10_acs, aes(x = reorder(city_name, active_establishment_score), 
                              y = active_establishment_score)) +
  geom_col(fill = "steelblue",col="midnightblue") +
  geom_text(aes(label = round(active_establishment_score, 2)), 
            hjust = -0.1, 
            size = 5.5) +  
  coord_flip() +
  scale_y_continuous(expand = expansion(mult = c(0, 0.15))) +
  theme_classic() +
  labs(
    title = "",
    x = "",
    y = "Active Establishment Score") +
  theme(
      panel.grid.major.y = element_blank(), 
    plot.title = element_text(face = "bold"),
    axis.title = element_text(size = 16),
    axis.text.y = element_text(size = 16),
     axis.text.x = element_text(size = 16))
acs

#Motor Vehicles
top_10_mvs <- final_dataset |> 
  distinct(city_name, motor_vehicles_scores) |> 
  slice_max(order_by = motor_vehicles_scores, n = 10)

mvs <- ggplot(top_10_mvs, aes(x = reorder(city_name, motor_vehicles_scores),
                              y = motor_vehicles_scores)) +
  geom_col(fill = "steelblue", col="midnightblue" ) +
  geom_text(aes(label = round(motor_vehicles_scores, 2)), 
            hjust = -0.1, 
            size = 5.5) +

  coord_flip() +
  scale_y_continuous(expand = expansion(mult = c(0, 0.15))) +
  theme_classic() +
  labs(
    title = "",
    x = "",
    y = "Motor Vehicle Score") +
  theme(
      panel.grid.major.y = element_blank(), 
    plot.title = element_text(face = "bold"),
    axis.title = element_text(size = 16),
    axis.text.y = element_text(size = 16),
     axis.text.x = element_text(size = 16))
mvs

library(showtext)

## Loading required package: sysfonts

## Loading required package: showtextdb

# Add Times New Roman support
font_add("Times New Roman", "C:/Windows/Fonts/times.ttf")
showtext_auto()

top_10_cities <- final_dataset |> 
  group_by(city_name, Region) |> 
  summarise(
    Average_AQI = round(mean(Weekly_AQI, na.rm = TRUE),10),
    .groups = "drop") |> 
  arrange(desc(Average_AQI)) |> 
  slice_head(n = 10)


ggplot(top_10_cities, aes(x = reorder(city_name, Average_AQI), y = Average_AQI)) +
  geom_bar(stat = "identity", fill="steelblue",col="midnightblue", width = 0.9) +
  coord_flip() +
  geom_text(aes(label = round(Average_AQI, 2)), 
            hjust = -0.2, 
            size = 50, 
            family = "Times New Roman") +
  theme_classic(base_family = "Times New Roman") +
  labs(
    title = "",
    x = "",
    y = "Average Air Quality Index (AQI)") +
 theme(
    axis.text.x = element_text(color = "black", size = 150, family = "Times New Roman"),
    axis.text.y = element_text(color = "black", size = 150, family = "Times New Roman"),
    axis.title.x = element_text(size =150, family = "Times New Roman"),
    axis.title.y = element_text(size = 150, family = "Times New Roman"),
    legend.text = element_text(size = 150, family = "Times New Roman"),
    legend.title = element_text(size = 150, family = "Times New Roman")) +
  scale_y_continuous(expand = expansion(mult = c(0, 0.15)))

Data Transformation

data <- read_excel("FINAL DATASET.xlsx")

# Define the variables based on their nature
vars_to_log <- c("Weekly_AQI", "avg_wind")
vars_log_plus_one <- c("total_precip")

# Transformation
city_data_transformed <- data |> 
  # Apply standard natural log to positive variables
  mutate(across(all_of(vars_to_log), 
                ~log(.), 
                .names = "ln_{.col}")) |> 
  
  # Apply log(x+1) to precipitation to handle zero-rain weeks
  mutate(across(all_of(vars_log_plus_one), 
                ~log(. + 1), 
                .names = "ln_{.col}"))

#Mean centering
city_data_transformed$temp_centered = city_data_transformed$avg_temp - mean(city_data_transformed$avg_temp, na.rm = TRUE)
city_data_transformed$rh_centered = city_data_transformed$avg_rh - mean(city_data_transformed$avg_rh, na.rm = TRUE)
city_data_transformed$ln_AQI_centered = city_data_transformed$ln_Weekly_AQI- mean(city_data_transformed$ln_Weekly_AQI, na.rm = TRUE)
city_data_transformed$ln_wind_centered = city_data_transformed$ln_avg_wind- mean(city_data_transformed$ln_avg_wind, na.rm = TRUE)
city_data_transformed$ln_precip_centered = city_data_transformed$ln_total_precip- mean(city_data_transformed$ln_total_precip, na.rm = TRUE)
  
summary(city_data_transformed |> select("ln_Weekly_AQI","ln_avg_wind","ln_total_precip","temp_centered","rh_centered"))

##  ln_Weekly_AQI    ln_avg_wind    ln_total_precip temp_centered      
##  Min.   :2.675   Min.   :1.019   Min.   :0.000   Min.   :-11.79285  
##  1st Qu.:3.313   1st Qu.:1.801   1st Qu.:2.342   1st Qu.: -0.75000  
##  Median :3.531   Median :2.127   Median :3.478   Median : -0.03571  
##  Mean   :3.566   Mean   :2.156   Mean   :3.226   Mean   :  0.00000  
##  3rd Qu.:3.787   3rd Qu.:2.490   3rd Qu.:4.230   3rd Qu.:  0.77857  
##  Max.   :4.727   Max.   :3.764   Max.   :6.282   Max.   :  4.89286  
##   rh_centered     
##  Min.   :-25.983  
##  1st Qu.: -5.268  
##  Median :  2.446  
##  Mean   :  0.000  
##  3rd Qu.:  6.017  
##  Max.   : 18.589

Cross-sectionally Augmented Autoregressive Distributed-Lag (CS-ARDL) Methodology

Cross-Sectional Dependence Test to see if the climatic factors and AQI across cities move together/ cities are interconnected.

library(plm)
library(dplyr)
pdata2 <- pdata.frame(city_data_transformed, index = c("city_name", "week_number"))

cd_test <- pcdtest(ln_Weekly_AQI ~ temp_centered + rh_centered + ln_wind_centered + ln_precip_centered, 
                   data = pdata2, test = "cd")
cd_test

## 
##  Pesaran CD test for cross-sectional dependence in panels
## 
## data:  ln_Weekly_AQI ~ temp_centered + rh_centered + ln_wind_centered +     ln_precip_centered
## z = 232.43, p-value < 2.2e-16
## alternative hypothesis: cross-sectional dependence

Conclusion: TThe Pesaran CD test reveals significant connections among cities, indicating that weekly AQI levels tend to move together across regions.”

Slope Homogeneity Test to test whether the variable relationship is uniform across cities.

library(xtbhst)
pdata2$city_name <- as.character(pdata2$city_name)

py_test <- xtbhst(ln_Weekly_AQI ~ temp_centered + rh_centered + ln_wind_centered + ln_precip_centered,, 
                  data =pdata2, 
                  id = "city_name", 
                  time = "week_number")
py_test 

## 
## Bootstrap test for slope heterogeneity
## (Blomquist & Westerlund, 2015. Empirical Economics)
## H0: slope coefficients are homogeneous
## --------------------------------------------- 
##          Delta     BS p-value
##        58.8189         0.0000
## adj.   62.5374         0.0000
## --------------------------------------------- 
## Bootstrap replications: 999 
## Block length: 7 
## Panel: N = 149 , T = 52 , K = 4 
## Variables partialled out: 1

Conclusion: The slope coefficients are heterogeneous. The relationship between the independent variables (Temperature, Wind, Humidity, Precipitation) and dependent variable (AQI) varies significantly from city to city so heterogeneity exists across cities and models that allow for city-specific effects, such as CS-ARDL must be used.

Second Generation Unit Root Test to test for the stationarity of the variables, while accounting for cross-sectional dependence.

## 
##  Pesaran's CIPS test for unit roots
## 
## data:  pdata2$ln_Weekly_AQI
## CIPS test = -3.0356, lag order = 2, p-value = 0.01
## alternative hypothesis: Stationarity

## 
##  Pesaran's CIPS test for unit roots
## 
## data:  pdata2$temp_centered
## CIPS test = -2.5607, lag order = 2, p-value = 0.04521
## alternative hypothesis: Stationarity

## 
##  Pesaran's CIPS test for unit roots
## 
## data:  pdata2$rh_centered
## CIPS test = -1.9649, lag order = 2, p-value = 0.1
## alternative hypothesis: Stationarity

## 
##  Pesaran's CIPS test for unit roots
## 
## data:  pdata2$ln_wind_centered
## CIPS test = -2.5507, lag order = 2, p-value = 0.04983
## alternative hypothesis: Stationarity

## 
##  Pesaran's CIPS test for unit roots
## 
## data:  pdata2$ln_precip_centered
## CIPS test = -3.1766, lag order = 2, p-value = 0.01
## alternative hypothesis: Stationarity

Conclusion: Weekly AQI, Temp and Total Precipitation are stationary at 5% level of significance, while WS at the borderline.

We’ll take the first difference of relative humidity and wind speed to test if it is stationary at I(1).

cipstest(diff(pdata2$rh_centered,1), type = "trend")

## Warning in cipstest(diff(pdata2$rh_centered, 1), type = "trend"): p-value
## smaller than printed p-value

## 
##  Pesaran's CIPS test for unit roots
## 
## data:  diff(pdata2$rh_centered, 1)
## CIPS test = -4.6298, lag order = 2, p-value = 0.01
## alternative hypothesis: Stationarity

cipstest(diff(pdata2$ln_wind_centered,1), type = "trend")

## Warning in cipstest(diff(pdata2$ln_wind_centered, 1), type = "trend"): p-value
## smaller than printed p-value

## 
##  Pesaran's CIPS test for unit roots
## 
## data:  diff(pdata2$ln_wind_centered, 1)
## CIPS test = -5.2563, lag order = 2, p-value = 0.01
## alternative hypothesis: Stationarity

Conclusion: All variables now appear stationary. Since variables are stationary at I(0) or at I(1), we may proceed.

Second Generation Cointegration Test

library(Westerlund)
# Convert to a standard dataframe to strip 'pseries' attributes
standard_df <- as.data.frame(pdata2)
standard_df$city_name <- as.character(standard_df$city_name) 
standard_df$week_number <- as.numeric(as.character(standard_df$week_number)) 

coint_res <- westerlund_test(
  data = standard_df,
  yvar = "ln_Weekly_AQI",
  xvars = c("avg_temp","ln_avg_wind","avg_rh","ln_total_precip"),
  idvar = "city_name",
  timevar = "week_number",
  lags = 2,      
  leads = 1,
  aic = TRUE,
  bootstrap = 500
)

coint_res

## $test_stats
## $test_stats$Gt
## [1] -2.716158
## 
## $test_stats$Ga
## [1] -14.69877
## 
## $test_stats$Pt
## [1] -31.98392
## 
## $test_stats$Pa
## [1] -13.38433
## 
## 
## $boot_pvals
## $boot_pvals$Gt
## [1] 0.001996008
## 
## $boot_pvals$Ga
## [1] 0.001996008
## 
## $boot_pvals$Pt
## [1] 0.001996008
## 
## $boot_pvals$Pa
## [1] 0.001996008
## 
## 
## $bootstrap_distributions
##              [,1]       [,2]       [,3]       [,4]
##   [1,] -1.5336864  -7.991591 -12.972034  -3.614784
##   [2,] -1.6664378  -8.212710 -15.180513  -4.013845
##   [3,] -1.4976513  -6.486193 -15.089235  -4.192561
##   [4,] -1.9781415 -10.430322 -20.565619  -7.305701
##   [5,] -1.6848779  -8.425178 -13.841971  -3.879882
##   [6,] -1.8444880  -9.123843 -17.435657  -5.748164
##   [7,] -1.6790847  -8.069985 -14.407235  -4.067448
##   [8,] -1.9015086  -8.012489 -19.076433  -4.423987
##   [9,] -1.6327813  -7.009552 -16.166407  -4.155832
##  [10,] -1.8579325  -8.000006 -19.610976  -5.872775
##  [11,] -1.7945261  -8.328395 -15.905656  -3.883672
##  [12,] -2.0977761  -9.154945 -22.263875  -6.517340
##  [13,] -1.5982087  -7.344065 -16.029685  -4.661315
##  [14,] -1.5610930  -7.672426 -15.372675  -5.085460
##  [15,] -1.6612212  -7.604538 -18.030116  -5.463030
##  [16,] -1.9729058  -9.148692 -21.451655  -6.952844
##  [17,] -1.8875239 -10.037174 -17.605258  -5.444653
##  [18,] -1.9470362 -10.200964 -21.872367  -7.565420
##  [19,] -1.8936429  -9.366267 -21.653355  -7.134074
##  [20,] -1.8578051  -7.597253 -20.552331  -5.744585
##  [21,] -1.5127534  -6.589595 -16.317141  -4.942662
##  [22,] -1.7154256  -8.927856 -14.806789  -4.021405
##  [23,] -1.6700314  -8.050793 -18.125064  -5.327313
##  [24,] -1.8876820  -9.178049 -20.611860  -7.008960
##  [25,] -1.3955745  -7.257459  -7.956378  -1.513524
##  [26,] -1.8699723  -9.171024 -18.688467  -5.252028
##  [27,] -1.9780391 -10.358197 -18.472376  -5.544303
##  [28,] -1.7146409  -7.148713 -14.282136  -2.913661
##  [29,] -1.9240782 -10.153729 -20.960196  -7.036346
##  [30,] -1.7330726  -7.644859 -13.859745  -3.102813
##  [31,] -1.7048270  -9.429753 -18.589489  -6.726371
##  [32,] -1.4835000  -6.678407 -15.020403  -4.393454
##  [33,] -1.6815285  -7.292314 -15.477034  -3.504010
##  [34,] -1.5978299  -7.121782 -15.407514  -4.030650
##  [35,] -1.5584758  -6.610458 -16.746984  -4.611535
##  [36,] -1.9482174 -10.344268 -22.276032  -8.418565
##  [37,] -1.6477503  -7.947720 -17.031632  -4.934889
##  [38,] -1.4160449  -6.506361 -13.584749  -4.232334
##  [39,] -1.7144754  -7.909755 -17.241149  -5.366070
##  [40,] -1.4346417  -7.683244 -14.379671  -4.113372
##  [41,] -1.5419540  -7.431568 -14.249413  -3.869546
##  [42,] -1.9402280  -9.602299 -20.270761  -6.624226
##  [43,] -1.9390732  -9.748831 -20.860989  -7.038752
##  [44,] -1.2806920  -5.956565 -11.935227  -3.167767
##  [45,] -1.8867312 -10.314981 -20.315419  -7.339935
##  [46,] -1.1269387  -5.532996 -10.149090  -2.659981
##  [47,] -1.9994701 -10.991218 -22.283858  -8.144943
##  [48,] -1.5277788  -7.431618 -16.155458  -5.040470
##  [49,] -1.2608938  -4.437591 -12.847108  -2.716552
##  [50,] -1.9968268 -10.041539 -21.191241  -7.768224
##  [51,] -2.0850293 -10.866288 -23.174236  -7.897612
##  [52,] -1.4475902  -6.624820 -13.396934  -3.458045
##  [53,] -1.1405869  -4.914653 -10.520027  -2.203140
##  [54,] -1.7930972  -7.906148 -18.255854  -5.217108
##  [55,] -2.0976876  -9.549770 -22.199801  -6.428759
##  [56,] -1.8463377  -8.025833 -18.439859  -4.678637
##  [57,] -1.6219806  -6.231739 -18.116661  -4.443004
##  [58,] -1.1035909  -5.351581  -6.753499  -1.439778
##  [59,] -1.8946859  -8.749357 -19.475214  -5.868686
##  [60,] -1.2703299  -7.172928  -7.005686  -1.331600
##  [61,] -1.4325471  -6.561726 -14.904127  -4.015845
##  [62,] -1.7601881  -9.258220 -19.183814  -6.412738
##  [63,] -1.9269602 -10.850013 -15.677234  -5.057233
##  [64,] -2.2277071 -13.822551 -23.159304  -8.324489
##  [65,] -2.0349490  -9.168833 -19.299428  -4.799665
##  [66,] -1.6391340  -6.744838 -16.816963  -3.975152
##  [67,] -2.1602196 -10.034787 -23.232147  -7.711274
##  [68,] -1.3686101  -6.527976 -12.039512  -3.092195
##  [69,] -1.8701750  -8.158577 -21.123669  -6.204947
##  [70,] -1.8565836  -8.906077 -19.339317  -6.065452
##  [71,] -1.7169412  -7.883259 -18.426247  -5.578906
##  [72,] -1.4816272  -7.011574 -13.894042  -3.878774
##  [73,] -1.6497678  -8.293861 -14.603752  -4.160379
##  [74,] -1.6107476  -6.901875 -13.623820  -3.349367
##  [75,] -1.4224119  -7.024226 -13.905938  -3.966026
##  [76,] -1.5809365  -8.360270 -15.037138  -4.496316
##  [77,] -1.7354679  -8.359421 -18.504315  -6.287136
##  [78,] -1.3767889  -6.654395 -13.476016  -3.555421
##  [79,] -1.7642590  -8.901753 -16.295985  -5.021505
##  [80,] -1.8268021 -10.156083 -16.596723  -5.494866
##  [81,] -1.6377055  -6.431981 -14.908080  -3.340446
##  [82,] -1.9568837  -9.047919 -20.003782  -5.857011
##  [83,] -1.3077310  -6.908823 -11.851722  -3.437556
##  [84,] -1.7586082  -8.297017 -15.734554  -3.816305
##  [85,] -1.1942993  -5.055264 -10.421131  -2.508855
##  [86,] -2.0984784  -8.820254 -20.472683  -5.695595
##  [87,] -2.0483262  -8.723645 -23.487601  -7.034939
##  [88,] -2.0866142  -9.963557 -20.801651  -5.953819
##  [89,] -1.4217707  -6.315436 -14.787042  -4.413610
##  [90,] -1.7205456  -8.583517 -17.200698  -5.589950
##  [91,] -1.5052490  -6.191376 -15.874882  -3.880693
##  [92,] -1.7230116  -8.595294 -17.231591  -4.864597
##  [93,] -2.1554590  -9.519170 -21.397494  -5.462038
##  [94,] -1.6464395  -8.170036 -17.468384  -5.574306
##  [95,] -1.3692958  -6.257036 -10.913156  -2.809534
##  [96,] -1.3991179  -7.893055 -12.641866  -3.722230
##  [97,] -2.1493794 -11.710134 -23.401977  -8.305889
##  [98,] -1.7557319  -8.223944 -20.499650  -6.995920
##  [99,] -1.3871396  -7.516586 -14.455791  -4.439342
## [100,] -1.4919637  -6.588483 -15.243920  -4.722274
## [101,] -1.7671690  -8.360055 -18.473700  -5.749147
## [102,] -1.9852818 -10.847970 -19.968840  -6.637978
## [103,] -1.5148558  -6.593828 -15.555445  -4.089407
## [104,] -1.5996256  -7.652280 -17.358695  -5.086726
## [105,] -1.2368375  -6.126372 -11.382488  -2.826662
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## [358,] -1.8431297  -9.932117 -19.302041  -7.250959
## [359,] -1.7687593  -6.880739 -18.270928  -4.575042
## [360,] -1.5913278  -7.429852 -15.727119  -4.227843
## [361,] -1.6076388  -6.787792 -14.730709  -3.950473
## [362,] -1.7042347  -7.259307 -17.013042  -4.391114
## [363,] -1.6846010  -6.890831 -18.320791  -5.061871
## [364,] -1.3276705  -5.906207 -12.251041  -2.774796
## [365,] -1.9928988 -10.381069 -16.840652  -5.056363
## [366,] -1.6506882  -8.363370 -16.342181  -5.177311
## [367,] -1.8090817  -8.598696 -19.570719  -6.004195
## [368,] -2.0026302 -10.475058 -20.925115  -7.532329
## [369,] -1.7642504  -8.464693 -17.826623  -5.279328
## [370,] -2.0369295 -10.271371 -20.813739  -6.342188
## [371,] -1.5996168  -7.912005 -15.177054  -4.600438
## [372,] -1.8000193  -9.594256 -16.487658  -5.230343
## [373,] -1.6553045  -7.641027 -17.027754  -4.592295
## [374,] -1.4528999  -6.597084 -12.607010  -2.715247
## [375,] -1.5120880  -7.813077 -12.831967  -3.479176
## [376,] -1.6876600  -7.966507 -18.324973  -5.892357
## [377,] -1.2969321  -6.153404 -12.030113  -3.287172
## [378,] -1.7911910  -8.249309 -21.302713  -6.944057
## [379,] -1.6498759  -9.477613 -10.545171  -2.778251
## [380,] -1.8992312  -9.354593 -18.423941  -5.121035
## [381,] -1.7329656  -8.322340 -18.307380  -5.524473
## [382,] -1.8150053  -9.040905 -17.581920  -5.061324
## [383,] -1.7037791  -9.760223 -17.441168  -6.296029
## [384,] -2.2067191 -11.980059 -23.936694  -8.397856
## [385,] -1.6165046  -8.973805 -16.092178  -5.519260
## [386,] -1.7597147  -7.260129 -18.500142  -5.283363
## [387,] -1.6109842  -8.052801 -15.285784  -4.532393
## [388,] -1.5348578  -5.774746 -13.720637  -2.923006
## [389,] -1.6436960  -7.276105 -18.585468  -6.084651
## [390,] -1.6974624  -8.006378 -14.559618  -3.836814
## [391,] -1.8246256  -7.887810 -18.934406  -5.312537
## [392,] -1.5655589  -7.190339 -14.625147  -4.134104
## [393,] -1.5576636  -8.291459 -16.011460  -5.333978
## [394,] -1.8741994  -9.350311 -20.263518  -6.775474
## [395,] -1.4932008  -6.247537 -14.179857  -3.432144
## [396,] -1.8492681  -7.965630 -21.413993  -6.092213
## [397,] -1.3341521  -5.533157 -13.367636  -3.276474
## [398,] -1.5445502  -7.764320 -15.810744  -4.936392
## [399,] -1.5549111  -6.588678 -15.174361  -4.069859
## [400,] -1.7313558  -7.540393 -18.151920  -5.309960
## [401,] -1.8601419  -8.925033 -20.191733  -5.973117
## [402,] -1.4504373  -6.522182 -13.189579  -3.247260
## [403,] -2.1507698 -12.638092 -20.553462  -6.910522
## [404,] -1.6048590  -7.540065 -15.734471  -4.256861
## [405,] -1.8146159  -7.748508 -18.999388  -5.296069
## [406,] -1.8460672  -9.322125 -17.803777  -5.600646
## [407,] -1.5720988  -8.081957 -14.510177  -5.030559
## [408,] -1.9277696  -9.750696 -18.315040  -5.369390
## [409,] -1.6984313  -8.022143 -15.413839  -3.740847
## [410,] -1.8588651  -9.835204 -20.393944  -6.957407
## [411,] -1.6458926  -7.463573 -18.024579  -5.468034
## [412,] -1.1778251  -5.162383  -8.137976  -1.819199
## [413,] -1.4853628  -5.940411 -17.586698  -4.702160
## [414,] -1.6337679  -7.251560 -16.045038  -4.052930
## [415,] -1.3893624  -5.408472 -12.921639  -2.722252
## [416,] -1.7584864  -8.408543 -17.673331  -5.661116
## [417,] -1.9577054  -9.426984 -20.946132  -6.527014
## [418,] -1.7448719  -6.256055 -18.023913  -3.883059
## [419,] -1.4332448  -7.407782 -16.008323  -4.841086
## [420,] -1.4409483  -6.451913 -13.185455  -3.519827
## [421,] -1.8226376  -8.872235 -18.937891  -5.802531
## [422,] -1.5593716  -7.386879 -17.178869  -5.092577
## [423,] -2.1361900 -11.709081 -22.749682  -8.448266
## [424,] -2.2767310 -13.315311 -23.645731  -9.551907
## [425,] -2.1216744 -11.594190 -21.205698  -7.854760
## [426,] -1.1065388  -4.816095 -10.713458  -2.389994
## [427,] -1.7827145 -10.473492 -16.442194  -5.583318
## [428,] -1.4158143  -7.009035 -12.524178  -3.500036
## [429,] -1.5372301  -7.106201 -19.097903  -5.931643
## [430,] -1.7063585  -8.251231 -17.122393  -5.003959
## [431,] -2.2008218 -10.966258 -24.009075  -8.228263
## [432,] -1.6761392  -7.729761 -15.806400  -4.222158
## [433,] -1.3852191  -5.867791 -17.136834  -4.985447
## [434,] -1.9107927  -9.710490 -18.867907  -6.162648
## [435,] -1.7391791  -7.820256 -18.180450  -5.208413
## [436,] -1.5116587  -6.618955 -14.767487  -3.789263
## [437,] -1.8214024  -7.975291 -16.889407  -4.530579
## [438,] -1.8171656  -9.087987 -18.922765  -6.224860
## [439,] -1.6359550  -7.646655 -15.490191  -4.309491
## [440,] -1.8075651  -8.820121 -20.279469  -6.529573
## [441,] -2.0406906  -9.763170 -21.736607  -6.656204
## [442,] -1.6501754  -8.133556 -14.864897  -4.170602
## [443,] -1.6749871  -6.244795 -18.828041  -4.984771
## [444,] -1.7939378  -8.263756 -18.434559  -5.439817
## [445,] -1.6541322  -8.197585 -14.550581  -4.051432
## [446,] -1.5599017  -6.868788 -17.679405  -5.142369
## [447,] -1.7885926  -9.631495 -18.047491  -5.878503
## [448,] -2.0791869 -11.749951 -20.819927  -7.580207
## [449,] -1.5988081  -6.736067 -15.676726  -3.891035
## [450,] -1.8620419  -7.786756 -20.277843  -5.931110
## [451,] -1.7607303  -8.718944 -19.206486  -6.328024
## [452,] -1.9027920 -10.056187 -19.546907  -6.791651
## [453,] -1.7893579  -8.998978 -18.340156  -5.295960
## [454,] -2.0627774 -10.373625 -23.396202  -8.569884
## [455,] -1.6214101  -6.776608 -18.882786  -5.394564
## [456,] -1.7823989  -8.188562 -16.789253  -4.410400
## [457,] -1.8262659  -9.610906 -18.440865  -5.429064
## [458,] -1.2670158  -6.128919 -11.620068  -2.959705
## [459,] -1.6773703  -8.345581 -17.968915  -6.320270
## [460,] -1.5673052  -8.780496 -14.209094  -4.342780
## [461,] -2.0694621 -10.551043 -22.497030  -7.896569
## [462,] -1.9904383 -10.455871 -20.581262  -6.579763
## [463,] -1.7467604  -8.394379 -18.723198  -5.892233
## [464,] -1.4383557  -6.036965 -14.749115  -4.061494
## [465,] -1.7262049  -8.154624  -8.745666  -1.557596
## [466,] -1.6831496  -7.487687 -19.225306  -5.588101
## [467,] -1.8446169  -9.060803 -20.105925  -6.139486
## [468,] -1.4616963  -6.637266 -14.185717  -3.732289
## [469,] -1.4737391  -7.459027 -15.521674  -5.052152
## [470,] -1.9371627  -8.766598 -21.859421  -7.012438
## [471,] -1.9336305  -9.547275 -21.132008  -7.222729
## [472,] -1.7279318  -8.388328 -15.856763  -4.399860
## [473,] -1.1443781  -6.477755  -8.399148  -2.197575
## [474,] -1.7545518  -9.584922 -19.189305  -6.736111
## [475,] -1.9082868  -8.143096 -19.368148  -5.115726
## [476,] -1.1541555  -5.249774 -12.121223  -3.230268
## [477,] -1.4207138  -5.992457 -13.310728  -3.174085
## [478,] -1.8981040  -9.774914 -16.681677  -4.569303
## [479,] -1.5471820  -6.727466 -17.986785  -5.232217
## [480,] -1.9284523  -8.766129 -21.386489  -6.899600
## [481,] -1.9183487  -9.660777 -17.442646  -4.947633
## [482,] -1.7553289  -8.888505 -15.857032  -4.684447
## [483,] -1.9866950  -9.164510 -17.295190  -4.399871
## [484,] -2.2171027 -11.042238 -23.832923  -8.131447
## [485,] -2.1241212  -9.088714 -19.329442  -5.096065
## [486,] -1.7219635  -8.151223 -17.228741  -6.102473
## [487,] -1.5927002  -6.472533 -16.522693  -4.539421
## [488,] -1.1938380  -4.959509 -11.572662  -3.074795
## [489,] -1.1905510  -5.589007  -7.757885  -1.858447
## [490,] -1.7531005  -8.307636 -19.269299  -6.074449
## [491,] -1.4515882  -7.782264 -11.687026  -2.939173
## [492,] -1.5694470  -7.155860 -17.645592  -5.384663
## [493,] -1.7337016  -7.685691 -16.439086  -4.616690
## [494,] -1.5385343  -5.958286 -16.995411  -4.299870
## [495,] -1.6375549  -8.207298 -12.764926  -2.984468
## [496,] -1.5656995  -8.289821 -16.171411  -5.231221
## [497,] -1.8153288 -10.970001 -16.824364  -5.444806
## [498,] -2.1438960 -10.067360 -24.403778  -8.595157
## [499,] -1.2573031  -6.019320  -9.581523  -1.682997
## [500,] -1.5304938  -6.517075 -16.101962  -4.425420
## 
## $unit_data
##                           id          ai      seai  aonesemi lags leads
## 1                   Alaminos -0.80165660 0.3231062 1.0579721    2     1
## 2               Angeles City -0.82592178 0.3215839 1.0497561    2     1
## 3                   Antipolo -0.79212975 0.1969987 1.1152347    2     1
## 4               Bacolod City -0.60988976 0.2879314 1.3230858    2     1
## 5                     Bacoor -0.41248577 0.3824086 1.4147862    2     1
## 6                  Bago City -0.72686503 0.3058350 1.3679073    2     1
## 7                     Baguio -0.61292593 0.2523098 1.8207247    2     1
## 8                       Bais -0.69532321 0.3356384 1.3629462    2     1
## 9                    Balanga -1.28177273 0.4258863 1.0343682    2     1
## 10                   Baliwag -0.82117590 0.2344967 1.0343243    2     1
## 11                Batac City -1.07835999 0.3327237 1.3312839    2     1
## 12                  Batangas -0.82016753 0.2388178 1.3038531    2     1
## 13                   Bayawan -0.52405716 0.3256368 1.4733337    2     1
## 14                    Baybay -0.57016356 0.1955426 1.2182560    2     1
## 15                   Bayugan -0.39965970 0.1432205 1.0178062    2     1
## 16                     Biñan -0.82568201 0.2860276 1.3183464    2     1
## 17                    Bislig -0.30495228 0.2244085 0.9269162    2     1
## 18                      Bogo -0.33938285 0.2116747 1.0715372    2     1
## 19                  Borongan -0.40586039 0.1854283 1.2390255    2     1
## 20                    Butuan -0.41968744 0.1843674 1.0468534    2     1
## 21                Cabadbaran -0.44008865 0.1837261 1.2747118    2     1
## 22           Cabanatuan City -0.61609880 0.2483694 1.1581764    2     1
## 23                   Cabuyao -0.73715344 0.2955746 1.1637814    2     1
## 24                     Cadiz -0.62164236 0.3141867 1.2500675    2     1
## 25            Cagayan De Oro -0.59893752 0.3241313 1.2528817    2     1
## 26                    Calaca -1.07512037 0.3052466 1.4355406    2     1
## 27                   Calamba -0.97955971 0.2481273 1.2112277    2     1
## 28                   Calapan -0.96169253 0.2681550 1.2368619    2     1
## 29             Calbayog City -0.48785820 0.2487068 1.0450769    2     1
## 30             Caloocan City -1.25183135 0.3468261 1.4457295    2     1
## 31                    Candon -1.04093809 0.3318137 1.2793991    2     1
## 32                   Canlaon -1.06182348 0.4122336 1.3748979    2     1
## 33                    Carcar -0.64217900 0.4284246 1.3400124    2     1
## 34                   Carmona -0.80395061 0.2847808 1.3109242    2     1
## 35                Catbalogan -0.38219463 0.2017649 1.0645142    2     1
## 36                   Cauayan -0.66594902 0.2400674 1.1251240    2     1
## 37               Cavite City -0.41077394 0.3815632 1.4111375    2     1
## 38                 Cebu City -0.39551562 0.2506373 1.3674793    2     1
## 39                  Cotabato -0.99734348 0.2808658 2.6612244    2     1
## 40                   Dagupan -0.28958629 0.2872666 1.0485454    2     1
## 41                     Danao -0.60764528 0.2560080 1.6489257    2     1
## 42                   Dapitan -0.77230207 0.2318800 1.4276733    2     1
## 43                Dasmariñas -0.75764767 0.3411655 1.4157260    2     1
## 44                     Davao -0.77377249 0.2108231 1.1633735    2     1
## 45                     Digos -0.38024473 0.2056051 1.3359758    2     1
## 46                   Dipolog -0.58415138 0.2348249 1.3951469    2     1
## 47                 Dumaguete -0.29266050 0.2334115 1.1985765    2     1
## 48               El Salvador -1.50340780 0.3723832 1.0031724    2     1
## 49                 Escalante -0.67355433 0.2695139 1.1897099    2     1
## 50                     Gapan -0.79765320 0.2256485 0.8956630    2     1
## 51            General Santos -0.69465192 0.2887955 1.2538355    2     1
## 52             General Trias -0.50973977 0.3673418 1.4923572    2     1
## 53                   Gingoog -0.46694943 0.2786506 1.2124856    2     1
## 54                Guihulngan -0.72921609 0.3736115 1.4102734    2     1
## 55                Himamaylan -0.90401524 0.2862482 1.3340409    2     1
## 56                    Ilagan -0.70604600 0.2528696 1.1091764    2     1
## 57                    Iligan -1.54315534 0.4041389 1.0227126    2     1
## 58                    Iloilo -0.77824000 0.2676563 1.4420570    2     1
## 59                      Imus -0.41432579 0.3814409 1.4071976    2     1
## 60                     Iriga -0.50498074 0.1996753 1.0258237    2     1
## 61                   Isabela -0.59367995 0.2994023 1.0684303    2     1
## 62                Kabankalan -1.29455379 0.3517932 1.2897320    2     1
## 63                 Kidapawan -0.11893750 0.2274839 1.6414597    2     1
## 64                 Koronadal -0.23431530 0.2134059 1.1935332    2     1
## 65                La Carlota -1.30922936 0.3871023 1.2131368    2     1
## 66                   Lamitan -0.98524929 0.2803325 1.3951102    2     1
## 67                     Laoag -1.13988249 0.3884598 1.1897649    2     1
## 68                 Lapu-Lapu -0.51688597 0.2307461 1.3952408    2     1
## 69                 Las Piñas -0.42393778 0.3820966 1.4072020    2     1
## 70                   Legazpi -0.63848513 0.1906205 1.6685714    2     1
## 71                     Ligao -0.43353409 0.2089628 1.2883190    2     1
## 72                      Lipa -1.18273389 0.3224111 1.3701552    2     1
## 73                    Lucena -0.73580569 0.2716203 1.0543646    2     1
## 74                    Maasin -0.59645610 0.2305769 0.9651812    2     1
## 75                 Mabalacat -0.94415674 0.3136919 1.2521737    2     1
## 76                    Makati -0.52713967 0.2183046 1.2651779    2     1
## 77                   Malabon -1.09353683 0.3057363 1.0998179    2     1
## 78                Malaybalay -1.25119706 0.2852295 1.1960334    2     1
## 79                   Malolos -1.06220501 0.3154222 1.0931538    2     1
## 80               Mandaluyong -0.81073268 0.2133379 1.5594732    2     1
## 81                   Mandaue -0.51698418 0.2301736 1.3970249    2     1
## 82                    Manila -0.87905611 0.2973391 1.1609152    2     1
## 83                    Marawi -1.09800764 0.3526521 1.2202409    2     1
## 84                  Marikina -0.79212975 0.1969987 1.1152347    2     1
## 85              Masbate City -0.46380272 0.1787169 1.7727601    2     1
## 86                      Mati -0.67944317 0.1731879 1.1905982    2     1
## 87                Meycauayan -1.07663126 0.3008716 1.0869311    2     1
## 88                     Muñoz -0.44263520 0.1563333 0.9475298    2     1
## 89                Muntinlupa -0.70936721 0.2769019 1.3759903    2     1
## 90                Naga(Cebu) -0.20413672 0.3519429 1.6098727    2     1
## 91                  Naga(CS) -0.69790189 0.2722267 1.4366836    2     1
## 92                   Navotas -1.07884069 0.3018903 1.1010309    2     1
## 93                  Olongapo -1.11363069 0.3896675 1.0592702    2     1
## 94                     Ormoc -0.48629161 0.2463756 0.9755902    2     1
## 95                 Oroquieta -0.90546746 0.2994037 1.5499795    2     1
## 96                    Ozamis -1.02192727 0.2937599 1.3055068    2     1
## 97                  Pagadian -0.77524485 0.2839697 1.2639962    2     1
## 98                   Palayan -0.63622638 0.1980590 1.0775772    2     1
## 99                    Panabo -1.01357366 0.2688188 1.2159809    2     1
## 100                Parañaque -0.41239356 0.3815836 1.4115441    2     1
## 101                    Pasay -0.81833340 0.2152789 1.5399122    2     1
## 102                    Pasig -0.76137922 0.1976888 1.5642197    2     1
## 103                    Passi -0.62891249 0.2800152 1.5786202    2     1
## 104          Puerto Princesa -1.57491211 0.3416402 1.6040633    2     1
## 105              Quezon City -0.73775239 0.2204231 1.1277277    2     1
## 106                    Roxas -0.41633329 0.3511404 1.4434919    2     1
## 107                    Sagay -0.67473774 0.2684112 1.0348236    2     1
## 108                    Samal -0.88492037 0.2684140 1.0970680    2     1
## 109 San Carlos (Negros Occ.) -0.58248685 0.3486703 1.4278088    2     1
## 110  San Carlos (Pangasinan) -0.06828187 0.3090511 1.1639775    2     1
## 111  San Fernando (La Union) -1.26428414 0.3236048 1.3902987    2     1
## 112  San Fernando (Pampanga) -0.85980255 0.2155218 1.0234117    2     1
## 113                 San Jose -0.79117087 0.2479803 1.3685397    2     1
## 114       San Jose del Monte -0.91311582 0.2462803 1.0058646    2     1
## 115                 San Juan -0.82221005 0.1981375 1.2125646    2     1
## 116                San Pablo -0.92658677 0.2805907 1.1267223    2     1
## 117                San Pedro -0.70570363 0.2743604 1.3777957    2     1
## 118               Santa Rosa -0.74866212 0.2956128 1.1545006    2     1
## 119                 Santiago -0.61690945 0.2042448 1.1722930    2     1
## 120              Santo Tomas -1.26961910 0.2860313 1.3993814    2     1
## 121                    Silay -0.60306441 0.2366567 1.6152104    2     1
## 122                  Sipalay -0.89311132 0.2572759 1.6158178    2     1
## 123            Sorsogon City -0.72762520 0.1810347 1.4475050    2     1
## 124             Surigao City -0.63216469 0.1469482 0.9450387    2     1
## 125                   Tabaco -0.76388482 0.1920594 1.5102421    2     1
## 126                    Tabuk -0.95443747 0.2733515 2.0999911    2     1
## 127                 Tacloban -0.32757461 0.1948755 1.0821066    2     1
## 128                 Tacurong -0.44216478 0.2637825 1.7148755    2     1
## 129                 Tagaytay -0.80804649 0.3328152 1.3187183    2     1
## 130               Tagbilaran -0.60346335 0.2953214 1.2144012    2     1
## 131                   Taguig -0.52875362 0.2189862 1.2667230    2     1
## 132                    Tagum -0.41106657 0.1741120 1.5226089    2     1
## 133            Talisay(Cebu) -0.40330338 0.2501572 1.3380406    2     1
## 134     Talisay(Negros Occ.) -0.79296379 0.2806636 1.6541585    2     1
## 135                  Tanauan -1.26961910 0.2860313 1.3993814    2     1
## 136                   Tandag -0.54592419 0.2494536 1.0621133    2     1
## 137                   Tangub -0.68615179 0.2227208 1.5088745    2     1
## 138                   Tanjay -0.72328721 0.3468574 1.4077462    2     1
## 139              Tarlac City -0.55083855 0.2417319 1.1828616    2     1
## 140                  Tayabas -0.63528881 0.2119154 1.0203277    2     1
## 141                   Toledo -0.41828084 0.2750333 1.6833266    2     1
## 142           Trece Martires -0.77143492 0.3420926 1.3221137    2     1
## 143               Tuguegarao -0.91663546 0.2688035 1.1205989    2     1
## 144                 Urdaneta -0.33124709 0.3278737 1.1021763    2     1
## 145                 Valencia -1.77497648 0.3694475 1.5895878    2     1
## 146               Valenzuela -1.06747777 0.3023612 1.0997191    2     1
## 147                Victorias -0.52390674 0.2623040 1.2983906    2     1
## 148                    Vigan -1.01868603 0.3171901 1.4408254    2     1
## 149                Zamboanga -0.79344729 0.2894794 0.8710290    2     1
##           wysq       wusq tnorm
## 1   0.02407348 0.02694556    25
## 2   0.02463873 0.02715158    25
## 3   0.06732201 0.08373166    25
## 4   0.03551764 0.06217562    25
## 5   0.03113824 0.06232693    25
## 6   0.02633452 0.04927638    25
## 7   0.01864414 0.06180606    25
## 8   0.02249397 0.04178530    25
## 9   0.03722000 0.03982233    25
## 10  0.02848539 0.03047444    25
## 11  0.02410073 0.04271413    25
## 12  0.02941831 0.05001209    25
## 13  0.02985337 0.06480308    25
## 14  0.03005268 0.04460262    25
## 15  0.03181043 0.03295336    25
## 16  0.05783297 0.10051586    25
## 17  0.03389102 0.02911827    25
## 18  0.04769043 0.05475776    25
## 19  0.02664175 0.04089999    25
## 20  0.03206847 0.03514390    25
## 21  0.03642898 0.05919309    25
## 22  0.03117808 0.04182142    25
## 23  0.05787984 0.07839172    25
## 24  0.03794727 0.05929901    25
## 25  0.02063921 0.03239762    25
## 26  0.03202920 0.06600504    25
## 27  0.05779976 0.08479644    25
## 28  0.02947889 0.04509761    25
## 29  0.03677045 0.04016016    25
## 30  0.02857860 0.05973309    25
## 31  0.01802094 0.02949779    25
## 32  0.02260527 0.04273175    25
## 33  0.01925995 0.03458381    25
## 34  0.05721675 0.09832826    25
## 35  0.03845490 0.04357673    25
## 36  0.02114146 0.02676306    25
## 37  0.03113824 0.06200587    25
## 38  0.02594643 0.04851981    25
## 39  0.01712241 0.12126287    25
## 40  0.02493564 0.02741543    25
## 41  0.02549695 0.06932508    25
## 42  0.02480548 0.05055978    25
## 43  0.03112407 0.06238136    25
## 44  0.01754747 0.02374941    25
## 45  0.01881289 0.03357783    25
## 46  0.02964547 0.05770298    25
## 47  0.02648674 0.03805047    25
## 48  0.01621442 0.01631746    25
## 49  0.03788654 0.05362497    25
## 50  0.03784899 0.03036292    25
## 51  0.02238382 0.03518968    25
## 52  0.03111113 0.06928853    25
## 53  0.03247346 0.04773992    25
## 54  0.01992385 0.03962597    25
## 55  0.02482918 0.04418763    25
## 56  0.04507622 0.05545603    25
## 57  0.01626594 0.01701322    25
## 58  0.03777182 0.07854758    25
## 59  0.03115969 0.06170258    25
## 60  0.04340200 0.04567254    25
## 61  0.01816802 0.02073958    25
## 62  0.02509206 0.04173836    25
## 63  0.02101088 0.05661151    25
## 64  0.01701268 0.02423493    25
## 65  0.02633452 0.03875654    25
## 66  0.01806894 0.03516817    25
## 67  0.02869221 0.04061499    25
## 68  0.02594643 0.05050983    25
## 69  0.03120223 0.06178720    25
## 70  0.03204699 0.08922301    25
## 71  0.03223496 0.05350249    25
## 72  0.03932298 0.07382202    25
## 73  0.05473903 0.06085255    25
## 74  0.03428303 0.03193720    25
## 75  0.02473090 0.03877654    25
## 76  0.05806740 0.09294705    25
## 77  0.02860452 0.03460001    25
## 78  0.02692457 0.03851548    25
## 79  0.02851692 0.03407731    25
## 80  0.05775042 0.14044651    25
## 81  0.02594643 0.05063908    25
## 82  0.02861184 0.03856086    25
## 83  0.01865497 0.02777703    25
## 84  0.06732201 0.08373166    25
## 85  0.04065160 0.12775491    25
## 86  0.03399942 0.04819500    25
## 87  0.02861085 0.03380142    25
## 88  0.03080318 0.02765548    25
## 89  0.05767946 0.10920738    25
## 90  0.02357084 0.06108830    25
## 91  0.04400644 0.09083193    25
## 92  0.02844701 0.03448543    25
## 93  0.03707704 0.04160241    25
## 94  0.03793324 0.03610396    25
## 95  0.01744661 0.04191438    25
## 96  0.02062642 0.03515461    25
## 97  0.02519189 0.04024874    25
## 98  0.05860968 0.06805595    25
## 99  0.01765484 0.02610462    25
## 100 0.03116230 0.06208954    25
## 101 0.05777011 0.13699197    25
## 102 0.05786853 0.14159176    25
## 103 0.03567732 0.08890936    25
## 104 0.03439791 0.08850648    25
## 105 0.06766601 0.08605558    25
## 106 0.03297875 0.06871681    25
## 107 0.03885568 0.04160899    25
## 108 0.01763932 0.02122994    25
## 109 0.02304898 0.04698852    25
## 110 0.02498427 0.03384978    25
## 111 0.01781855 0.03444202    25
## 112 0.03787619 0.03967044    25
## 113 0.03684789 0.06901244    25
## 114 0.06542454 0.06619417    25
## 115 0.06832295 0.10045612    25
## 116 0.03937081 0.04998137    25
## 117 0.05781224 0.10974618    25
## 118 0.05768109 0.07688149    25
## 119 0.02116982 0.02909307    25
## 120 0.03917023 0.07670583    25
## 121 0.03500401 0.09132212    25
## 122 0.02773774 0.07241955    25
## 123 0.04592916 0.09623402    25
## 124 0.03758064 0.03356321    25
## 125 0.03219243 0.07342551    25
## 126 0.03045653 0.13431216    25
## 127 0.03724596 0.04361334    25
## 128 0.01370205 0.04029497    25
## 129 0.03217458 0.05595218    25
## 130 0.03434502 0.05065102    25
## 131 0.05815411 0.09331333    25
## 132 0.02330910 0.05403838    25
## 133 0.02548589 0.04562874    25
## 134 0.03546014 0.09702747    25
## 135 0.03917023 0.07670583    25
## 136 0.03313818 0.03738267    25
## 137 0.02521414 0.05740508    25
## 138 0.02217189 0.04393913    25
## 139 0.03086727 0.04318829    25
## 140 0.05499977 0.05725853    25
## 141 0.02335247 0.06617130    25
## 142 0.03081796 0.05386931    25
## 143 0.03110672 0.03906201    25
## 144 0.02505722 0.03043932    25
## 145 0.02647719 0.06690228    25
## 146 0.02835891 0.03429676    25
## 147 0.03825222 0.06448629    25
## 148 0.02023416 0.04200566    25
## 149 0.01804549 0.01369096    25
## 
## $indiv_data
## $indiv_data$Alaminos
## $indiv_data$Alaminos$gid
## [1] "Alaminos"
## 
## $indiv_data$Alaminos$ai
## [1] -0.8016566
## 
## $indiv_data$Alaminos$seai
## [1] 0.3231062
## 
## $indiv_data$Alaminos$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.14767440     -0.34539228      0.01097088     -0.09898233 
## 
## $indiv_data$Alaminos$blag
## [1] 2
## 
## $indiv_data$Alaminos$blead
## [1] 1
## 
## $indiv_data$Alaminos$ti
## [1] 52
## 
## $indiv_data$Alaminos$tnorm
## [1] 25
## 
## $indiv_data$Alaminos$reg_coef
## NULL
## 
## $indiv_data$Alaminos$aonesemi
## [1] 1.057972
## 
## 
## $indiv_data$`Angeles City`
## $indiv_data$`Angeles City`$gid
## [1] "Angeles City"
## 
## $indiv_data$`Angeles City`$ai
## [1] -0.8259218
## 
## $indiv_data$`Angeles City`$seai
## [1] 0.3215839
## 
## $indiv_data$`Angeles City`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.10614480     -0.24447100      0.02432322     -0.14210046 
## 
## $indiv_data$`Angeles City`$blag
## [1] 2
## 
## $indiv_data$`Angeles City`$blead
## [1] 1
## 
## $indiv_data$`Angeles City`$ti
## [1] 52
## 
## $indiv_data$`Angeles City`$tnorm
## [1] 25
## 
## $indiv_data$`Angeles City`$reg_coef
## NULL
## 
## $indiv_data$`Angeles City`$aonesemi
## [1] 1.049756
## 
## 
## $indiv_data$Antipolo
## $indiv_data$Antipolo$gid
## [1] "Antipolo"
## 
## $indiv_data$Antipolo$ai
## [1] -0.7921298
## 
## $indiv_data$Antipolo$seai
## [1] 0.1969987
## 
## $indiv_data$Antipolo$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##   -0.0006245948    0.6940611090    0.0320348245   -0.0375403948 
## 
## $indiv_data$Antipolo$blag
## [1] 2
## 
## $indiv_data$Antipolo$blead
## [1] 1
## 
## $indiv_data$Antipolo$ti
## [1] 52
## 
## $indiv_data$Antipolo$tnorm
## [1] 25
## 
## $indiv_data$Antipolo$reg_coef
## NULL
## 
## $indiv_data$Antipolo$aonesemi
## [1] 1.115235
## 
## 
## $indiv_data$`Bacolod City`
## $indiv_data$`Bacolod City`$gid
## [1] "Bacolod City"
## 
## $indiv_data$`Bacolod City`$ai
## [1] -0.6098898
## 
## $indiv_data$`Bacolod City`$seai
## [1] 0.2879314
## 
## $indiv_data$`Bacolod City`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.11064750      0.45246631     -0.02570148      0.37197256 
## 
## $indiv_data$`Bacolod City`$blag
## [1] 2
## 
## $indiv_data$`Bacolod City`$blead
## [1] 1
## 
## $indiv_data$`Bacolod City`$ti
## [1] 52
## 
## $indiv_data$`Bacolod City`$tnorm
## [1] 25
## 
## $indiv_data$`Bacolod City`$reg_coef
## NULL
## 
## $indiv_data$`Bacolod City`$aonesemi
## [1] 1.323086
## 
## 
## $indiv_data$Bacoor
## $indiv_data$Bacoor$gid
## [1] "Bacoor"
## 
## $indiv_data$Bacoor$ai
## [1] -0.4124858
## 
## $indiv_data$Bacoor$seai
## [1] 0.3824086
## 
## $indiv_data$Bacoor$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.085383825     0.403661830     0.004663892     0.073574739 
## 
## $indiv_data$Bacoor$blag
## [1] 2
## 
## $indiv_data$Bacoor$blead
## [1] 1
## 
## $indiv_data$Bacoor$ti
## [1] 52
## 
## $indiv_data$Bacoor$tnorm
## [1] 25
## 
## $indiv_data$Bacoor$reg_coef
## NULL
## 
## $indiv_data$Bacoor$aonesemi
## [1] 1.414786
## 
## 
## $indiv_data$`Bago City`
## $indiv_data$`Bago City`$gid
## [1] "Bago City"
## 
## $indiv_data$`Bago City`$ai
## [1] -0.726865
## 
## $indiv_data$`Bago City`$seai
## [1] 0.305835
## 
## $indiv_data$`Bago City`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.09126517      0.44501452     -0.01340965      0.23810072 
## 
## $indiv_data$`Bago City`$blag
## [1] 2
## 
## $indiv_data$`Bago City`$blead
## [1] 1
## 
## $indiv_data$`Bago City`$ti
## [1] 52
## 
## $indiv_data$`Bago City`$tnorm
## [1] 25
## 
## $indiv_data$`Bago City`$reg_coef
## NULL
## 
## $indiv_data$`Bago City`$aonesemi
## [1] 1.367907
## 
## 
## $indiv_data$Baguio
## $indiv_data$Baguio$gid
## [1] "Baguio"
## 
## $indiv_data$Baguio$ai
## [1] -0.6129259
## 
## $indiv_data$Baguio$seai
## [1] 0.2523098
## 
## $indiv_data$Baguio$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.10972111     -0.45663601      0.03706876     -0.22749116 
## 
## $indiv_data$Baguio$blag
## [1] 2
## 
## $indiv_data$Baguio$blead
## [1] 1
## 
## $indiv_data$Baguio$ti
## [1] 52
## 
## $indiv_data$Baguio$tnorm
## [1] 25
## 
## $indiv_data$Baguio$reg_coef
## NULL
## 
## $indiv_data$Baguio$aonesemi
## [1] 1.820725
## 
## 
## $indiv_data$Bais
## $indiv_data$Bais$gid
## [1] "Bais"
## 
## $indiv_data$Bais$ai
## [1] -0.6953232
## 
## $indiv_data$Bais$seai
## [1] 0.3356384
## 
## $indiv_data$Bais$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.066392922     0.356887503     0.003488104     0.121951517 
## 
## $indiv_data$Bais$blag
## [1] 2
## 
## $indiv_data$Bais$blead
## [1] 1
## 
## $indiv_data$Bais$ti
## [1] 52
## 
## $indiv_data$Bais$tnorm
## [1] 25
## 
## $indiv_data$Bais$reg_coef
## NULL
## 
## $indiv_data$Bais$aonesemi
## [1] 1.362946
## 
## 
## $indiv_data$Balanga
## $indiv_data$Balanga$gid
## [1] "Balanga"
## 
## $indiv_data$Balanga$ai
## [1] -1.281773
## 
## $indiv_data$Balanga$seai
## [1] 0.4258863
## 
## $indiv_data$Balanga$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##    0.1490525331   -0.0905662575   -0.0004073817   -0.0022570890 
## 
## $indiv_data$Balanga$blag
## [1] 2
## 
## $indiv_data$Balanga$blead
## [1] 1
## 
## $indiv_data$Balanga$ti
## [1] 52
## 
## $indiv_data$Balanga$tnorm
## [1] 25
## 
## $indiv_data$Balanga$reg_coef
## NULL
## 
## $indiv_data$Balanga$aonesemi
## [1] 1.034368
## 
## 
## $indiv_data$Baliwag
## $indiv_data$Baliwag$gid
## [1] "Baliwag"
## 
## $indiv_data$Baliwag$ai
## [1] -0.8211759
## 
## $indiv_data$Baliwag$seai
## [1] 0.2344967
## 
## $indiv_data$Baliwag$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.12203418     -0.40468225      0.02444269     -0.12147746 
## 
## $indiv_data$Baliwag$blag
## [1] 2
## 
## $indiv_data$Baliwag$blead
## [1] 1
## 
## $indiv_data$Baliwag$ti
## [1] 52
## 
## $indiv_data$Baliwag$tnorm
## [1] 25
## 
## $indiv_data$Baliwag$reg_coef
## NULL
## 
## $indiv_data$Baliwag$aonesemi
## [1] 1.034324
## 
## 
## $indiv_data$`Batac City`
## $indiv_data$`Batac City`$gid
## [1] "Batac City"
## 
## $indiv_data$`Batac City`$ai
## [1] -1.07836
## 
## $indiv_data$`Batac City`$seai
## [1] 0.3327237
## 
## $indiv_data$`Batac City`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.107276956     0.157067539     0.006832661    -0.052770338 
## 
## $indiv_data$`Batac City`$blag
## [1] 2
## 
## $indiv_data$`Batac City`$blead
## [1] 1
## 
## $indiv_data$`Batac City`$ti
## [1] 52
## 
## $indiv_data$`Batac City`$tnorm
## [1] 25
## 
## $indiv_data$`Batac City`$reg_coef
## NULL
## 
## $indiv_data$`Batac City`$aonesemi
## [1] 1.331284
## 
## 
## $indiv_data$Batangas
## $indiv_data$Batangas$gid
## [1] "Batangas"
## 
## $indiv_data$Batangas$ai
## [1] -0.8201675
## 
## $indiv_data$Batangas$seai
## [1] 0.2388178
## 
## $indiv_data$Batangas$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.081493116     0.398246407     0.005776893    -0.032761256 
## 
## $indiv_data$Batangas$blag
## [1] 2
## 
## $indiv_data$Batangas$blead
## [1] 1
## 
## $indiv_data$Batangas$ti
## [1] 52
## 
## $indiv_data$Batangas$tnorm
## [1] 25
## 
## $indiv_data$Batangas$reg_coef
## NULL
## 
## $indiv_data$Batangas$aonesemi
## [1] 1.303853
## 
## 
## $indiv_data$Bayawan
## $indiv_data$Bayawan$gid
## [1] "Bayawan"
## 
## $indiv_data$Bayawan$ai
## [1] -0.5240572
## 
## $indiv_data$Bayawan$seai
## [1] 0.3256368
## 
## $indiv_data$Bayawan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.02903797      1.09243151     -0.01710111      0.43288679 
## 
## $indiv_data$Bayawan$blag
## [1] 2
## 
## $indiv_data$Bayawan$blead
## [1] 1
## 
## $indiv_data$Bayawan$ti
## [1] 52
## 
## $indiv_data$Bayawan$tnorm
## [1] 25
## 
## $indiv_data$Bayawan$reg_coef
## NULL
## 
## $indiv_data$Bayawan$aonesemi
## [1] 1.473334
## 
## 
## $indiv_data$Baybay
## $indiv_data$Baybay$gid
## [1] "Baybay"
## 
## $indiv_data$Baybay$ai
## [1] -0.5701636
## 
## $indiv_data$Baybay$seai
## [1] 0.1955426
## 
## $indiv_data$Baybay$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.18068144      0.76346942     -0.05353134      0.18726313 
## 
## $indiv_data$Baybay$blag
## [1] 2
## 
## $indiv_data$Baybay$blead
## [1] 1
## 
## $indiv_data$Baybay$ti
## [1] 52
## 
## $indiv_data$Baybay$tnorm
## [1] 25
## 
## $indiv_data$Baybay$reg_coef
## NULL
## 
## $indiv_data$Baybay$aonesemi
## [1] 1.218256
## 
## 
## $indiv_data$Bayugan
## $indiv_data$Bayugan$gid
## [1] "Bayugan"
## 
## $indiv_data$Bayugan$ai
## [1] -0.3996597
## 
## $indiv_data$Bayugan$seai
## [1] 0.1432205
## 
## $indiv_data$Bayugan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     -0.02189095      1.89055643     -0.01742925      0.68415137 
## 
## $indiv_data$Bayugan$blag
## [1] 2
## 
## $indiv_data$Bayugan$blead
## [1] 1
## 
## $indiv_data$Bayugan$ti
## [1] 52
## 
## $indiv_data$Bayugan$tnorm
## [1] 25
## 
## $indiv_data$Bayugan$reg_coef
## NULL
## 
## $indiv_data$Bayugan$aonesemi
## [1] 1.017806
## 
## 
## $indiv_data$Biñan
## $indiv_data$Biñan$gid
## [1] "Biñan"
## 
## $indiv_data$Biñan$ai
## [1] -0.825682
## 
## $indiv_data$Biñan$seai
## [1] 0.2860276
## 
## $indiv_data$Biñan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.06474876     -0.61045701      0.05306377     -0.27002716 
## 
## $indiv_data$Biñan$blag
## [1] 2
## 
## $indiv_data$Biñan$blead
## [1] 1
## 
## $indiv_data$Biñan$ti
## [1] 52
## 
## $indiv_data$Biñan$tnorm
## [1] 25
## 
## $indiv_data$Biñan$reg_coef
## NULL
## 
## $indiv_data$Biñan$aonesemi
## [1] 1.318346
## 
## 
## $indiv_data$Bislig
## $indiv_data$Bislig$gid
## [1] "Bislig"
## 
## $indiv_data$Bislig$ai
## [1] -0.3049523
## 
## $indiv_data$Bislig$seai
## [1] 0.2244085
## 
## $indiv_data$Bislig$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.11092134      0.20751241     -0.01084898      0.25307749 
## 
## $indiv_data$Bislig$blag
## [1] 2
## 
## $indiv_data$Bislig$blead
## [1] 1
## 
## $indiv_data$Bislig$ti
## [1] 52
## 
## $indiv_data$Bislig$tnorm
## [1] 25
## 
## $indiv_data$Bislig$reg_coef
## NULL
## 
## $indiv_data$Bislig$aonesemi
## [1] 0.9269162
## 
## 
## $indiv_data$Bogo
## $indiv_data$Bogo$gid
## [1] "Bogo"
## 
## $indiv_data$Bogo$ai
## [1] -0.3393828
## 
## $indiv_data$Bogo$seai
## [1] 0.2116747
## 
## $indiv_data$Bogo$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##       0.2599402       0.8546691      -0.1177425       1.0293954 
## 
## $indiv_data$Bogo$blag
## [1] 2
## 
## $indiv_data$Bogo$blead
## [1] 1
## 
## $indiv_data$Bogo$ti
## [1] 52
## 
## $indiv_data$Bogo$tnorm
## [1] 25
## 
## $indiv_data$Bogo$reg_coef
## NULL
## 
## $indiv_data$Bogo$aonesemi
## [1] 1.071537
## 
## 
## $indiv_data$Borongan
## $indiv_data$Borongan$gid
## [1] "Borongan"
## 
## $indiv_data$Borongan$ai
## [1] -0.4058604
## 
## $indiv_data$Borongan$seai
## [1] 0.1854283
## 
## $indiv_data$Borongan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##       0.1574241       0.7251816      -0.0668191       0.8423665 
## 
## $indiv_data$Borongan$blag
## [1] 2
## 
## $indiv_data$Borongan$blead
## [1] 1
## 
## $indiv_data$Borongan$ti
## [1] 52
## 
## $indiv_data$Borongan$tnorm
## [1] 25
## 
## $indiv_data$Borongan$reg_coef
## NULL
## 
## $indiv_data$Borongan$aonesemi
## [1] 1.239025
## 
## 
## $indiv_data$Butuan
## $indiv_data$Butuan$gid
## [1] "Butuan"
## 
## $indiv_data$Butuan$ai
## [1] -0.4196874
## 
## $indiv_data$Butuan$seai
## [1] 0.1843674
## 
## $indiv_data$Butuan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     -0.10906712      1.78374456      0.03588009      0.02500215 
## 
## $indiv_data$Butuan$blag
## [1] 2
## 
## $indiv_data$Butuan$blead
## [1] 1
## 
## $indiv_data$Butuan$ti
## [1] 52
## 
## $indiv_data$Butuan$tnorm
## [1] 25
## 
## $indiv_data$Butuan$reg_coef
## NULL
## 
## $indiv_data$Butuan$aonesemi
## [1] 1.046853
## 
## 
## $indiv_data$Cabadbaran
## $indiv_data$Cabadbaran$gid
## [1] "Cabadbaran"
## 
## $indiv_data$Cabadbaran$ai
## [1] -0.4400887
## 
## $indiv_data$Cabadbaran$seai
## [1] 0.1837261
## 
## $indiv_data$Cabadbaran$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.112966370     0.209954184     0.007370465    -0.209245222 
## 
## $indiv_data$Cabadbaran$blag
## [1] 2
## 
## $indiv_data$Cabadbaran$blead
## [1] 1
## 
## $indiv_data$Cabadbaran$ti
## [1] 52
## 
## $indiv_data$Cabadbaran$tnorm
## [1] 25
## 
## $indiv_data$Cabadbaran$reg_coef
## NULL
## 
## $indiv_data$Cabadbaran$aonesemi
## [1] 1.274712
## 
## 
## $indiv_data$`Cabanatuan City`
## $indiv_data$`Cabanatuan City`$gid
## [1] "Cabanatuan City"
## 
## $indiv_data$`Cabanatuan City`$ai
## [1] -0.6160988
## 
## $indiv_data$`Cabanatuan City`$seai
## [1] 0.2483694
## 
## $indiv_data$`Cabanatuan City`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.02734995      0.64461388      0.02448317      0.01656859 
## 
## $indiv_data$`Cabanatuan City`$blag
## [1] 2
## 
## $indiv_data$`Cabanatuan City`$blead
## [1] 1
## 
## $indiv_data$`Cabanatuan City`$ti
## [1] 52
## 
## $indiv_data$`Cabanatuan City`$tnorm
## [1] 25
## 
## $indiv_data$`Cabanatuan City`$reg_coef
## NULL
## 
## $indiv_data$`Cabanatuan City`$aonesemi
## [1] 1.158176
## 
## 
## $indiv_data$Cabuyao
## $indiv_data$Cabuyao$gid
## [1] "Cabuyao"
## 
## $indiv_data$Cabuyao$ai
## [1] -0.7371534
## 
## $indiv_data$Cabuyao$seai
## [1] 0.2955746
## 
## $indiv_data$Cabuyao$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.057614863    -0.233186069     0.034321068    -0.002091799 
## 
## $indiv_data$Cabuyao$blag
## [1] 2
## 
## $indiv_data$Cabuyao$blead
## [1] 1
## 
## $indiv_data$Cabuyao$ti
## [1] 52
## 
## $indiv_data$Cabuyao$tnorm
## [1] 25
## 
## $indiv_data$Cabuyao$reg_coef
## NULL
## 
## $indiv_data$Cabuyao$aonesemi
## [1] 1.163781
## 
## 
## $indiv_data$Cadiz
## $indiv_data$Cadiz$gid
## [1] "Cadiz"
## 
## $indiv_data$Cadiz$ai
## [1] -0.6216424
## 
## $indiv_data$Cadiz$seai
## [1] 0.3141867
## 
## $indiv_data$Cadiz$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.15228790      0.32559630     -0.03768249      0.44148635 
## 
## $indiv_data$Cadiz$blag
## [1] 2
## 
## $indiv_data$Cadiz$blead
## [1] 1
## 
## $indiv_data$Cadiz$ti
## [1] 52
## 
## $indiv_data$Cadiz$tnorm
## [1] 25
## 
## $indiv_data$Cadiz$reg_coef
## NULL
## 
## $indiv_data$Cadiz$aonesemi
## [1] 1.250067
## 
## 
## $indiv_data$`Cagayan De Oro`
## $indiv_data$`Cagayan De Oro`$gid
## [1] "Cagayan De Oro"
## 
## $indiv_data$`Cagayan De Oro`$ai
## [1] -0.5989375
## 
## $indiv_data$`Cagayan De Oro`$seai
## [1] 0.3241313
## 
## $indiv_data$`Cagayan De Oro`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.11772773     -0.37401952      0.01182878      0.02814693 
## 
## $indiv_data$`Cagayan De Oro`$blag
## [1] 2
## 
## $indiv_data$`Cagayan De Oro`$blead
## [1] 1
## 
## $indiv_data$`Cagayan De Oro`$ti
## [1] 52
## 
## $indiv_data$`Cagayan De Oro`$tnorm
## [1] 25
## 
## $indiv_data$`Cagayan De Oro`$reg_coef
## NULL
## 
## $indiv_data$`Cagayan De Oro`$aonesemi
## [1] 1.252882
## 
## 
## $indiv_data$Calaca
## $indiv_data$Calaca$gid
## [1] "Calaca"
## 
## $indiv_data$Calaca$ai
## [1] -1.07512
## 
## $indiv_data$Calaca$seai
## [1] 0.3052466
## 
## $indiv_data$Calaca$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.08187118     -0.31539467      0.03663789     -0.21064237 
## 
## $indiv_data$Calaca$blag
## [1] 2
## 
## $indiv_data$Calaca$blead
## [1] 1
## 
## $indiv_data$Calaca$ti
## [1] 52
## 
## $indiv_data$Calaca$tnorm
## [1] 25
## 
## $indiv_data$Calaca$reg_coef
## NULL
## 
## $indiv_data$Calaca$aonesemi
## [1] 1.435541
## 
## 
## $indiv_data$Calamba
## $indiv_data$Calamba$gid
## [1] "Calamba"
## 
## $indiv_data$Calamba$ai
## [1] -0.9795597
## 
## $indiv_data$Calamba$seai
## [1] 0.2481273
## 
## $indiv_data$Calamba$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.06150441     -0.47071108      0.04827479     -0.27777837 
## 
## $indiv_data$Calamba$blag
## [1] 2
## 
## $indiv_data$Calamba$blead
## [1] 1
## 
## $indiv_data$Calamba$ti
## [1] 52
## 
## $indiv_data$Calamba$tnorm
## [1] 25
## 
## $indiv_data$Calamba$reg_coef
## NULL
## 
## $indiv_data$Calamba$aonesemi
## [1] 1.211228
## 
## 
## $indiv_data$Calapan
## $indiv_data$Calapan$gid
## [1] "Calapan"
## 
## $indiv_data$Calapan$ai
## [1] -0.9616925
## 
## $indiv_data$Calapan$seai
## [1] 0.268155
## 
## $indiv_data$Calapan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.107645784     0.200793915     0.006156415    -0.074782691 
## 
## $indiv_data$Calapan$blag
## [1] 2
## 
## $indiv_data$Calapan$blead
## [1] 1
## 
## $indiv_data$Calapan$ti
## [1] 52
## 
## $indiv_data$Calapan$tnorm
## [1] 25
## 
## $indiv_data$Calapan$reg_coef
## NULL
## 
## $indiv_data$Calapan$aonesemi
## [1] 1.236862
## 
## 
## $indiv_data$`Calbayog City`
## $indiv_data$`Calbayog City`$gid
## [1] "Calbayog City"
## 
## $indiv_data$`Calbayog City`$ai
## [1] -0.4878582
## 
## $indiv_data$`Calbayog City`$seai
## [1] 0.2487068
## 
## $indiv_data$`Calbayog City`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.07254835      0.87707829     -0.02201844      0.27103710 
## 
## $indiv_data$`Calbayog City`$blag
## [1] 2
## 
## $indiv_data$`Calbayog City`$blead
## [1] 1
## 
## $indiv_data$`Calbayog City`$ti
## [1] 52
## 
## $indiv_data$`Calbayog City`$tnorm
## [1] 25
## 
## $indiv_data$`Calbayog City`$reg_coef
## NULL
## 
## $indiv_data$`Calbayog City`$aonesemi
## [1] 1.045077
## 
## 
## $indiv_data$`Caloocan City`
## $indiv_data$`Caloocan City`$gid
## [1] "Caloocan City"
## 
## $indiv_data$`Caloocan City`$ai
## [1] -1.251831
## 
## $indiv_data$`Caloocan City`$seai
## [1] 0.3468261
## 
## $indiv_data$`Caloocan City`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.12762315     -0.59571449      0.02416328     -0.06232822 
## 
## $indiv_data$`Caloocan City`$blag
## [1] 2
## 
## $indiv_data$`Caloocan City`$blead
## [1] 1
## 
## $indiv_data$`Caloocan City`$ti
## [1] 52
## 
## $indiv_data$`Caloocan City`$tnorm
## [1] 25
## 
## $indiv_data$`Caloocan City`$reg_coef
## NULL
## 
## $indiv_data$`Caloocan City`$aonesemi
## [1] 1.44573
## 
## 
## $indiv_data$Candon
## $indiv_data$Candon$gid
## [1] "Candon"
## 
## $indiv_data$Candon$ai
## [1] -1.040938
## 
## $indiv_data$Candon$seai
## [1] 0.3318137
## 
## $indiv_data$Candon$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.141668788     0.071755793    -0.002650676    -0.046391681 
## 
## $indiv_data$Candon$blag
## [1] 2
## 
## $indiv_data$Candon$blead
## [1] 1
## 
## $indiv_data$Candon$ti
## [1] 52
## 
## $indiv_data$Candon$tnorm
## [1] 25
## 
## $indiv_data$Candon$reg_coef
## NULL
## 
## $indiv_data$Candon$aonesemi
## [1] 1.279399
## 
## 
## $indiv_data$Canlaon
## $indiv_data$Canlaon$gid
## [1] "Canlaon"
## 
## $indiv_data$Canlaon$ai
## [1] -1.061823
## 
## $indiv_data$Canlaon$seai
## [1] 0.4122336
## 
## $indiv_data$Canlaon$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.07339471      0.22821673      0.01073920      0.05517546 
## 
## $indiv_data$Canlaon$blag
## [1] 2
## 
## $indiv_data$Canlaon$blead
## [1] 1
## 
## $indiv_data$Canlaon$ti
## [1] 52
## 
## $indiv_data$Canlaon$tnorm
## [1] 25
## 
## $indiv_data$Canlaon$reg_coef
## NULL
## 
## $indiv_data$Canlaon$aonesemi
## [1] 1.374898
## 
## 
## $indiv_data$Carcar
## $indiv_data$Carcar$gid
## [1] "Carcar"
## 
## $indiv_data$Carcar$ai
## [1] -0.642179
## 
## $indiv_data$Carcar$seai
## [1] 0.4284246
## 
## $indiv_data$Carcar$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.03097004      0.46540394      0.01717170      0.07178179 
## 
## $indiv_data$Carcar$blag
## [1] 2
## 
## $indiv_data$Carcar$blead
## [1] 1
## 
## $indiv_data$Carcar$ti
## [1] 52
## 
## $indiv_data$Carcar$tnorm
## [1] 25
## 
## $indiv_data$Carcar$reg_coef
## NULL
## 
## $indiv_data$Carcar$aonesemi
## [1] 1.340012
## 
## 
## $indiv_data$Carmona
## $indiv_data$Carmona$gid
## [1] "Carmona"
## 
## $indiv_data$Carmona$ai
## [1] -0.8039506
## 
## $indiv_data$Carmona$seai
## [1] 0.2847808
## 
## $indiv_data$Carmona$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.06360492     -0.57623082      0.05245264     -0.25808356 
## 
## $indiv_data$Carmona$blag
## [1] 2
## 
## $indiv_data$Carmona$blead
## [1] 1
## 
## $indiv_data$Carmona$ti
## [1] 52
## 
## $indiv_data$Carmona$tnorm
## [1] 25
## 
## $indiv_data$Carmona$reg_coef
## NULL
## 
## $indiv_data$Carmona$aonesemi
## [1] 1.310924
## 
## 
## $indiv_data$Catbalogan
## $indiv_data$Catbalogan$gid
## [1] "Catbalogan"
## 
## $indiv_data$Catbalogan$ai
## [1] -0.3821946
## 
## $indiv_data$Catbalogan$seai
## [1] 0.2017649
## 
## $indiv_data$Catbalogan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.04188141      0.92131607     -0.01173251      0.30911769 
## 
## $indiv_data$Catbalogan$blag
## [1] 2
## 
## $indiv_data$Catbalogan$blead
## [1] 1
## 
## $indiv_data$Catbalogan$ti
## [1] 52
## 
## $indiv_data$Catbalogan$tnorm
## [1] 25
## 
## $indiv_data$Catbalogan$reg_coef
## NULL
## 
## $indiv_data$Catbalogan$aonesemi
## [1] 1.064514
## 
## 
## $indiv_data$Cauayan
## $indiv_data$Cauayan$gid
## [1] "Cauayan"
## 
## $indiv_data$Cauayan$ai
## [1] -0.665949
## 
## $indiv_data$Cauayan$seai
## [1] 0.2400674
## 
## $indiv_data$Cauayan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.04013780      0.23190856      0.03421485     -0.18869648 
## 
## $indiv_data$Cauayan$blag
## [1] 2
## 
## $indiv_data$Cauayan$blead
## [1] 1
## 
## $indiv_data$Cauayan$ti
## [1] 52
## 
## $indiv_data$Cauayan$tnorm
## [1] 25
## 
## $indiv_data$Cauayan$reg_coef
## NULL
## 
## $indiv_data$Cauayan$aonesemi
## [1] 1.125124
## 
## 
## $indiv_data$`Cavite City`
## $indiv_data$`Cavite City`$gid
## [1] "Cavite City"
## 
## $indiv_data$`Cavite City`$ai
## [1] -0.4107739
## 
## $indiv_data$`Cavite City`$seai
## [1] 0.3815632
## 
## $indiv_data$`Cavite City`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.085051822     0.393594685     0.005294521     0.068744644 
## 
## $indiv_data$`Cavite City`$blag
## [1] 2
## 
## $indiv_data$`Cavite City`$blead
## [1] 1
## 
## $indiv_data$`Cavite City`$ti
## [1] 52
## 
## $indiv_data$`Cavite City`$tnorm
## [1] 25
## 
## $indiv_data$`Cavite City`$reg_coef
## NULL
## 
## $indiv_data$`Cavite City`$aonesemi
## [1] 1.411138
## 
## 
## $indiv_data$`Cebu City`
## $indiv_data$`Cebu City`$gid
## [1] "Cebu City"
## 
## $indiv_data$`Cebu City`$ai
## [1] -0.3955156
## 
## $indiv_data$`Cebu City`$seai
## [1] 0.2506373
## 
## $indiv_data$`Cebu City`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.05933820      0.95414278     -0.02646222      0.55851273 
## 
## $indiv_data$`Cebu City`$blag
## [1] 2
## 
## $indiv_data$`Cebu City`$blead
## [1] 1
## 
## $indiv_data$`Cebu City`$ti
## [1] 52
## 
## $indiv_data$`Cebu City`$tnorm
## [1] 25
## 
## $indiv_data$`Cebu City`$reg_coef
## NULL
## 
## $indiv_data$`Cebu City`$aonesemi
## [1] 1.367479
## 
## 
## $indiv_data$Cotabato
## $indiv_data$Cotabato$gid
## [1] "Cotabato"
## 
## $indiv_data$Cotabato$ai
## [1] -0.9973435
## 
## $indiv_data$Cotabato$seai
## [1] 0.2808658
## 
## $indiv_data$Cotabato$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     -0.06443075      1.46562361      0.01599560      0.30762250 
## 
## $indiv_data$Cotabato$blag
## [1] 2
## 
## $indiv_data$Cotabato$blead
## [1] 1
## 
## $indiv_data$Cotabato$ti
## [1] 52
## 
## $indiv_data$Cotabato$tnorm
## [1] 25
## 
## $indiv_data$Cotabato$reg_coef
## NULL
## 
## $indiv_data$Cotabato$aonesemi
## [1] 2.661224
## 
## 
## $indiv_data$Dagupan
## $indiv_data$Dagupan$gid
## [1] "Dagupan"
## 
## $indiv_data$Dagupan$ai
## [1] -0.2895863
## 
## $indiv_data$Dagupan$seai
## [1] 0.2872666
## 
## $indiv_data$Dagupan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.06676851     -0.71760519      0.05808270     -0.23007288 
## 
## $indiv_data$Dagupan$blag
## [1] 2
## 
## $indiv_data$Dagupan$blead
## [1] 1
## 
## $indiv_data$Dagupan$ti
## [1] 52
## 
## $indiv_data$Dagupan$tnorm
## [1] 25
## 
## $indiv_data$Dagupan$reg_coef
## NULL
## 
## $indiv_data$Dagupan$aonesemi
## [1] 1.048545
## 
## 
## $indiv_data$Danao
## $indiv_data$Danao$gid
## [1] "Danao"
## 
## $indiv_data$Danao$ai
## [1] -0.6076453
## 
## $indiv_data$Danao$seai
## [1] 0.256008
## 
## $indiv_data$Danao$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.16848106      0.63964639     -0.05833807      0.64277394 
## 
## $indiv_data$Danao$blag
## [1] 2
## 
## $indiv_data$Danao$blead
## [1] 1
## 
## $indiv_data$Danao$ti
## [1] 52
## 
## $indiv_data$Danao$tnorm
## [1] 25
## 
## $indiv_data$Danao$reg_coef
## NULL
## 
## $indiv_data$Danao$aonesemi
## [1] 1.648926
## 
## 
## $indiv_data$Dapitan
## $indiv_data$Dapitan$gid
## [1] "Dapitan"
## 
## $indiv_data$Dapitan$ai
## [1] -0.7723021
## 
## $indiv_data$Dapitan$seai
## [1] 0.23188
## 
## $indiv_data$Dapitan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.14472878      0.50636663     -0.03459172      0.32723912 
## 
## $indiv_data$Dapitan$blag
## [1] 2
## 
## $indiv_data$Dapitan$blead
## [1] 1
## 
## $indiv_data$Dapitan$ti
## [1] 52
## 
## $indiv_data$Dapitan$tnorm
## [1] 25
## 
## $indiv_data$Dapitan$reg_coef
## NULL
## 
## $indiv_data$Dapitan$aonesemi
## [1] 1.427673
## 
## 
## $indiv_data$Dasmariñas
## $indiv_data$Dasmariñas$gid
## [1] "Dasmariñas"
## 
## $indiv_data$Dasmariñas$ai
## [1] -0.7576477
## 
## $indiv_data$Dasmariñas$seai
## [1] 0.3411655
## 
## $indiv_data$Dasmariñas$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.10581461     -0.24243989      0.02572455     -0.11086528 
## 
## $indiv_data$Dasmariñas$blag
## [1] 2
## 
## $indiv_data$Dasmariñas$blead
## [1] 1
## 
## $indiv_data$Dasmariñas$ti
## [1] 52
## 
## $indiv_data$Dasmariñas$tnorm
## [1] 25
## 
## $indiv_data$Dasmariñas$reg_coef
## NULL
## 
## $indiv_data$Dasmariñas$aonesemi
## [1] 1.415726
## 
## 
## $indiv_data$Davao
## $indiv_data$Davao$gid
## [1] "Davao"
## 
## $indiv_data$Davao$ai
## [1] -0.7737725
## 
## $indiv_data$Davao$seai
## [1] 0.2108231
## 
## $indiv_data$Davao$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.08485622     -0.28435687      0.03277736     -0.20296023 
## 
## $indiv_data$Davao$blag
## [1] 2
## 
## $indiv_data$Davao$blead
## [1] 1
## 
## $indiv_data$Davao$ti
## [1] 52
## 
## $indiv_data$Davao$tnorm
## [1] 25
## 
## $indiv_data$Davao$reg_coef
## NULL
## 
## $indiv_data$Davao$aonesemi
## [1] 1.163373
## 
## 
## $indiv_data$Digos
## $indiv_data$Digos$gid
## [1] "Digos"
## 
## $indiv_data$Digos$ai
## [1] -0.3802447
## 
## $indiv_data$Digos$seai
## [1] 0.2056051
## 
## $indiv_data$Digos$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.110286401     0.063146677     0.009481993    -0.125823142 
## 
## $indiv_data$Digos$blag
## [1] 2
## 
## $indiv_data$Digos$blead
## [1] 1
## 
## $indiv_data$Digos$ti
## [1] 52
## 
## $indiv_data$Digos$tnorm
## [1] 25
## 
## $indiv_data$Digos$reg_coef
## NULL
## 
## $indiv_data$Digos$aonesemi
## [1] 1.335976
## 
## 
## $indiv_data$Dipolog
## $indiv_data$Dipolog$gid
## [1] "Dipolog"
## 
## $indiv_data$Dipolog$ai
## [1] -0.5841514
## 
## $indiv_data$Dipolog$seai
## [1] 0.2348249
## 
## $indiv_data$Dipolog$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.12406030      0.35299164     -0.02838622      0.42088044 
## 
## $indiv_data$Dipolog$blag
## [1] 2
## 
## $indiv_data$Dipolog$blead
## [1] 1
## 
## $indiv_data$Dipolog$ti
## [1] 52
## 
## $indiv_data$Dipolog$tnorm
## [1] 25
## 
## $indiv_data$Dipolog$reg_coef
## NULL
## 
## $indiv_data$Dipolog$aonesemi
## [1] 1.395147
## 
## 
## $indiv_data$Dumaguete
## $indiv_data$Dumaguete$gid
## [1] "Dumaguete"
## 
## $indiv_data$Dumaguete$ai
## [1] -0.2926605
## 
## $indiv_data$Dumaguete$seai
## [1] 0.2334115
## 
## $indiv_data$Dumaguete$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.07174768     -0.10249575      0.02185977     -0.09462870 
## 
## $indiv_data$Dumaguete$blag
## [1] 2
## 
## $indiv_data$Dumaguete$blead
## [1] 1
## 
## $indiv_data$Dumaguete$ti
## [1] 52
## 
## $indiv_data$Dumaguete$tnorm
## [1] 25
## 
## $indiv_data$Dumaguete$reg_coef
## NULL
## 
## $indiv_data$Dumaguete$aonesemi
## [1] 1.198576
## 
## 
## $indiv_data$`El Salvador`
## $indiv_data$`El Salvador`$gid
## [1] "El Salvador"
## 
## $indiv_data$`El Salvador`$ai
## [1] -1.503408
## 
## $indiv_data$`El Salvador`$seai
## [1] 0.3723832
## 
## $indiv_data$`El Salvador`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.08941407     -0.05495362      0.02055284     -0.12505241 
## 
## $indiv_data$`El Salvador`$blag
## [1] 2
## 
## $indiv_data$`El Salvador`$blead
## [1] 1
## 
## $indiv_data$`El Salvador`$ti
## [1] 52
## 
## $indiv_data$`El Salvador`$tnorm
## [1] 25
## 
## $indiv_data$`El Salvador`$reg_coef
## NULL
## 
## $indiv_data$`El Salvador`$aonesemi
## [1] 1.003172
## 
## 
## $indiv_data$Escalante
## $indiv_data$Escalante$gid
## [1] "Escalante"
## 
## $indiv_data$Escalante$ai
## [1] -0.6735543
## 
## $indiv_data$Escalante$seai
## [1] 0.2695139
## 
## $indiv_data$Escalante$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.117033250     0.038973870    -0.005260973     0.142116086 
## 
## $indiv_data$Escalante$blag
## [1] 2
## 
## $indiv_data$Escalante$blead
## [1] 1
## 
## $indiv_data$Escalante$ti
## [1] 52
## 
## $indiv_data$Escalante$tnorm
## [1] 25
## 
## $indiv_data$Escalante$reg_coef
## NULL
## 
## $indiv_data$Escalante$aonesemi
## [1] 1.18971
## 
## 
## $indiv_data$Gapan
## $indiv_data$Gapan$gid
## [1] "Gapan"
## 
## $indiv_data$Gapan$ai
## [1] -0.7976532
## 
## $indiv_data$Gapan$seai
## [1] 0.2256485
## 
## $indiv_data$Gapan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.06287801     -0.12629297      0.03418593     -0.07233158 
## 
## $indiv_data$Gapan$blag
## [1] 2
## 
## $indiv_data$Gapan$blead
## [1] 1
## 
## $indiv_data$Gapan$ti
## [1] 52
## 
## $indiv_data$Gapan$tnorm
## [1] 25
## 
## $indiv_data$Gapan$reg_coef
## NULL
## 
## $indiv_data$Gapan$aonesemi
## [1] 0.895663
## 
## 
## $indiv_data$`General Santos`
## $indiv_data$`General Santos`$gid
## [1] "General Santos"
## 
## $indiv_data$`General Santos`$ai
## [1] -0.6946519
## 
## $indiv_data$`General Santos`$seai
## [1] 0.2887955
## 
## $indiv_data$`General Santos`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.10322548     -0.05237531      0.01151942     -0.06977464 
## 
## $indiv_data$`General Santos`$blag
## [1] 2
## 
## $indiv_data$`General Santos`$blead
## [1] 1
## 
## $indiv_data$`General Santos`$ti
## [1] 52
## 
## $indiv_data$`General Santos`$tnorm
## [1] 25
## 
## $indiv_data$`General Santos`$reg_coef
## NULL
## 
## $indiv_data$`General Santos`$aonesemi
## [1] 1.253836
## 
## 
## $indiv_data$`General Trias`
## $indiv_data$`General Trias`$gid
## [1] "General Trias"
## 
## $indiv_data$`General Trias`$ai
## [1] -0.5097398
## 
## $indiv_data$`General Trias`$seai
## [1] 0.3673418
## 
## $indiv_data$`General Trias`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.09880553      0.10639217      0.01116908      0.03249486 
## 
## $indiv_data$`General Trias`$blag
## [1] 2
## 
## $indiv_data$`General Trias`$blead
## [1] 1
## 
## $indiv_data$`General Trias`$ti
## [1] 52
## 
## $indiv_data$`General Trias`$tnorm
## [1] 25
## 
## $indiv_data$`General Trias`$reg_coef
## NULL
## 
## $indiv_data$`General Trias`$aonesemi
## [1] 1.492357
## 
## 
## $indiv_data$Gingoog
## $indiv_data$Gingoog$gid
## [1] "Gingoog"
## 
## $indiv_data$Gingoog$ai
## [1] -0.4669494
## 
## $indiv_data$Gingoog$seai
## [1] 0.2786506
## 
## $indiv_data$Gingoog$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.02480757     -0.52009979      0.04969237     -0.15765651 
## 
## $indiv_data$Gingoog$blag
## [1] 2
## 
## $indiv_data$Gingoog$blead
## [1] 1
## 
## $indiv_data$Gingoog$ti
## [1] 52
## 
## $indiv_data$Gingoog$tnorm
## [1] 25
## 
## $indiv_data$Gingoog$reg_coef
## NULL
## 
## $indiv_data$Gingoog$aonesemi
## [1] 1.212486
## 
## 
## $indiv_data$Guihulngan
## $indiv_data$Guihulngan$gid
## [1] "Guihulngan"
## 
## $indiv_data$Guihulngan$ai
## [1] -0.7292161
## 
## $indiv_data$Guihulngan$seai
## [1] 0.3736115
## 
## $indiv_data$Guihulngan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.05027424      0.14801918      0.01866867      0.01975128 
## 
## $indiv_data$Guihulngan$blag
## [1] 2
## 
## $indiv_data$Guihulngan$blead
## [1] 1
## 
## $indiv_data$Guihulngan$ti
## [1] 52
## 
## $indiv_data$Guihulngan$tnorm
## [1] 25
## 
## $indiv_data$Guihulngan$reg_coef
## NULL
## 
## $indiv_data$Guihulngan$aonesemi
## [1] 1.410273
## 
## 
## $indiv_data$Himamaylan
## $indiv_data$Himamaylan$gid
## [1] "Himamaylan"
## 
## $indiv_data$Himamaylan$ai
## [1] -0.9040152
## 
## $indiv_data$Himamaylan$seai
## [1] 0.2862482
## 
## $indiv_data$Himamaylan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.03471533      0.69792353      0.00686934      0.09395997 
## 
## $indiv_data$Himamaylan$blag
## [1] 2
## 
## $indiv_data$Himamaylan$blead
## [1] 1
## 
## $indiv_data$Himamaylan$ti
## [1] 52
## 
## $indiv_data$Himamaylan$tnorm
## [1] 25
## 
## $indiv_data$Himamaylan$reg_coef
## NULL
## 
## $indiv_data$Himamaylan$aonesemi
## [1] 1.334041
## 
## 
## $indiv_data$Ilagan
## $indiv_data$Ilagan$gid
## [1] "Ilagan"
## 
## $indiv_data$Ilagan$ai
## [1] -0.706046
## 
## $indiv_data$Ilagan$seai
## [1] 0.2528696
## 
## $indiv_data$Ilagan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.01466715      0.06814085      0.04628276     -0.30538702 
## 
## $indiv_data$Ilagan$blag
## [1] 2
## 
## $indiv_data$Ilagan$blead
## [1] 1
## 
## $indiv_data$Ilagan$ti
## [1] 52
## 
## $indiv_data$Ilagan$tnorm
## [1] 25
## 
## $indiv_data$Ilagan$reg_coef
## NULL
## 
## $indiv_data$Ilagan$aonesemi
## [1] 1.109176
## 
## 
## $indiv_data$Iligan
## $indiv_data$Iligan$gid
## [1] "Iligan"
## 
## $indiv_data$Iligan$ai
## [1] -1.543155
## 
## $indiv_data$Iligan$seai
## [1] 0.4041389
## 
## $indiv_data$Iligan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.108082491     0.133136502     0.007812625    -0.051039799 
## 
## $indiv_data$Iligan$blag
## [1] 2
## 
## $indiv_data$Iligan$blead
## [1] 1
## 
## $indiv_data$Iligan$ti
## [1] 52
## 
## $indiv_data$Iligan$tnorm
## [1] 25
## 
## $indiv_data$Iligan$reg_coef
## NULL
## 
## $indiv_data$Iligan$aonesemi
## [1] 1.022713
## 
## 
## $indiv_data$Iloilo
## $indiv_data$Iloilo$gid
## [1] "Iloilo"
## 
## $indiv_data$Iloilo$ai
## [1] -0.77824
## 
## $indiv_data$Iloilo$seai
## [1] 0.2676563
## 
## $indiv_data$Iloilo$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.09506661      0.50622386     -0.02125477      0.35623004 
## 
## $indiv_data$Iloilo$blag
## [1] 2
## 
## $indiv_data$Iloilo$blead
## [1] 1
## 
## $indiv_data$Iloilo$ti
## [1] 52
## 
## $indiv_data$Iloilo$tnorm
## [1] 25
## 
## $indiv_data$Iloilo$reg_coef
## NULL
## 
## $indiv_data$Iloilo$aonesemi
## [1] 1.442057
## 
## 
## $indiv_data$Imus
## $indiv_data$Imus$gid
## [1] "Imus"
## 
## $indiv_data$Imus$ai
## [1] -0.4143258
## 
## $indiv_data$Imus$seai
## [1] 0.3814409
## 
## $indiv_data$Imus$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.086203916     0.389269834     0.004984978     0.069454138 
## 
## $indiv_data$Imus$blag
## [1] 2
## 
## $indiv_data$Imus$blead
## [1] 1
## 
## $indiv_data$Imus$ti
## [1] 52
## 
## $indiv_data$Imus$tnorm
## [1] 25
## 
## $indiv_data$Imus$reg_coef
## NULL
## 
## $indiv_data$Imus$aonesemi
## [1] 1.407198
## 
## 
## $indiv_data$Iriga
## $indiv_data$Iriga$gid
## [1] "Iriga"
## 
## $indiv_data$Iriga$ai
## [1] -0.5049807
## 
## $indiv_data$Iriga$seai
## [1] 0.1996753
## 
## $indiv_data$Iriga$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.050894626     0.580904045     0.008136799     0.023189228 
## 
## $indiv_data$Iriga$blag
## [1] 2
## 
## $indiv_data$Iriga$blead
## [1] 1
## 
## $indiv_data$Iriga$ti
## [1] 52
## 
## $indiv_data$Iriga$tnorm
## [1] 25
## 
## $indiv_data$Iriga$reg_coef
## NULL
## 
## $indiv_data$Iriga$aonesemi
## [1] 1.025824
## 
## 
## $indiv_data$Isabela
## $indiv_data$Isabela$gid
## [1] "Isabela"
## 
## $indiv_data$Isabela$ai
## [1] -0.59368
## 
## $indiv_data$Isabela$seai
## [1] 0.2994023
## 
## $indiv_data$Isabela$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.04216938      0.50369715      0.01632917     -0.04946672 
## 
## $indiv_data$Isabela$blag
## [1] 2
## 
## $indiv_data$Isabela$blead
## [1] 1
## 
## $indiv_data$Isabela$ti
## [1] 52
## 
## $indiv_data$Isabela$tnorm
## [1] 25
## 
## $indiv_data$Isabela$reg_coef
## NULL
## 
## $indiv_data$Isabela$aonesemi
## [1] 1.06843
## 
## 
## $indiv_data$Kabankalan
## $indiv_data$Kabankalan$gid
## [1] "Kabankalan"
## 
## $indiv_data$Kabankalan$ai
## [1] -1.294554
## 
## $indiv_data$Kabankalan$seai
## [1] 0.3517932
## 
## $indiv_data$Kabankalan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.023329347     0.588105087     0.017674771     0.005983451 
## 
## $indiv_data$Kabankalan$blag
## [1] 2
## 
## $indiv_data$Kabankalan$blead
## [1] 1
## 
## $indiv_data$Kabankalan$ti
## [1] 52
## 
## $indiv_data$Kabankalan$tnorm
## [1] 25
## 
## $indiv_data$Kabankalan$reg_coef
## NULL
## 
## $indiv_data$Kabankalan$aonesemi
## [1] 1.289732
## 
## 
## $indiv_data$Kidapawan
## $indiv_data$Kidapawan$gid
## [1] "Kidapawan"
## 
## $indiv_data$Kidapawan$ai
## [1] -0.1189375
## 
## $indiv_data$Kidapawan$seai
## [1] 0.2274839
## 
## $indiv_data$Kidapawan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     -0.04504044      3.84203490     -0.11373645      1.95476428 
## 
## $indiv_data$Kidapawan$blag
## [1] 2
## 
## $indiv_data$Kidapawan$blead
## [1] 1
## 
## $indiv_data$Kidapawan$ti
## [1] 52
## 
## $indiv_data$Kidapawan$tnorm
## [1] 25
## 
## $indiv_data$Kidapawan$reg_coef
## NULL
## 
## $indiv_data$Kidapawan$aonesemi
## [1] 1.64146
## 
## 
## $indiv_data$Koronadal
## $indiv_data$Koronadal$gid
## [1] "Koronadal"
## 
## $indiv_data$Koronadal$ai
## [1] -0.2343153
## 
## $indiv_data$Koronadal$seai
## [1] 0.2134059
## 
## $indiv_data$Koronadal$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.18012254     -1.76706771      0.04013642     -0.55363775 
## 
## $indiv_data$Koronadal$blag
## [1] 2
## 
## $indiv_data$Koronadal$blead
## [1] 1
## 
## $indiv_data$Koronadal$ti
## [1] 52
## 
## $indiv_data$Koronadal$tnorm
## [1] 25
## 
## $indiv_data$Koronadal$reg_coef
## NULL
## 
## $indiv_data$Koronadal$aonesemi
## [1] 1.193533
## 
## 
## $indiv_data$`La Carlota`
## $indiv_data$`La Carlota`$gid
## [1] "La Carlota"
## 
## $indiv_data$`La Carlota`$ai
## [1] -1.309229
## 
## $indiv_data$`La Carlota`$seai
## [1] 0.3871023
## 
## $indiv_data$`La Carlota`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.06474145      0.36627614      0.01263346     -0.02559465 
## 
## $indiv_data$`La Carlota`$blag
## [1] 2
## 
## $indiv_data$`La Carlota`$blead
## [1] 1
## 
## $indiv_data$`La Carlota`$ti
## [1] 52
## 
## $indiv_data$`La Carlota`$tnorm
## [1] 25
## 
## $indiv_data$`La Carlota`$reg_coef
## NULL
## 
## $indiv_data$`La Carlota`$aonesemi
## [1] 1.213137
## 
## 
## $indiv_data$Lamitan
## $indiv_data$Lamitan$gid
## [1] "Lamitan"
## 
## $indiv_data$Lamitan$ai
## [1] -0.9852493
## 
## $indiv_data$Lamitan$seai
## [1] 0.2803325
## 
## $indiv_data$Lamitan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.03068098     -0.18752173      0.05255919     -0.43242712 
## 
## $indiv_data$Lamitan$blag
## [1] 2
## 
## $indiv_data$Lamitan$blead
## [1] 1
## 
## $indiv_data$Lamitan$ti
## [1] 52
## 
## $indiv_data$Lamitan$tnorm
## [1] 25
## 
## $indiv_data$Lamitan$reg_coef
## NULL
## 
## $indiv_data$Lamitan$aonesemi
## [1] 1.39511
## 
## 
## $indiv_data$Laoag
## $indiv_data$Laoag$gid
## [1] "Laoag"
## 
## $indiv_data$Laoag$ai
## [1] -1.139882
## 
## $indiv_data$Laoag$seai
## [1] 0.3884598
## 
## $indiv_data$Laoag$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.109618595     0.183942609     0.003065275    -0.075096264 
## 
## $indiv_data$Laoag$blag
## [1] 2
## 
## $indiv_data$Laoag$blead
## [1] 1
## 
## $indiv_data$Laoag$ti
## [1] 52
## 
## $indiv_data$Laoag$tnorm
## [1] 25
## 
## $indiv_data$Laoag$reg_coef
## NULL
## 
## $indiv_data$Laoag$aonesemi
## [1] 1.189765
## 
## 
## $indiv_data$`Lapu-Lapu`
## $indiv_data$`Lapu-Lapu`$gid
## [1] "Lapu-Lapu"
## 
## $indiv_data$`Lapu-Lapu`$ai
## [1] -0.516886
## 
## $indiv_data$`Lapu-Lapu`$seai
## [1] 0.2307461
## 
## $indiv_data$`Lapu-Lapu`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.15830292      0.66232634     -0.05277946      0.56885927 
## 
## $indiv_data$`Lapu-Lapu`$blag
## [1] 2
## 
## $indiv_data$`Lapu-Lapu`$blead
## [1] 1
## 
## $indiv_data$`Lapu-Lapu`$ti
## [1] 52
## 
## $indiv_data$`Lapu-Lapu`$tnorm
## [1] 25
## 
## $indiv_data$`Lapu-Lapu`$reg_coef
## NULL
## 
## $indiv_data$`Lapu-Lapu`$aonesemi
## [1] 1.395241
## 
## 
## $indiv_data$`Las Piñas`
## $indiv_data$`Las Piñas`$gid
## [1] "Las Piñas"
## 
## $indiv_data$`Las Piñas`$ai
## [1] -0.4239378
## 
## $indiv_data$`Las Piñas`$seai
## [1] 0.3820966
## 
## $indiv_data$`Las Piñas`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.087348281     0.383821722     0.004641708     0.072210959 
## 
## $indiv_data$`Las Piñas`$blag
## [1] 2
## 
## $indiv_data$`Las Piñas`$blead
## [1] 1
## 
## $indiv_data$`Las Piñas`$ti
## [1] 52
## 
## $indiv_data$`Las Piñas`$tnorm
## [1] 25
## 
## $indiv_data$`Las Piñas`$reg_coef
## NULL
## 
## $indiv_data$`Las Piñas`$aonesemi
## [1] 1.407202
## 
## 
## $indiv_data$Legazpi
## $indiv_data$Legazpi$gid
## [1] "Legazpi"
## 
## $indiv_data$Legazpi$ai
## [1] -0.6384851
## 
## $indiv_data$Legazpi$seai
## [1] 0.1906205
## 
## $indiv_data$Legazpi$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.11195372      0.56872783     -0.04064807      0.62467252 
## 
## $indiv_data$Legazpi$blag
## [1] 2
## 
## $indiv_data$Legazpi$blead
## [1] 1
## 
## $indiv_data$Legazpi$ti
## [1] 52
## 
## $indiv_data$Legazpi$tnorm
## [1] 25
## 
## $indiv_data$Legazpi$reg_coef
## NULL
## 
## $indiv_data$Legazpi$aonesemi
## [1] 1.668571
## 
## 
## $indiv_data$Ligao
## $indiv_data$Ligao$gid
## [1] "Ligao"
## 
## $indiv_data$Ligao$ai
## [1] -0.4335341
## 
## $indiv_data$Ligao$seai
## [1] 0.2089628
## 
## $indiv_data$Ligao$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     -0.03681056      0.92115802      0.03313164     -0.07607985 
## 
## $indiv_data$Ligao$blag
## [1] 2
## 
## $indiv_data$Ligao$blead
## [1] 1
## 
## $indiv_data$Ligao$ti
## [1] 52
## 
## $indiv_data$Ligao$tnorm
## [1] 25
## 
## $indiv_data$Ligao$reg_coef
## NULL
## 
## $indiv_data$Ligao$aonesemi
## [1] 1.288319
## 
## 
## $indiv_data$Lipa
## $indiv_data$Lipa$gid
## [1] "Lipa"
## 
## $indiv_data$Lipa$ai
## [1] -1.182734
## 
## $indiv_data$Lipa$seai
## [1] 0.3224111
## 
## $indiv_data$Lipa$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.07834912     -0.40704590      0.03735085     -0.14945488 
## 
## $indiv_data$Lipa$blag
## [1] 2
## 
## $indiv_data$Lipa$blead
## [1] 1
## 
## $indiv_data$Lipa$ti
## [1] 52
## 
## $indiv_data$Lipa$tnorm
## [1] 25
## 
## $indiv_data$Lipa$reg_coef
## NULL
## 
## $indiv_data$Lipa$aonesemi
## [1] 1.370155
## 
## 
## $indiv_data$Lucena
## $indiv_data$Lucena$gid
## [1] "Lucena"
## 
## $indiv_data$Lucena$ai
## [1] -0.7358057
## 
## $indiv_data$Lucena$seai
## [1] 0.2716203
## 
## $indiv_data$Lucena$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.082999398     0.004513545     0.017519601    -0.040187889 
## 
## $indiv_data$Lucena$blag
## [1] 2
## 
## $indiv_data$Lucena$blead
## [1] 1
## 
## $indiv_data$Lucena$ti
## [1] 52
## 
## $indiv_data$Lucena$tnorm
## [1] 25
## 
## $indiv_data$Lucena$reg_coef
## NULL
## 
## $indiv_data$Lucena$aonesemi
## [1] 1.054365
## 
## 
## $indiv_data$Maasin
## $indiv_data$Maasin$gid
## [1] "Maasin"
## 
## $indiv_data$Maasin$ai
## [1] -0.5964561
## 
## $indiv_data$Maasin$seai
## [1] 0.2305769
## 
## $indiv_data$Maasin$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     -0.04547223      1.14913140      0.01748575      0.16697181 
## 
## $indiv_data$Maasin$blag
## [1] 2
## 
## $indiv_data$Maasin$blead
## [1] 1
## 
## $indiv_data$Maasin$ti
## [1] 52
## 
## $indiv_data$Maasin$tnorm
## [1] 25
## 
## $indiv_data$Maasin$reg_coef
## NULL
## 
## $indiv_data$Maasin$aonesemi
## [1] 0.9651812
## 
## 
## $indiv_data$Mabalacat
## $indiv_data$Mabalacat$gid
## [1] "Mabalacat"
## 
## $indiv_data$Mabalacat$ai
## [1] -0.9441567
## 
## $indiv_data$Mabalacat$seai
## [1] 0.3136919
## 
## $indiv_data$Mabalacat$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.10557393      0.00337361      0.01358850     -0.03055379 
## 
## $indiv_data$Mabalacat$blag
## [1] 2
## 
## $indiv_data$Mabalacat$blead
## [1] 1
## 
## $indiv_data$Mabalacat$ti
## [1] 52
## 
## $indiv_data$Mabalacat$tnorm
## [1] 25
## 
## $indiv_data$Mabalacat$reg_coef
## NULL
## 
## $indiv_data$Mabalacat$aonesemi
## [1] 1.252174
## 
## 
## $indiv_data$Makati
## $indiv_data$Makati$gid
## [1] "Makati"
## 
## $indiv_data$Makati$ai
## [1] -0.5271397
## 
## $indiv_data$Makati$seai
## [1] 0.2183046
## 
## $indiv_data$Makati$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     -0.02607375      0.92261332      0.02871139      0.03886992 
## 
## $indiv_data$Makati$blag
## [1] 2
## 
## $indiv_data$Makati$blead
## [1] 1
## 
## $indiv_data$Makati$ti
## [1] 52
## 
## $indiv_data$Makati$tnorm
## [1] 25
## 
## $indiv_data$Makati$reg_coef
## NULL
## 
## $indiv_data$Makati$aonesemi
## [1] 1.265178
## 
## 
## $indiv_data$Malabon
## $indiv_data$Malabon$gid
## [1] "Malabon"
## 
## $indiv_data$Malabon$ai
## [1] -1.093537
## 
## $indiv_data$Malabon$seai
## [1] 0.3057363
## 
## $indiv_data$Malabon$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.11072237     -0.35098774      0.02600588     -0.09668407 
## 
## $indiv_data$Malabon$blag
## [1] 2
## 
## $indiv_data$Malabon$blead
## [1] 1
## 
## $indiv_data$Malabon$ti
## [1] 52
## 
## $indiv_data$Malabon$tnorm
## [1] 25
## 
## $indiv_data$Malabon$reg_coef
## NULL
## 
## $indiv_data$Malabon$aonesemi
## [1] 1.099818
## 
## 
## $indiv_data$Malaybalay
## $indiv_data$Malaybalay$gid
## [1] "Malaybalay"
## 
## $indiv_data$Malaybalay$ai
## [1] -1.251197
## 
## $indiv_data$Malaybalay$seai
## [1] 0.2852295
## 
## $indiv_data$Malaybalay$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.12433264     -0.60416141      0.02074719     -0.12700774 
## 
## $indiv_data$Malaybalay$blag
## [1] 2
## 
## $indiv_data$Malaybalay$blead
## [1] 1
## 
## $indiv_data$Malaybalay$ti
## [1] 52
## 
## $indiv_data$Malaybalay$tnorm
## [1] 25
## 
## $indiv_data$Malaybalay$reg_coef
## NULL
## 
## $indiv_data$Malaybalay$aonesemi
## [1] 1.196033
## 
## 
## $indiv_data$Malolos
## $indiv_data$Malolos$gid
## [1] "Malolos"
## 
## $indiv_data$Malolos$ai
## [1] -1.062205
## 
## $indiv_data$Malolos$seai
## [1] 0.3154222
## 
## $indiv_data$Malolos$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.12122650     -0.39472892      0.02589103     -0.12778751 
## 
## $indiv_data$Malolos$blag
## [1] 2
## 
## $indiv_data$Malolos$blead
## [1] 1
## 
## $indiv_data$Malolos$ti
## [1] 52
## 
## $indiv_data$Malolos$tnorm
## [1] 25
## 
## $indiv_data$Malolos$reg_coef
## NULL
## 
## $indiv_data$Malolos$aonesemi
## [1] 1.093154
## 
## 
## $indiv_data$Mandaluyong
## $indiv_data$Mandaluyong$gid
## [1] "Mandaluyong"
## 
## $indiv_data$Mandaluyong$ai
## [1] -0.8107327
## 
## $indiv_data$Mandaluyong$seai
## [1] 0.2133379
## 
## $indiv_data$Mandaluyong$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##    -0.001597194     0.642296828     0.040266115    -0.215974298 
## 
## $indiv_data$Mandaluyong$blag
## [1] 2
## 
## $indiv_data$Mandaluyong$blead
## [1] 1
## 
## $indiv_data$Mandaluyong$ti
## [1] 52
## 
## $indiv_data$Mandaluyong$tnorm
## [1] 25
## 
## $indiv_data$Mandaluyong$reg_coef
## NULL
## 
## $indiv_data$Mandaluyong$aonesemi
## [1] 1.559473
## 
## 
## $indiv_data$Mandaue
## $indiv_data$Mandaue$gid
## [1] "Mandaue"
## 
## $indiv_data$Mandaue$ai
## [1] -0.5169842
## 
## $indiv_data$Mandaue$seai
## [1] 0.2301736
## 
## $indiv_data$Mandaue$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##       0.1595388       0.6620161      -0.0531922       0.5696030 
## 
## $indiv_data$Mandaue$blag
## [1] 2
## 
## $indiv_data$Mandaue$blead
## [1] 1
## 
## $indiv_data$Mandaue$ti
## [1] 52
## 
## $indiv_data$Mandaue$tnorm
## [1] 25
## 
## $indiv_data$Mandaue$reg_coef
## NULL
## 
## $indiv_data$Mandaue$aonesemi
## [1] 1.397025
## 
## 
## $indiv_data$Manila
## $indiv_data$Manila$gid
## [1] "Manila"
## 
## $indiv_data$Manila$ai
## [1] -0.8790561
## 
## $indiv_data$Manila$seai
## [1] 0.2973391
## 
## $indiv_data$Manila$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.10975301     -0.15781223      0.01940645     -0.04346680 
## 
## $indiv_data$Manila$blag
## [1] 2
## 
## $indiv_data$Manila$blead
## [1] 1
## 
## $indiv_data$Manila$ti
## [1] 52
## 
## $indiv_data$Manila$tnorm
## [1] 25
## 
## $indiv_data$Manila$reg_coef
## NULL
## 
## $indiv_data$Manila$aonesemi
## [1] 1.160915
## 
## 
## $indiv_data$Marawi
## $indiv_data$Marawi$gid
## [1] "Marawi"
## 
## $indiv_data$Marawi$ai
## [1] -1.098008
## 
## $indiv_data$Marawi$seai
## [1] 0.3526521
## 
## $indiv_data$Marawi$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.11172542     -0.08726398      0.01860002     -0.19181450 
## 
## $indiv_data$Marawi$blag
## [1] 2
## 
## $indiv_data$Marawi$blead
## [1] 1
## 
## $indiv_data$Marawi$ti
## [1] 52
## 
## $indiv_data$Marawi$tnorm
## [1] 25
## 
## $indiv_data$Marawi$reg_coef
## NULL
## 
## $indiv_data$Marawi$aonesemi
## [1] 1.220241
## 
## 
## $indiv_data$Marikina
## $indiv_data$Marikina$gid
## [1] "Marikina"
## 
## $indiv_data$Marikina$ai
## [1] -0.7921298
## 
## $indiv_data$Marikina$seai
## [1] 0.1969987
## 
## $indiv_data$Marikina$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##   -0.0006245948    0.6940611090    0.0320348245   -0.0375403948 
## 
## $indiv_data$Marikina$blag
## [1] 2
## 
## $indiv_data$Marikina$blead
## [1] 1
## 
## $indiv_data$Marikina$ti
## [1] 52
## 
## $indiv_data$Marikina$tnorm
## [1] 25
## 
## $indiv_data$Marikina$reg_coef
## NULL
## 
## $indiv_data$Marikina$aonesemi
## [1] 1.115235
## 
## 
## $indiv_data$`Masbate City`
## $indiv_data$`Masbate City`$gid
## [1] "Masbate City"
## 
## $indiv_data$`Masbate City`$ai
## [1] -0.4638027
## 
## $indiv_data$`Masbate City`$seai
## [1] 0.1787169
## 
## $indiv_data$`Masbate City`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##       0.1540203       2.0698234      -0.1197359       1.1698769 
## 
## $indiv_data$`Masbate City`$blag
## [1] 2
## 
## $indiv_data$`Masbate City`$blead
## [1] 1
## 
## $indiv_data$`Masbate City`$ti
## [1] 52
## 
## $indiv_data$`Masbate City`$tnorm
## [1] 25
## 
## $indiv_data$`Masbate City`$reg_coef
## NULL
## 
## $indiv_data$`Masbate City`$aonesemi
## [1] 1.77276
## 
## 
## $indiv_data$Mati
## $indiv_data$Mati$gid
## [1] "Mati"
## 
## $indiv_data$Mati$ai
## [1] -0.6794432
## 
## $indiv_data$Mati$seai
## [1] 0.1731879
## 
## $indiv_data$Mati$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     -0.02920960      0.67966627      0.03613917     -0.07115404 
## 
## $indiv_data$Mati$blag
## [1] 2
## 
## $indiv_data$Mati$blead
## [1] 1
## 
## $indiv_data$Mati$ti
## [1] 52
## 
## $indiv_data$Mati$tnorm
## [1] 25
## 
## $indiv_data$Mati$reg_coef
## NULL
## 
## $indiv_data$Mati$aonesemi
## [1] 1.190598
## 
## 
## $indiv_data$Meycauayan
## $indiv_data$Meycauayan$gid
## [1] "Meycauayan"
## 
## $indiv_data$Meycauayan$ai
## [1] -1.076631
## 
## $indiv_data$Meycauayan$seai
## [1] 0.3008716
## 
## $indiv_data$Meycauayan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.11033089     -0.34432006      0.02563744     -0.09192334 
## 
## $indiv_data$Meycauayan$blag
## [1] 2
## 
## $indiv_data$Meycauayan$blead
## [1] 1
## 
## $indiv_data$Meycauayan$ti
## [1] 52
## 
## $indiv_data$Meycauayan$tnorm
## [1] 25
## 
## $indiv_data$Meycauayan$reg_coef
## NULL
## 
## $indiv_data$Meycauayan$aonesemi
## [1] 1.086931
## 
## 
## $indiv_data$Muñoz
## $indiv_data$Muñoz$gid
## [1] "Muñoz"
## 
## $indiv_data$Muñoz$ai
## [1] -0.4426352
## 
## $indiv_data$Muñoz$seai
## [1] 0.1563333
## 
## $indiv_data$Muñoz$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.02230663      0.58272236      0.02427268      0.05483253 
## 
## $indiv_data$Muñoz$blag
## [1] 2
## 
## $indiv_data$Muñoz$blead
## [1] 1
## 
## $indiv_data$Muñoz$ti
## [1] 52
## 
## $indiv_data$Muñoz$tnorm
## [1] 25
## 
## $indiv_data$Muñoz$reg_coef
## NULL
## 
## $indiv_data$Muñoz$aonesemi
## [1] 0.9475298
## 
## 
## $indiv_data$Muntinlupa
## $indiv_data$Muntinlupa$gid
## [1] "Muntinlupa"
## 
## $indiv_data$Muntinlupa$ai
## [1] -0.7093672
## 
## $indiv_data$Muntinlupa$seai
## [1] 0.2769019
## 
## $indiv_data$Muntinlupa$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.05286784     -0.26480921      0.04277803     -0.15889541 
## 
## $indiv_data$Muntinlupa$blag
## [1] 2
## 
## $indiv_data$Muntinlupa$blead
## [1] 1
## 
## $indiv_data$Muntinlupa$ti
## [1] 52
## 
## $indiv_data$Muntinlupa$tnorm
## [1] 25
## 
## $indiv_data$Muntinlupa$reg_coef
## NULL
## 
## $indiv_data$Muntinlupa$aonesemi
## [1] 1.37599
## 
## 
## $indiv_data$`Naga(Cebu)`
## $indiv_data$`Naga(Cebu)`$gid
## [1] "Naga(Cebu)"
## 
## $indiv_data$`Naga(Cebu)`$ai
## [1] -0.2041367
## 
## $indiv_data$`Naga(Cebu)`$seai
## [1] 0.3519429
## 
## $indiv_data$`Naga(Cebu)`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     -0.11899061      3.19309427     -0.05509302      1.27909654 
## 
## $indiv_data$`Naga(Cebu)`$blag
## [1] 2
## 
## $indiv_data$`Naga(Cebu)`$blead
## [1] 1
## 
## $indiv_data$`Naga(Cebu)`$ti
## [1] 52
## 
## $indiv_data$`Naga(Cebu)`$tnorm
## [1] 25
## 
## $indiv_data$`Naga(Cebu)`$reg_coef
## NULL
## 
## $indiv_data$`Naga(Cebu)`$aonesemi
## [1] 1.609873
## 
## 
## $indiv_data$`Naga(CS)`
## $indiv_data$`Naga(CS)`$gid
## [1] "Naga(CS)"
## 
## $indiv_data$`Naga(CS)`$ai
## [1] -0.6979019
## 
## $indiv_data$`Naga(CS)`$seai
## [1] 0.2722267
## 
## $indiv_data$`Naga(CS)`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.08367214      0.08399209      0.01700031     -0.14764755 
## 
## $indiv_data$`Naga(CS)`$blag
## [1] 2
## 
## $indiv_data$`Naga(CS)`$blead
## [1] 1
## 
## $indiv_data$`Naga(CS)`$ti
## [1] 52
## 
## $indiv_data$`Naga(CS)`$tnorm
## [1] 25
## 
## $indiv_data$`Naga(CS)`$reg_coef
## NULL
## 
## $indiv_data$`Naga(CS)`$aonesemi
## [1] 1.436684
## 
## 
## $indiv_data$Navotas
## $indiv_data$Navotas$gid
## [1] "Navotas"
## 
## $indiv_data$Navotas$ai
## [1] -1.078841
## 
## $indiv_data$Navotas$seai
## [1] 0.3018903
## 
## $indiv_data$Navotas$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.11141506     -0.35507100      0.02575114     -0.09404607 
## 
## $indiv_data$Navotas$blag
## [1] 2
## 
## $indiv_data$Navotas$blead
## [1] 1
## 
## $indiv_data$Navotas$ti
## [1] 52
## 
## $indiv_data$Navotas$tnorm
## [1] 25
## 
## $indiv_data$Navotas$reg_coef
## NULL
## 
## $indiv_data$Navotas$aonesemi
## [1] 1.101031
## 
## 
## $indiv_data$Olongapo
## $indiv_data$Olongapo$gid
## [1] "Olongapo"
## 
## $indiv_data$Olongapo$ai
## [1] -1.113631
## 
## $indiv_data$Olongapo$seai
## [1] 0.3896675
## 
## $indiv_data$Olongapo$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##    0.1421070272   -0.0597897445   -0.0008939095    0.0550881406 
## 
## $indiv_data$Olongapo$blag
## [1] 2
## 
## $indiv_data$Olongapo$blead
## [1] 1
## 
## $indiv_data$Olongapo$ti
## [1] 52
## 
## $indiv_data$Olongapo$tnorm
## [1] 25
## 
## $indiv_data$Olongapo$reg_coef
## NULL
## 
## $indiv_data$Olongapo$aonesemi
## [1] 1.05927
## 
## 
## $indiv_data$Ormoc
## $indiv_data$Ormoc$gid
## [1] "Ormoc"
## 
## $indiv_data$Ormoc$ai
## [1] -0.4862916
## 
## $indiv_data$Ormoc$seai
## [1] 0.2463756
## 
## $indiv_data$Ormoc$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.02656527      0.34074624      0.02673226     -0.05402587 
## 
## $indiv_data$Ormoc$blag
## [1] 2
## 
## $indiv_data$Ormoc$blead
## [1] 1
## 
## $indiv_data$Ormoc$ti
## [1] 52
## 
## $indiv_data$Ormoc$tnorm
## [1] 25
## 
## $indiv_data$Ormoc$reg_coef
## NULL
## 
## $indiv_data$Ormoc$aonesemi
## [1] 0.9755902
## 
## 
## $indiv_data$Oroquieta
## $indiv_data$Oroquieta$gid
## [1] "Oroquieta"
## 
## $indiv_data$Oroquieta$ai
## [1] -0.9054675
## 
## $indiv_data$Oroquieta$seai
## [1] 0.2994037
## 
## $indiv_data$Oroquieta$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.03759889      0.14711127      0.02761775     -0.01894203 
## 
## $indiv_data$Oroquieta$blag
## [1] 2
## 
## $indiv_data$Oroquieta$blead
## [1] 1
## 
## $indiv_data$Oroquieta$ti
## [1] 52
## 
## $indiv_data$Oroquieta$tnorm
## [1] 25
## 
## $indiv_data$Oroquieta$reg_coef
## NULL
## 
## $indiv_data$Oroquieta$aonesemi
## [1] 1.54998
## 
## 
## $indiv_data$Ozamis
## $indiv_data$Ozamis$gid
## [1] "Ozamis"
## 
## $indiv_data$Ozamis$ai
## [1] -1.021927
## 
## $indiv_data$Ozamis$seai
## [1] 0.2937599
## 
## $indiv_data$Ozamis$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.122328471     0.140568994    -0.007390087     0.125184456 
## 
## $indiv_data$Ozamis$blag
## [1] 2
## 
## $indiv_data$Ozamis$blead
## [1] 1
## 
## $indiv_data$Ozamis$ti
## [1] 52
## 
## $indiv_data$Ozamis$tnorm
## [1] 25
## 
## $indiv_data$Ozamis$reg_coef
## NULL
## 
## $indiv_data$Ozamis$aonesemi
## [1] 1.305507
## 
## 
## $indiv_data$Pagadian
## $indiv_data$Pagadian$gid
## [1] "Pagadian"
## 
## $indiv_data$Pagadian$ai
## [1] -0.7752448
## 
## $indiv_data$Pagadian$seai
## [1] 0.2839697
## 
## $indiv_data$Pagadian$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.03369776     -0.01497544      0.04842224     -0.39296297 
## 
## $indiv_data$Pagadian$blag
## [1] 2
## 
## $indiv_data$Pagadian$blead
## [1] 1
## 
## $indiv_data$Pagadian$ti
## [1] 52
## 
## $indiv_data$Pagadian$tnorm
## [1] 25
## 
## $indiv_data$Pagadian$reg_coef
## NULL
## 
## $indiv_data$Pagadian$aonesemi
## [1] 1.263996
## 
## 
## $indiv_data$Palayan
## $indiv_data$Palayan$gid
## [1] "Palayan"
## 
## $indiv_data$Palayan$ai
## [1] -0.6362264
## 
## $indiv_data$Palayan$seai
## [1] 0.198059
## 
## $indiv_data$Palayan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##    -0.008972957     1.226507554     0.016072698     0.114632373 
## 
## $indiv_data$Palayan$blag
## [1] 2
## 
## $indiv_data$Palayan$blead
## [1] 1
## 
## $indiv_data$Palayan$ti
## [1] 52
## 
## $indiv_data$Palayan$tnorm
## [1] 25
## 
## $indiv_data$Palayan$reg_coef
## NULL
## 
## $indiv_data$Palayan$aonesemi
## [1] 1.077577
## 
## 
## $indiv_data$Panabo
## $indiv_data$Panabo$gid
## [1] "Panabo"
## 
## $indiv_data$Panabo$ai
## [1] -1.013574
## 
## $indiv_data$Panabo$seai
## [1] 0.2688188
## 
## $indiv_data$Panabo$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.08273357     -0.39935508      0.03497681     -0.17221116 
## 
## $indiv_data$Panabo$blag
## [1] 2
## 
## $indiv_data$Panabo$blead
## [1] 1
## 
## $indiv_data$Panabo$ti
## [1] 52
## 
## $indiv_data$Panabo$tnorm
## [1] 25
## 
## $indiv_data$Panabo$reg_coef
## NULL
## 
## $indiv_data$Panabo$aonesemi
## [1] 1.215981
## 
## 
## $indiv_data$Parañaque
## $indiv_data$Parañaque$gid
## [1] "Parañaque"
## 
## $indiv_data$Parañaque$ai
## [1] -0.4123936
## 
## $indiv_data$Parañaque$seai
## [1] 0.3815836
## 
## $indiv_data$Parañaque$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.085809318     0.386003701     0.005312956     0.067739788 
## 
## $indiv_data$Parañaque$blag
## [1] 2
## 
## $indiv_data$Parañaque$blead
## [1] 1
## 
## $indiv_data$Parañaque$ti
## [1] 52
## 
## $indiv_data$Parañaque$tnorm
## [1] 25
## 
## $indiv_data$Parañaque$reg_coef
## NULL
## 
## $indiv_data$Parañaque$aonesemi
## [1] 1.411544
## 
## 
## $indiv_data$Pasay
## $indiv_data$Pasay$gid
## [1] "Pasay"
## 
## $indiv_data$Pasay$ai
## [1] -0.8183334
## 
## $indiv_data$Pasay$seai
## [1] 0.2152789
## 
## $indiv_data$Pasay$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##    0.0004106454    0.6253537408    0.0397331111   -0.2093985767 
## 
## $indiv_data$Pasay$blag
## [1] 2
## 
## $indiv_data$Pasay$blead
## [1] 1
## 
## $indiv_data$Pasay$ti
## [1] 52
## 
## $indiv_data$Pasay$tnorm
## [1] 25
## 
## $indiv_data$Pasay$reg_coef
## NULL
## 
## $indiv_data$Pasay$aonesemi
## [1] 1.539912
## 
## 
## $indiv_data$Pasig
## $indiv_data$Pasig$gid
## [1] "Pasig"
## 
## $indiv_data$Pasig$ai
## [1] -0.7613792
## 
## $indiv_data$Pasig$seai
## [1] 0.1976888
## 
## $indiv_data$Pasig$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     -0.02081546      1.06358608      0.03108699     -0.07402112 
## 
## $indiv_data$Pasig$blag
## [1] 2
## 
## $indiv_data$Pasig$blead
## [1] 1
## 
## $indiv_data$Pasig$ti
## [1] 52
## 
## $indiv_data$Pasig$tnorm
## [1] 25
## 
## $indiv_data$Pasig$reg_coef
## NULL
## 
## $indiv_data$Pasig$aonesemi
## [1] 1.56422
## 
## 
## $indiv_data$Passi
## $indiv_data$Passi$gid
## [1] "Passi"
## 
## $indiv_data$Passi$ai
## [1] -0.6289125
## 
## $indiv_data$Passi$seai
## [1] 0.2800152
## 
## $indiv_data$Passi$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.06197720      0.98702719     -0.02223671      0.38002798 
## 
## $indiv_data$Passi$blag
## [1] 2
## 
## $indiv_data$Passi$blead
## [1] 1
## 
## $indiv_data$Passi$ti
## [1] 52
## 
## $indiv_data$Passi$tnorm
## [1] 25
## 
## $indiv_data$Passi$reg_coef
## NULL
## 
## $indiv_data$Passi$aonesemi
## [1] 1.57862
## 
## 
## $indiv_data$`Puerto Princesa`
## $indiv_data$`Puerto Princesa`$gid
## [1] "Puerto Princesa"
## 
## $indiv_data$`Puerto Princesa`$ai
## [1] -1.574912
## 
## $indiv_data$`Puerto Princesa`$seai
## [1] 0.3416402
## 
## $indiv_data$`Puerto Princesa`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.109970275     0.365139050    -0.004815916     0.044907441 
## 
## $indiv_data$`Puerto Princesa`$blag
## [1] 2
## 
## $indiv_data$`Puerto Princesa`$blead
## [1] 1
## 
## $indiv_data$`Puerto Princesa`$ti
## [1] 52
## 
## $indiv_data$`Puerto Princesa`$tnorm
## [1] 25
## 
## $indiv_data$`Puerto Princesa`$reg_coef
## NULL
## 
## $indiv_data$`Puerto Princesa`$aonesemi
## [1] 1.604063
## 
## 
## $indiv_data$`Quezon City`
## $indiv_data$`Quezon City`$gid
## [1] "Quezon City"
## 
## $indiv_data$`Quezon City`$ai
## [1] -0.7377524
## 
## $indiv_data$`Quezon City`$seai
## [1] 0.2204231
## 
## $indiv_data$`Quezon City`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##    -0.002101437     0.920495106     0.029434297    -0.087413055 
## 
## $indiv_data$`Quezon City`$blag
## [1] 2
## 
## $indiv_data$`Quezon City`$blead
## [1] 1
## 
## $indiv_data$`Quezon City`$ti
## [1] 52
## 
## $indiv_data$`Quezon City`$tnorm
## [1] 25
## 
## $indiv_data$`Quezon City`$reg_coef
## NULL
## 
## $indiv_data$`Quezon City`$aonesemi
## [1] 1.127728
## 
## 
## $indiv_data$Roxas
## $indiv_data$Roxas$gid
## [1] "Roxas"
## 
## $indiv_data$Roxas$ai
## [1] -0.4163333
## 
## $indiv_data$Roxas$seai
## [1] 0.3511404
## 
## $indiv_data$Roxas$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.15968434      0.42368940     -0.05087422      0.57376596 
## 
## $indiv_data$Roxas$blag
## [1] 2
## 
## $indiv_data$Roxas$blead
## [1] 1
## 
## $indiv_data$Roxas$ti
## [1] 52
## 
## $indiv_data$Roxas$tnorm
## [1] 25
## 
## $indiv_data$Roxas$reg_coef
## NULL
## 
## $indiv_data$Roxas$aonesemi
## [1] 1.443492
## 
## 
## $indiv_data$Sagay
## $indiv_data$Sagay$gid
## [1] "Sagay"
## 
## $indiv_data$Sagay$ai
## [1] -0.6747377
## 
## $indiv_data$Sagay$seai
## [1] 0.2684112
## 
## $indiv_data$Sagay$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.111796897     0.026394355     0.004053939    -0.018972298 
## 
## $indiv_data$Sagay$blag
## [1] 2
## 
## $indiv_data$Sagay$blead
## [1] 1
## 
## $indiv_data$Sagay$ti
## [1] 52
## 
## $indiv_data$Sagay$tnorm
## [1] 25
## 
## $indiv_data$Sagay$reg_coef
## NULL
## 
## $indiv_data$Sagay$aonesemi
## [1] 1.034824
## 
## 
## $indiv_data$Samal
## $indiv_data$Samal$gid
## [1] "Samal"
## 
## $indiv_data$Samal$ai
## [1] -0.8849204
## 
## $indiv_data$Samal$seai
## [1] 0.268414
## 
## $indiv_data$Samal$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.09015950     -0.31870517      0.02426073     -0.02914513 
## 
## $indiv_data$Samal$blag
## [1] 2
## 
## $indiv_data$Samal$blead
## [1] 1
## 
## $indiv_data$Samal$ti
## [1] 52
## 
## $indiv_data$Samal$tnorm
## [1] 25
## 
## $indiv_data$Samal$reg_coef
## NULL
## 
## $indiv_data$Samal$aonesemi
## [1] 1.097068
## 
## 
## $indiv_data$`San Carlos (Negros Occ.)`
## $indiv_data$`San Carlos (Negros Occ.)`$gid
## [1] "San Carlos (Negros Occ.)"
## 
## $indiv_data$`San Carlos (Negros Occ.)`$ai
## [1] -0.5824869
## 
## $indiv_data$`San Carlos (Negros Occ.)`$seai
## [1] 0.3486703
## 
## $indiv_data$`San Carlos (Negros Occ.)`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##    0.1107991179   -0.0069236243   -0.0003056025    0.1194177497 
## 
## $indiv_data$`San Carlos (Negros Occ.)`$blag
## [1] 2
## 
## $indiv_data$`San Carlos (Negros Occ.)`$blead
## [1] 1
## 
## $indiv_data$`San Carlos (Negros Occ.)`$ti
## [1] 52
## 
## $indiv_data$`San Carlos (Negros Occ.)`$tnorm
## [1] 25
## 
## $indiv_data$`San Carlos (Negros Occ.)`$reg_coef
## NULL
## 
## $indiv_data$`San Carlos (Negros Occ.)`$aonesemi
## [1] 1.427809
## 
## 
## $indiv_data$`San Carlos (Pangasinan)`
## $indiv_data$`San Carlos (Pangasinan)`$gid
## [1] "San Carlos (Pangasinan)"
## 
## $indiv_data$`San Carlos (Pangasinan)`$ai
## [1] -0.06828187
## 
## $indiv_data$`San Carlos (Pangasinan)`$seai
## [1] 0.3090511
## 
## $indiv_data$`San Carlos (Pangasinan)`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     -0.06698989     -0.24818036      0.13699111     -1.23265744 
## 
## $indiv_data$`San Carlos (Pangasinan)`$blag
## [1] 2
## 
## $indiv_data$`San Carlos (Pangasinan)`$blead
## [1] 1
## 
## $indiv_data$`San Carlos (Pangasinan)`$ti
## [1] 52
## 
## $indiv_data$`San Carlos (Pangasinan)`$tnorm
## [1] 25
## 
## $indiv_data$`San Carlos (Pangasinan)`$reg_coef
## NULL
## 
## $indiv_data$`San Carlos (Pangasinan)`$aonesemi
## [1] 1.163978
## 
## 
## $indiv_data$`San Fernando (La Union)`
## $indiv_data$`San Fernando (La Union)`$gid
## [1] "San Fernando (La Union)"
## 
## $indiv_data$`San Fernando (La Union)`$ai
## [1] -1.264284
## 
## $indiv_data$`San Fernando (La Union)`$seai
## [1] 0.3236048
## 
## $indiv_data$`San Fernando (La Union)`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.138141281    -0.060113761     0.001369642    -0.044143673 
## 
## $indiv_data$`San Fernando (La Union)`$blag
## [1] 2
## 
## $indiv_data$`San Fernando (La Union)`$blead
## [1] 1
## 
## $indiv_data$`San Fernando (La Union)`$ti
## [1] 52
## 
## $indiv_data$`San Fernando (La Union)`$tnorm
## [1] 25
## 
## $indiv_data$`San Fernando (La Union)`$reg_coef
## NULL
## 
## $indiv_data$`San Fernando (La Union)`$aonesemi
## [1] 1.390299
## 
## 
## $indiv_data$`San Fernando (Pampanga)`
## $indiv_data$`San Fernando (Pampanga)`$gid
## [1] "San Fernando (Pampanga)"
## 
## $indiv_data$`San Fernando (Pampanga)`$ai
## [1] -0.8598026
## 
## $indiv_data$`San Fernando (Pampanga)`$seai
## [1] 0.2155218
## 
## $indiv_data$`San Fernando (Pampanga)`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.05330509      0.04417656      0.03320247     -0.06197473 
## 
## $indiv_data$`San Fernando (Pampanga)`$blag
## [1] 2
## 
## $indiv_data$`San Fernando (Pampanga)`$blead
## [1] 1
## 
## $indiv_data$`San Fernando (Pampanga)`$ti
## [1] 52
## 
## $indiv_data$`San Fernando (Pampanga)`$tnorm
## [1] 25
## 
## $indiv_data$`San Fernando (Pampanga)`$reg_coef
## NULL
## 
## $indiv_data$`San Fernando (Pampanga)`$aonesemi
## [1] 1.023412
## 
## 
## $indiv_data$`San Jose`
## $indiv_data$`San Jose`$gid
## [1] "San Jose"
## 
## $indiv_data$`San Jose`$ai
## [1] -0.7911709
## 
## $indiv_data$`San Jose`$seai
## [1] 0.2479803
## 
## $indiv_data$`San Jose`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.04092723      0.54628900      0.01408720     -0.06472811 
## 
## $indiv_data$`San Jose`$blag
## [1] 2
## 
## $indiv_data$`San Jose`$blead
## [1] 1
## 
## $indiv_data$`San Jose`$ti
## [1] 52
## 
## $indiv_data$`San Jose`$tnorm
## [1] 25
## 
## $indiv_data$`San Jose`$reg_coef
## NULL
## 
## $indiv_data$`San Jose`$aonesemi
## [1] 1.36854
## 
## 
## $indiv_data$`San Jose del Monte`
## $indiv_data$`San Jose del Monte`$gid
## [1] "San Jose del Monte"
## 
## $indiv_data$`San Jose del Monte`$ai
## [1] -0.9131158
## 
## $indiv_data$`San Jose del Monte`$seai
## [1] 0.2462803
## 
## $indiv_data$`San Jose del Monte`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.02549139      0.02821026      0.04095587     -0.09277629 
## 
## $indiv_data$`San Jose del Monte`$blag
## [1] 2
## 
## $indiv_data$`San Jose del Monte`$blead
## [1] 1
## 
## $indiv_data$`San Jose del Monte`$ti
## [1] 52
## 
## $indiv_data$`San Jose del Monte`$tnorm
## [1] 25
## 
## $indiv_data$`San Jose del Monte`$reg_coef
## NULL
## 
## $indiv_data$`San Jose del Monte`$aonesemi
## [1] 1.005865
## 
## 
## $indiv_data$`San Juan`
## $indiv_data$`San Juan`$gid
## [1] "San Juan"
## 
## $indiv_data$`San Juan`$ai
## [1] -0.82221
## 
## $indiv_data$`San Juan`$seai
## [1] 0.1981375
## 
## $indiv_data$`San Juan`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##    -0.002352592     0.541175115     0.040927484    -0.163403238 
## 
## $indiv_data$`San Juan`$blag
## [1] 2
## 
## $indiv_data$`San Juan`$blead
## [1] 1
## 
## $indiv_data$`San Juan`$ti
## [1] 52
## 
## $indiv_data$`San Juan`$tnorm
## [1] 25
## 
## $indiv_data$`San Juan`$reg_coef
## NULL
## 
## $indiv_data$`San Juan`$aonesemi
## [1] 1.212565
## 
## 
## $indiv_data$`San Pablo`
## $indiv_data$`San Pablo`$gid
## [1] "San Pablo"
## 
## $indiv_data$`San Pablo`$ai
## [1] -0.9265868
## 
## $indiv_data$`San Pablo`$seai
## [1] 0.2805907
## 
## $indiv_data$`San Pablo`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.07608812     -0.29440557      0.03178242     -0.11941525 
## 
## $indiv_data$`San Pablo`$blag
## [1] 2
## 
## $indiv_data$`San Pablo`$blead
## [1] 1
## 
## $indiv_data$`San Pablo`$ti
## [1] 52
## 
## $indiv_data$`San Pablo`$tnorm
## [1] 25
## 
## $indiv_data$`San Pablo`$reg_coef
## NULL
## 
## $indiv_data$`San Pablo`$aonesemi
## [1] 1.126722
## 
## 
## $indiv_data$`San Pedro`
## $indiv_data$`San Pedro`$gid
## [1] "San Pedro"
## 
## $indiv_data$`San Pedro`$ai
## [1] -0.7057036
## 
## $indiv_data$`San Pedro`$seai
## [1] 0.2743604
## 
## $indiv_data$`San Pedro`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.05243852     -0.26721348      0.04288399     -0.15881045 
## 
## $indiv_data$`San Pedro`$blag
## [1] 2
## 
## $indiv_data$`San Pedro`$blead
## [1] 1
## 
## $indiv_data$`San Pedro`$ti
## [1] 52
## 
## $indiv_data$`San Pedro`$tnorm
## [1] 25
## 
## $indiv_data$`San Pedro`$reg_coef
## NULL
## 
## $indiv_data$`San Pedro`$aonesemi
## [1] 1.377796
## 
## 
## $indiv_data$`Santa Rosa`
## $indiv_data$`Santa Rosa`$gid
## [1] "Santa Rosa"
## 
## $indiv_data$`Santa Rosa`$ai
## [1] -0.7486621
## 
## $indiv_data$`Santa Rosa`$seai
## [1] 0.2956128
## 
## $indiv_data$`Santa Rosa`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.059063038    -0.245331890     0.034268569    -0.003084862 
## 
## $indiv_data$`Santa Rosa`$blag
## [1] 2
## 
## $indiv_data$`Santa Rosa`$blead
## [1] 1
## 
## $indiv_data$`Santa Rosa`$ti
## [1] 52
## 
## $indiv_data$`Santa Rosa`$tnorm
## [1] 25
## 
## $indiv_data$`Santa Rosa`$reg_coef
## NULL
## 
## $indiv_data$`Santa Rosa`$aonesemi
## [1] 1.154501
## 
## 
## $indiv_data$Santiago
## $indiv_data$Santiago$gid
## [1] "Santiago"
## 
## $indiv_data$Santiago$ai
## [1] -0.6169094
## 
## $indiv_data$Santiago$seai
## [1] 0.2042448
## 
## $indiv_data$Santiago$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.02517760      0.35477260      0.03537432     -0.15157581 
## 
## $indiv_data$Santiago$blag
## [1] 2
## 
## $indiv_data$Santiago$blead
## [1] 1
## 
## $indiv_data$Santiago$ti
## [1] 52
## 
## $indiv_data$Santiago$tnorm
## [1] 25
## 
## $indiv_data$Santiago$reg_coef
## NULL
## 
## $indiv_data$Santiago$aonesemi
## [1] 1.172293
## 
## 
## $indiv_data$`Santo Tomas`
## $indiv_data$`Santo Tomas`$gid
## [1] "Santo Tomas"
## 
## $indiv_data$`Santo Tomas`$ai
## [1] -1.269619
## 
## $indiv_data$`Santo Tomas`$seai
## [1] 0.2860313
## 
## $indiv_data$`Santo Tomas`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.07585021     -0.39862713      0.04028827     -0.23393047 
## 
## $indiv_data$`Santo Tomas`$blag
## [1] 2
## 
## $indiv_data$`Santo Tomas`$blead
## [1] 1
## 
## $indiv_data$`Santo Tomas`$ti
## [1] 52
## 
## $indiv_data$`Santo Tomas`$tnorm
## [1] 25
## 
## $indiv_data$`Santo Tomas`$reg_coef
## NULL
## 
## $indiv_data$`Santo Tomas`$aonesemi
## [1] 1.399381
## 
## 
## $indiv_data$Silay
## $indiv_data$Silay$gid
## [1] "Silay"
## 
## $indiv_data$Silay$ai
## [1] -0.6030644
## 
## $indiv_data$Silay$seai
## [1] 0.2366567
## 
## $indiv_data$Silay$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.15756268      0.57075631     -0.04891222      0.42821106 
## 
## $indiv_data$Silay$blag
## [1] 2
## 
## $indiv_data$Silay$blead
## [1] 1
## 
## $indiv_data$Silay$ti
## [1] 52
## 
## $indiv_data$Silay$tnorm
## [1] 25
## 
## $indiv_data$Silay$reg_coef
## NULL
## 
## $indiv_data$Silay$aonesemi
## [1] 1.61521
## 
## 
## $indiv_data$Sipalay
## $indiv_data$Sipalay$gid
## [1] "Sipalay"
## 
## $indiv_data$Sipalay$ai
## [1] -0.8931113
## 
## $indiv_data$Sipalay$seai
## [1] 0.2572759
## 
## $indiv_data$Sipalay$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.11077311      0.75480459     -0.02678159      0.28991364 
## 
## $indiv_data$Sipalay$blag
## [1] 2
## 
## $indiv_data$Sipalay$blead
## [1] 1
## 
## $indiv_data$Sipalay$ti
## [1] 52
## 
## $indiv_data$Sipalay$tnorm
## [1] 25
## 
## $indiv_data$Sipalay$reg_coef
## NULL
## 
## $indiv_data$Sipalay$aonesemi
## [1] 1.615818
## 
## 
## $indiv_data$`Sorsogon City`
## $indiv_data$`Sorsogon City`$gid
## [1] "Sorsogon City"
## 
## $indiv_data$`Sorsogon City`$ai
## [1] -0.7276252
## 
## $indiv_data$`Sorsogon City`$seai
## [1] 0.1810347
## 
## $indiv_data$`Sorsogon City`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##       0.1216882       0.9278469      -0.0651829       0.8553801 
## 
## $indiv_data$`Sorsogon City`$blag
## [1] 2
## 
## $indiv_data$`Sorsogon City`$blead
## [1] 1
## 
## $indiv_data$`Sorsogon City`$ti
## [1] 52
## 
## $indiv_data$`Sorsogon City`$tnorm
## [1] 25
## 
## $indiv_data$`Sorsogon City`$reg_coef
## NULL
## 
## $indiv_data$`Sorsogon City`$aonesemi
## [1] 1.447505
## 
## 
## $indiv_data$`Surigao City`
## $indiv_data$`Surigao City`$gid
## [1] "Surigao City"
## 
## $indiv_data$`Surigao City`$ai
## [1] -0.6321647
## 
## $indiv_data$`Surigao City`$seai
## [1] 0.1469482
## 
## $indiv_data$`Surigao City`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##       0.1693627       0.3874547      -0.0531687       0.5727491 
## 
## $indiv_data$`Surigao City`$blag
## [1] 2
## 
## $indiv_data$`Surigao City`$blead
## [1] 1
## 
## $indiv_data$`Surigao City`$ti
## [1] 52
## 
## $indiv_data$`Surigao City`$tnorm
## [1] 25
## 
## $indiv_data$`Surigao City`$reg_coef
## NULL
## 
## $indiv_data$`Surigao City`$aonesemi
## [1] 0.9450387
## 
## 
## $indiv_data$Tabaco
## $indiv_data$Tabaco$gid
## [1] "Tabaco"
## 
## $indiv_data$Tabaco$ai
## [1] -0.7638848
## 
## $indiv_data$Tabaco$seai
## [1] 0.1920594
## 
## $indiv_data$Tabaco$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.13516664      0.49156361     -0.03877156      0.55532612 
## 
## $indiv_data$Tabaco$blag
## [1] 2
## 
## $indiv_data$Tabaco$blead
## [1] 1
## 
## $indiv_data$Tabaco$ti
## [1] 52
## 
## $indiv_data$Tabaco$tnorm
## [1] 25
## 
## $indiv_data$Tabaco$reg_coef
## NULL
## 
## $indiv_data$Tabaco$aonesemi
## [1] 1.510242
## 
## 
## $indiv_data$Tabuk
## $indiv_data$Tabuk$gid
## [1] "Tabuk"
## 
## $indiv_data$Tabuk$ai
## [1] -0.9544375
## 
## $indiv_data$Tabuk$seai
## [1] 0.2733515
## 
## $indiv_data$Tabuk$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.17994964      0.29683055     -0.02240961     -0.21958183 
## 
## $indiv_data$Tabuk$blag
## [1] 2
## 
## $indiv_data$Tabuk$blead
## [1] 1
## 
## $indiv_data$Tabuk$ti
## [1] 52
## 
## $indiv_data$Tabuk$tnorm
## [1] 25
## 
## $indiv_data$Tabuk$reg_coef
## NULL
## 
## $indiv_data$Tabuk$aonesemi
## [1] 2.099991
## 
## 
## $indiv_data$Tacloban
## $indiv_data$Tacloban$gid
## [1] "Tacloban"
## 
## $indiv_data$Tacloban$ai
## [1] -0.3275746
## 
## $indiv_data$Tacloban$seai
## [1] 0.1948755
## 
## $indiv_data$Tacloban$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     -0.04885135      0.79230031      0.03309202      0.12087796 
## 
## $indiv_data$Tacloban$blag
## [1] 2
## 
## $indiv_data$Tacloban$blead
## [1] 1
## 
## $indiv_data$Tacloban$ti
## [1] 52
## 
## $indiv_data$Tacloban$tnorm
## [1] 25
## 
## $indiv_data$Tacloban$reg_coef
## NULL
## 
## $indiv_data$Tacloban$aonesemi
## [1] 1.082107
## 
## 
## $indiv_data$Tacurong
## $indiv_data$Tacurong$gid
## [1] "Tacurong"
## 
## $indiv_data$Tacurong$ai
## [1] -0.4421648
## 
## $indiv_data$Tacurong$seai
## [1] 0.2637825
## 
## $indiv_data$Tacurong$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.22806558     -2.09993267      0.03290614     -0.59640234 
## 
## $indiv_data$Tacurong$blag
## [1] 2
## 
## $indiv_data$Tacurong$blead
## [1] 1
## 
## $indiv_data$Tacurong$ti
## [1] 52
## 
## $indiv_data$Tacurong$tnorm
## [1] 25
## 
## $indiv_data$Tacurong$reg_coef
## NULL
## 
## $indiv_data$Tacurong$aonesemi
## [1] 1.714876
## 
## 
## $indiv_data$Tagaytay
## $indiv_data$Tagaytay$gid
## [1] "Tagaytay"
## 
## $indiv_data$Tagaytay$ai
## [1] -0.8080465
## 
## $indiv_data$Tagaytay$seai
## [1] 0.3328152
## 
## $indiv_data$Tagaytay$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.07548099     -0.21859313      0.03505252     -0.15041360 
## 
## $indiv_data$Tagaytay$blag
## [1] 2
## 
## $indiv_data$Tagaytay$blead
## [1] 1
## 
## $indiv_data$Tagaytay$ti
## [1] 52
## 
## $indiv_data$Tagaytay$tnorm
## [1] 25
## 
## $indiv_data$Tagaytay$reg_coef
## NULL
## 
## $indiv_data$Tagaytay$aonesemi
## [1] 1.318718
## 
## 
## $indiv_data$Tagbilaran
## $indiv_data$Tagbilaran$gid
## [1] "Tagbilaran"
## 
## $indiv_data$Tagbilaran$ai
## [1] -0.6034633
## 
## $indiv_data$Tagbilaran$seai
## [1] 0.2953214
## 
## $indiv_data$Tagbilaran$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.02973057      0.07686541      0.04103926     -0.27656561 
## 
## $indiv_data$Tagbilaran$blag
## [1] 2
## 
## $indiv_data$Tagbilaran$blead
## [1] 1
## 
## $indiv_data$Tagbilaran$ti
## [1] 52
## 
## $indiv_data$Tagbilaran$tnorm
## [1] 25
## 
## $indiv_data$Tagbilaran$reg_coef
## NULL
## 
## $indiv_data$Tagbilaran$aonesemi
## [1] 1.214401
## 
## 
## $indiv_data$Taguig
## $indiv_data$Taguig$gid
## [1] "Taguig"
## 
## $indiv_data$Taguig$ai
## [1] -0.5287536
## 
## $indiv_data$Taguig$seai
## [1] 0.2189862
## 
## $indiv_data$Taguig$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     -0.02563023      0.92710962      0.02840585      0.03962254 
## 
## $indiv_data$Taguig$blag
## [1] 2
## 
## $indiv_data$Taguig$blead
## [1] 1
## 
## $indiv_data$Taguig$ti
## [1] 52
## 
## $indiv_data$Taguig$tnorm
## [1] 25
## 
## $indiv_data$Taguig$reg_coef
## NULL
## 
## $indiv_data$Taguig$aonesemi
## [1] 1.266723
## 
## 
## $indiv_data$Tagum
## $indiv_data$Tagum$gid
## [1] "Tagum"
## 
## $indiv_data$Tagum$ai
## [1] -0.4110666
## 
## $indiv_data$Tagum$seai
## [1] 0.174112
## 
## $indiv_data$Tagum$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.143516003    -0.666600440     0.006444202     0.084807017 
## 
## $indiv_data$Tagum$blag
## [1] 2
## 
## $indiv_data$Tagum$blead
## [1] 1
## 
## $indiv_data$Tagum$ti
## [1] 52
## 
## $indiv_data$Tagum$tnorm
## [1] 25
## 
## $indiv_data$Tagum$reg_coef
## NULL
## 
## $indiv_data$Tagum$aonesemi
## [1] 1.522609
## 
## 
## $indiv_data$`Talisay(Cebu)`
## $indiv_data$`Talisay(Cebu)`$gid
## [1] "Talisay(Cebu)"
## 
## $indiv_data$`Talisay(Cebu)`$ai
## [1] -0.4033034
## 
## $indiv_data$`Talisay(Cebu)`$seai
## [1] 0.2501572
## 
## $indiv_data$`Talisay(Cebu)`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.05606981      0.88023093     -0.02123465      0.50234783 
## 
## $indiv_data$`Talisay(Cebu)`$blag
## [1] 2
## 
## $indiv_data$`Talisay(Cebu)`$blead
## [1] 1
## 
## $indiv_data$`Talisay(Cebu)`$ti
## [1] 52
## 
## $indiv_data$`Talisay(Cebu)`$tnorm
## [1] 25
## 
## $indiv_data$`Talisay(Cebu)`$reg_coef
## NULL
## 
## $indiv_data$`Talisay(Cebu)`$aonesemi
## [1] 1.338041
## 
## 
## $indiv_data$`Talisay(Negros Occ.)`
## $indiv_data$`Talisay(Negros Occ.)`$gid
## [1] "Talisay(Negros Occ.)"
## 
## $indiv_data$`Talisay(Negros Occ.)`$ai
## [1] -0.7929638
## 
## $indiv_data$`Talisay(Negros Occ.)`$seai
## [1] 0.2806636
## 
## $indiv_data$`Talisay(Negros Occ.)`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.12417984      0.39561108     -0.02710297      0.32223296 
## 
## $indiv_data$`Talisay(Negros Occ.)`$blag
## [1] 2
## 
## $indiv_data$`Talisay(Negros Occ.)`$blead
## [1] 1
## 
## $indiv_data$`Talisay(Negros Occ.)`$ti
## [1] 52
## 
## $indiv_data$`Talisay(Negros Occ.)`$tnorm
## [1] 25
## 
## $indiv_data$`Talisay(Negros Occ.)`$reg_coef
## NULL
## 
## $indiv_data$`Talisay(Negros Occ.)`$aonesemi
## [1] 1.654158
## 
## 
## $indiv_data$Tanauan
## $indiv_data$Tanauan$gid
## [1] "Tanauan"
## 
## $indiv_data$Tanauan$ai
## [1] -1.269619
## 
## $indiv_data$Tanauan$seai
## [1] 0.2860313
## 
## $indiv_data$Tanauan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.07585021     -0.39862713      0.04028827     -0.23393047 
## 
## $indiv_data$Tanauan$blag
## [1] 2
## 
## $indiv_data$Tanauan$blead
## [1] 1
## 
## $indiv_data$Tanauan$ti
## [1] 52
## 
## $indiv_data$Tanauan$tnorm
## [1] 25
## 
## $indiv_data$Tanauan$reg_coef
## NULL
## 
## $indiv_data$Tanauan$aonesemi
## [1] 1.399381
## 
## 
## $indiv_data$Tandag
## $indiv_data$Tandag$gid
## [1] "Tandag"
## 
## $indiv_data$Tandag$ai
## [1] -0.5459242
## 
## $indiv_data$Tandag$seai
## [1] 0.2494536
## 
## $indiv_data$Tandag$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.058000101     0.783641297    -0.005361883     0.213694190 
## 
## $indiv_data$Tandag$blag
## [1] 2
## 
## $indiv_data$Tandag$blead
## [1] 1
## 
## $indiv_data$Tandag$ti
## [1] 52
## 
## $indiv_data$Tandag$tnorm
## [1] 25
## 
## $indiv_data$Tandag$reg_coef
## NULL
## 
## $indiv_data$Tandag$aonesemi
## [1] 1.062113
## 
## 
## $indiv_data$Tangub
## $indiv_data$Tangub$gid
## [1] "Tangub"
## 
## $indiv_data$Tangub$ai
## [1] -0.6861518
## 
## $indiv_data$Tangub$seai
## [1] 0.2227208
## 
## $indiv_data$Tangub$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.109966703    -0.119962463     0.004561834     0.069064792 
## 
## $indiv_data$Tangub$blag
## [1] 2
## 
## $indiv_data$Tangub$blead
## [1] 1
## 
## $indiv_data$Tangub$ti
## [1] 52
## 
## $indiv_data$Tangub$tnorm
## [1] 25
## 
## $indiv_data$Tangub$reg_coef
## NULL
## 
## $indiv_data$Tangub$aonesemi
## [1] 1.508874
## 
## 
## $indiv_data$Tanjay
## $indiv_data$Tanjay$gid
## [1] "Tanjay"
## 
## $indiv_data$Tanjay$ai
## [1] -0.7232872
## 
## $indiv_data$Tanjay$seai
## [1] 0.3468574
## 
## $indiv_data$Tanjay$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.063137721     0.326791105     0.005765875     0.119494935 
## 
## $indiv_data$Tanjay$blag
## [1] 2
## 
## $indiv_data$Tanjay$blead
## [1] 1
## 
## $indiv_data$Tanjay$ti
## [1] 52
## 
## $indiv_data$Tanjay$tnorm
## [1] 25
## 
## $indiv_data$Tanjay$reg_coef
## NULL
## 
## $indiv_data$Tanjay$aonesemi
## [1] 1.407746
## 
## 
## $indiv_data$`Tarlac City`
## $indiv_data$`Tarlac City`$gid
## [1] "Tarlac City"
## 
## $indiv_data$`Tarlac City`$ai
## [1] -0.5508386
## 
## $indiv_data$`Tarlac City`$seai
## [1] 0.2417319
## 
## $indiv_data$`Tarlac City`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.04397807      0.52226279      0.02257211     -0.01693385 
## 
## $indiv_data$`Tarlac City`$blag
## [1] 2
## 
## $indiv_data$`Tarlac City`$blead
## [1] 1
## 
## $indiv_data$`Tarlac City`$ti
## [1] 52
## 
## $indiv_data$`Tarlac City`$tnorm
## [1] 25
## 
## $indiv_data$`Tarlac City`$reg_coef
## NULL
## 
## $indiv_data$`Tarlac City`$aonesemi
## [1] 1.182862
## 
## 
## $indiv_data$Tayabas
## $indiv_data$Tayabas$gid
## [1] "Tayabas"
## 
## $indiv_data$Tayabas$ai
## [1] -0.6352888
## 
## $indiv_data$Tayabas$seai
## [1] 0.2119154
## 
## $indiv_data$Tayabas$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.087720556     0.164916993     0.004009392     0.159028260 
## 
## $indiv_data$Tayabas$blag
## [1] 2
## 
## $indiv_data$Tayabas$blead
## [1] 1
## 
## $indiv_data$Tayabas$ti
## [1] 52
## 
## $indiv_data$Tayabas$tnorm
## [1] 25
## 
## $indiv_data$Tayabas$reg_coef
## NULL
## 
## $indiv_data$Tayabas$aonesemi
## [1] 1.020328
## 
## 
## $indiv_data$Toledo
## $indiv_data$Toledo$gid
## [1] "Toledo"
## 
## $indiv_data$Toledo$ai
## [1] -0.4182808
## 
## $indiv_data$Toledo$seai
## [1] 0.2750333
## 
## $indiv_data$Toledo$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.19683931      0.91581721     -0.07623105      0.58277439 
## 
## $indiv_data$Toledo$blag
## [1] 2
## 
## $indiv_data$Toledo$blead
## [1] 1
## 
## $indiv_data$Toledo$ti
## [1] 52
## 
## $indiv_data$Toledo$tnorm
## [1] 25
## 
## $indiv_data$Toledo$reg_coef
## NULL
## 
## $indiv_data$Toledo$aonesemi
## [1] 1.683327
## 
## 
## $indiv_data$`Trece Martires`
## $indiv_data$`Trece Martires`$gid
## [1] "Trece Martires"
## 
## $indiv_data$`Trece Martires`$ai
## [1] -0.7714349
## 
## $indiv_data$`Trece Martires`$seai
## [1] 0.3420926
## 
## $indiv_data$`Trece Martires`$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.11949957     -0.46877407      0.03209942     -0.20502668 
## 
## $indiv_data$`Trece Martires`$blag
## [1] 2
## 
## $indiv_data$`Trece Martires`$blead
## [1] 1
## 
## $indiv_data$`Trece Martires`$ti
## [1] 52
## 
## $indiv_data$`Trece Martires`$tnorm
## [1] 25
## 
## $indiv_data$`Trece Martires`$reg_coef
## NULL
## 
## $indiv_data$`Trece Martires`$aonesemi
## [1] 1.322114
## 
## 
## $indiv_data$Tuguegarao
## $indiv_data$Tuguegarao$gid
## [1] "Tuguegarao"
## 
## $indiv_data$Tuguegarao$ai
## [1] -0.9166355
## 
## $indiv_data$Tuguegarao$seai
## [1] 0.2688035
## 
## $indiv_data$Tuguegarao$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.06955304      0.20326393      0.02138944     -0.17262286 
## 
## $indiv_data$Tuguegarao$blag
## [1] 2
## 
## $indiv_data$Tuguegarao$blead
## [1] 1
## 
## $indiv_data$Tuguegarao$ti
## [1] 52
## 
## $indiv_data$Tuguegarao$tnorm
## [1] 25
## 
## $indiv_data$Tuguegarao$reg_coef
## NULL
## 
## $indiv_data$Tuguegarao$aonesemi
## [1] 1.120599
## 
## 
## $indiv_data$Urdaneta
## $indiv_data$Urdaneta$gid
## [1] "Urdaneta"
## 
## $indiv_data$Urdaneta$ai
## [1] -0.3312471
## 
## $indiv_data$Urdaneta$seai
## [1] 0.3278737
## 
## $indiv_data$Urdaneta$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.11936513     -0.48266438      0.03199826     -0.23953445 
## 
## $indiv_data$Urdaneta$blag
## [1] 2
## 
## $indiv_data$Urdaneta$blead
## [1] 1
## 
## $indiv_data$Urdaneta$ti
## [1] 52
## 
## $indiv_data$Urdaneta$tnorm
## [1] 25
## 
## $indiv_data$Urdaneta$reg_coef
## NULL
## 
## $indiv_data$Urdaneta$aonesemi
## [1] 1.102176
## 
## 
## $indiv_data$Valencia
## $indiv_data$Valencia$gid
## [1] "Valencia"
## 
## $indiv_data$Valencia$ai
## [1] -1.774976
## 
## $indiv_data$Valencia$seai
## [1] 0.3694475
## 
## $indiv_data$Valencia$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.10759611     -0.59519021      0.03419266     -0.31388337 
## 
## $indiv_data$Valencia$blag
## [1] 2
## 
## $indiv_data$Valencia$blead
## [1] 1
## 
## $indiv_data$Valencia$ti
## [1] 52
## 
## $indiv_data$Valencia$tnorm
## [1] 25
## 
## $indiv_data$Valencia$reg_coef
## NULL
## 
## $indiv_data$Valencia$aonesemi
## [1] 1.589588
## 
## 
## $indiv_data$Valenzuela
## $indiv_data$Valenzuela$gid
## [1] "Valenzuela"
## 
## $indiv_data$Valenzuela$ai
## [1] -1.067478
## 
## $indiv_data$Valenzuela$seai
## [1] 0.3023612
## 
## $indiv_data$Valenzuela$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.11050981     -0.34640064      0.02613311     -0.09855088 
## 
## $indiv_data$Valenzuela$blag
## [1] 2
## 
## $indiv_data$Valenzuela$blead
## [1] 1
## 
## $indiv_data$Valenzuela$ti
## [1] 52
## 
## $indiv_data$Valenzuela$tnorm
## [1] 25
## 
## $indiv_data$Valenzuela$reg_coef
## NULL
## 
## $indiv_data$Valenzuela$aonesemi
## [1] 1.099719
## 
## 
## $indiv_data$Victorias
## $indiv_data$Victorias$gid
## [1] "Victorias"
## 
## $indiv_data$Victorias$ai
## [1] -0.5239067
## 
## $indiv_data$Victorias$seai
## [1] 0.262304
## 
## $indiv_data$Victorias$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.20504968      0.39061295     -0.06248441      0.51231580 
## 
## $indiv_data$Victorias$blag
## [1] 2
## 
## $indiv_data$Victorias$blead
## [1] 1
## 
## $indiv_data$Victorias$ti
## [1] 52
## 
## $indiv_data$Victorias$tnorm
## [1] 25
## 
## $indiv_data$Victorias$reg_coef
## NULL
## 
## $indiv_data$Victorias$aonesemi
## [1] 1.298391
## 
## 
## $indiv_data$Vigan
## $indiv_data$Vigan$gid
## [1] "Vigan"
## 
## $indiv_data$Vigan$ai
## [1] -1.018686
## 
## $indiv_data$Vigan$seai
## [1] 0.3171901
## 
## $indiv_data$Vigan$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.123845292    -0.078277300     0.007253017    -0.062824431 
## 
## $indiv_data$Vigan$blag
## [1] 2
## 
## $indiv_data$Vigan$blead
## [1] 1
## 
## $indiv_data$Vigan$ti
## [1] 52
## 
## $indiv_data$Vigan$tnorm
## [1] 25
## 
## $indiv_data$Vigan$reg_coef
## NULL
## 
## $indiv_data$Vigan$aonesemi
## [1] 1.440825
## 
## 
## $indiv_data$Zamboanga
## $indiv_data$Zamboanga$gid
## [1] "Zamboanga"
## 
## $indiv_data$Zamboanga$ai
## [1] -0.7934473
## 
## $indiv_data$Zamboanga$seai
## [1] 0.2894794
## 
## $indiv_data$Zamboanga$betai
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##      0.02847862      0.62600732      0.02498343     -0.23736063 
## 
## $indiv_data$Zamboanga$blag
## [1] 2
## 
## $indiv_data$Zamboanga$blead
## [1] 1
## 
## $indiv_data$Zamboanga$ti
## [1] 52
## 
## $indiv_data$Zamboanga$tnorm
## [1] 25
## 
## $indiv_data$Zamboanga$reg_coef
## NULL
## 
## $indiv_data$Zamboanga$aonesemi
## [1] 0.871029
## 
## 
## 
## $mean_group
## $mean_group$mg_alpha
## [1] -0.7382819
## 
## $mean_group$se_mg_alpha
## [1] 0.02461345
## 
## $mean_group$mg_betas
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.078472824     0.263274616     0.008161874     0.060574944 
## 
## $mean_group$se_mg_betas
##        avg_temp     ln_avg_wind          avg_rh ln_total_precip 
##     0.005178470     0.057222100     0.002930654     0.029521304 
## 
## 
## $settings
## $settings$meanlag
## [1] 2
## 
## $settings$meanlead
## [1] 1
## 
## $settings$realmeanlag
## [1] 2
## 
## $settings$realmeanlead
## [1] 1
## 
## $settings$auto
## [1] FALSE
## 
## 
## $mg_results
## $mg_results$mg_model
##                 Coef.  Std. Err.         z P>|z| [95% Conf.  Interval]
## ec (alpha) -0.7382819 0.02461345 -29.99506     0 -0.7865243 -0.6900395
## 
## $mg_results$long_run
##                       Coef.   Std. Err.         z        P>|z|  [95% Conf.
## avg_temp        0.078472824 0.005178470 15.153670 0.000000e+00 0.068323023
## ln_avg_wind     0.263274616 0.057222100  4.600925 4.206179e-06 0.151119301
## avg_rh          0.008161874 0.002930654  2.785000 5.352765e-03 0.002417791
## ln_total_precip 0.060574944 0.029521304  2.051906 4.017879e-02 0.002713188
##                  Interval]
## avg_temp        0.08862263
## ln_avg_wind     0.37542993
## avg_rh          0.01390596
## ln_total_precip 0.11843670

Conclusion: - ec(alpha): This confirms that cointegration exist and there is evidence of a long-run relationship. Because it is negative and significant, it means that if AQI gets “pushed” out of balance by a weather spike, it will return to its long-run average.

library(tsibble)
library(feasts)
library(patchwork)

# Convert to tsibble
panel_tsibble <- pdata2 |>
  mutate(week_number = as.numeric(week_number)) |>
  as_tsibble(key = city_name, index = week_number)

# Get list of all cities
target_cities <- unique(panel_tsibble$city_name)

# Loop through each city
for (city in target_cities) {
  
  # Filter data for the city
  city_data <- panel_tsibble |> filter(city_name == city)
  
  # Compute ACF and PACF
  acf_data <- ACF(city_data, Weekly_AQI)
  pacf_data <- PACF(city_data, Weekly_AQI)
  
  # Create plots
  p_acf <- autoplot(acf_data) +
    labs(title = paste("ACF Plot for", city),
         x = "Lag (Weeks)", y = "Autocorrelation") +
    theme_classic() +
    theme(axis.title = element_text(size = 16),
          axis.text = element_text(size = 16))
  
  p_pacf <- autoplot(pacf_data) +
    labs(title = paste("PACF Plot for", city),
         x = "Lag (Weeks)", y = "Partial Autocorrelation") +
    theme_classic() +
    theme(axis.title = element_text(size = 16),
          axis.text = element_text(size = 16)) +
    ylim(-0.30, 0.30)   
  
  # Combine side by side
  final_plot <- p_acf + p_pacf
  print(final_plot)
}

• Visual inspection of the ACF and PACF plots indicate that the autocorrelation structure for the dependent variable is consistent with an autoregressive process of order \(p \in \{1, 2\}\). Most of the cities had significant lag correlation at 1, while some showed correlation until lag 2.

CS-ARDL MODEL

library(csdm)

ylag_range   <- 1:2
xlag_range   <- 0:2
csalag_range <- 0:2

lag_grid_csardl <- expand.grid(
  ylag = ylag_range,
  xlag = xlag_range,
  csalag = csalag_range)

run_csardl <- function(ylag, xlag, csalag, city_data_transformed) {
  csdm(
    formula = ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered,
    data = city_data_transformed,
    id = "city_name",
    time = "week_number",
    model = "cs_ardl",
    csa = csdm_csa(vars = c("ln_Weekly_AQI",
               "temp_centered",
               "ln_wind_centered",
               "rh_centered",
               "ln_precip_centered"), # CSA with variable lag
               lags = csalag),
    lr = csdm_lr(type = "ardl",
      ylags = ylag,
      xdlags = xlag), # Dynamic structure
    vcov = csdm_vcov(type = "nw"))
} 
 

results_list_csardl <- list()

for (i in 1:nrow(lag_grid_csardl)) {
  
  ylag   <- lag_grid_csardl$ylag[i]
  xlag   <- lag_grid_csardl$xlag[i]
  csalag <- lag_grid_csardl$csalag[i]
  
  model_name <- paste0("ARDL(", ylag, ",", xlag, ")_CSA(", csalag, ")")
  
  results_list_csardl[[model_name]] <- list(
    csardl   = run_csardl(ylag, xlag, csalag, city_data_transformed)
  )
}

## cd_test: Dropped 1 incomplete time period (1.9%). Balanced panel: 149 units x 51 periods.
## cd_test: Dropped 1 incomplete time period (1.9%). Balanced panel: 149 units x 51 periods.

## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.

## cd_test: Dropped 1 incomplete time period (1.9%). Balanced panel: 149 units x 51 periods.
## cd_test: Dropped 1 incomplete time period (1.9%). Balanced panel: 149 units x 51 periods.

## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.

## cd_test: Dropped 1 incomplete time period (1.9%). Balanced panel: 149 units x 51 periods.
## cd_test: Dropped 1 incomplete time period (1.9%). Balanced panel: 149 units x 51 periods.

## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.

## cd_test: Dropped 1 incomplete time period (1.9%). Balanced panel: 149 units x 51 periods.
## cd_test: Dropped 1 incomplete time period (1.9%). Balanced panel: 149 units x 51 periods.

## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.
## cd_test: Dropped 2 incomplete time periods (3.8%). Balanced panel: 149 units x 50 periods.

# Iterate through each model in the results_list_csardl
for (name in names(results_list_csardl)) {
  cat("###", name, "\n") #Create a Level 3 header 
  cat("Output for specification:", name, "\n\n") #Add a visual separator 
  print(summary(results_list_csardl[[name]]$csardl)) #Print the summary
  cat("\n\n")#Add extra spacing 
}

ARDL(1,0)_CSA(0)

Output for specification: ARDL(1,0)_CSA(0)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7599 R-squared (mg): 0.6948

CD = 2.8136, p = 0.0049 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% (Intercept) -0.3982 0.1136 -3.5048 0.0005 *** -0.6209 -0.1755 temp_centered -0.0001 0.0117 -0.0049 0.9961 -0.0229 0.0228 ln_wind_centered -0.2153 0.0173 -12.4127 0.0000 *** -0.2493 -0.1813 rh_centered -0.0106 0.0017 -6.2535 0.0000 *** -0.0139 -0.0072 ln_precip_centered 0.0072 0.0049 1.4585 0.1447 -0.0025 0.0169 lag1_ln_Weekly_AQI 0.1240 0.0119 10.4332 0.0000 *** 0.1007 0.1473

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.876 0.0119 -73.6922 0 *** -0.8993 -0.8527

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% lr_temp_centered 0.0029 0.0144 0.1999 0.8415 -0.0253 lr_ln_wind_centered -0.2596 0.0234 -11.1080 0.0000 *** -0.3054 lr_rh_centered -0.0115 0.0021 -5.5977 0.0000 *** -0.0155 lr_ln_precip_centered 0.0036 0.0064 0.5651 0.5720 -0.0089 CI 97.5% n_used lr_temp_centered 0.0311 149 lr_ln_wind_centered -0.2138 149 lr_rh_centered -0.0075 149 lr_ln_precip_centered 0.0161 149

Mean Group Variables: lag1_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=0) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(2,0)_CSA(0)

Output for specification: ARDL(2,0)_CSA(0)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7450 R-squared (mg): 0.6969

CD = 3.9636, p = 1e-04 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% (Intercept) -0.4645 0.1297 -3.5807 0.0003 *** -0.7188 -0.2102 temp_centered 0.0120 0.0123 0.9722 0.3310 -0.0121 0.0361 ln_wind_centered -0.2115 0.0177 -11.9265 0.0000 *** -0.2462 -0.1767 rh_centered -0.0085 0.0018 -4.6383 0.0000 *** -0.0121 -0.0049 ln_precip_centered 0.0044 0.0050 0.8823 0.3776 -0.0054 0.0143 lag1_ln_Weekly_AQI 0.1062 0.0115 9.2375 0.0000 *** 0.0836 0.1287 lag2_ln_Weekly_AQI 0.0309 0.0083 3.7418 0.0002 *** 0.0147 0.0471

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.8629 0.015 -57.4234 0 *** -0.8923 -0.8334

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0127 0.0158 0.8056 0.4205 -0.0182 0.0436 lr_ln_wind_centered -0.2817 0.0296 -9.5229 0.0000 *** -0.3397 -0.2238 lr_rh_centered -0.0098 0.0024 -4.1112 0.0000 *** -0.0145 -0.0051 lr_ln_precip_centered -0.0022 0.0074 -0.3006 0.7637 -0.0167 0.0122 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, lag2_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=0) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(1,1)_CSA(0)

Output for specification: ARDL(1,1)_CSA(0)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7599 R-squared (mg): 0.704

CD = 9.8711, p = 0 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% (Intercept) -0.4895 0.1079 -4.5363 0.0000 *** -0.7010 temp_centered 0.0132 0.0115 1.1517 0.2495 -0.0093 ln_wind_centered -0.2125 0.0178 -11.9495 0.0000 *** -0.2474 rh_centered -0.0084 0.0019 -4.4361 0.0000 *** -0.0121 ln_precip_centered 0.0125 0.0055 2.2892 0.0221 * 0.0018 lag1_ln_Weekly_AQI 0.1513 0.0138 10.9572 0.0000 *** 0.1243 lag1_temp_centered 0.0075 0.0045 1.6802 0.0929 . -0.0012 lag1_ln_wind_centered 0.0568 0.0104 5.4734 0.0000 *** 0.0365 lag1_rh_centered 0.0040 0.0010 3.8305 0.0001 *** 0.0019 lag1_ln_precip_centered -0.0124 0.0038 -3.2526 0.0011 ** -0.0199 CI 97.5% (Intercept) -0.2780 temp_centered 0.0358 ln_wind_centered -0.1776 rh_centered -0.0047 ln_precip_centered 0.0232 lag1_ln_Weekly_AQI 0.1784 lag1_temp_centered 0.0162 lag1_ln_wind_centered 0.0771 lag1_rh_centered 0.0060 lag1_ln_precip_centered -0.0049

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.8487 0.0138 -61.4434 0 *** -0.8757 -0.8216

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0273 0.0149 1.8294 0.0673 . -0.0019 0.0565 lr_ln_wind_centered -0.1933 0.0286 -6.7524 0.0000 *** -0.2494 -0.1372 lr_rh_centered -0.0041 0.0024 -1.7281 0.0840 . -0.0087 0.0005 lr_ln_precip_centered -0.0081 0.0105 -0.7734 0.4393 -0.0288 0.0125 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag1_ln_wind_centered, lag1_rh_centered, lag1_ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=0) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(2,1)_CSA(0)

Output for specification: ARDL(2,1)_CSA(0)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7450 R-squared (mg): 0.7056

CD = 12.3042, p = 0 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% (Intercept) -0.5893 0.1243 -4.7393 0.0000 *** -0.8330 temp_centered 0.0209 0.0124 1.6869 0.0916 . -0.0034 ln_wind_centered -0.2120 0.0182 -11.6429 0.0000 *** -0.2477 rh_centered -0.0071 0.0021 -3.3669 0.0008 *** -0.0113 ln_precip_centered 0.0094 0.0054 1.7371 0.0824 . -0.0012 lag1_ln_Weekly_AQI 0.1366 0.0134 10.2045 0.0000 *** 0.1104 lag2_ln_Weekly_AQI 0.0386 0.0092 4.2009 0.0000 *** 0.0206 lag1_temp_centered 0.0078 0.0046 1.7004 0.0891 . -0.0012 lag1_ln_wind_centered 0.0559 0.0107 5.2156 0.0000 *** 0.0349 lag1_rh_centered 0.0037 0.0011 3.4313 0.0006 *** 0.0016 lag1_ln_precip_centered -0.0115 0.0040 -2.9078 0.0036 ** -0.0192 CI 97.5% (Intercept) -0.3456 temp_centered 0.0453 ln_wind_centered -0.1763 rh_centered -0.0030 ln_precip_centered 0.0201 lag1_ln_Weekly_AQI 0.1629 lag2_ln_Weekly_AQI 0.0565 lag1_temp_centered 0.0168 lag1_ln_wind_centered 0.0769 lag1_rh_centered 0.0059 lag1_ln_precip_centered -0.0037

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.8248 0.0169 -48.9277 0 *** -0.8579 -0.7918

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0407 0.0204 1.9968 0.0458 * 0.0008 0.0806 lr_ln_wind_centered -0.2322 0.0474 -4.8947 0.0000 *** -0.3251 -0.1392 lr_rh_centered 0.0009 0.0052 0.1768 0.8597 -0.0093 0.0111 lr_ln_precip_centered -0.0354 0.0247 -1.4303 0.1526 -0.0838 0.0131 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, lag2_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag1_ln_wind_centered, lag1_rh_centered, lag1_ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=0) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(1,2)_CSA(0)

Output for specification: ARDL(1,2)_CSA(0)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7450 R-squared (mg): 0.7082

CD = 12.5083, p = 0 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% (Intercept) -0.4712 0.1172 -4.0197 0.0001 *** -0.7009 temp_centered 0.0159 0.0128 1.2444 0.2133 -0.0091 ln_wind_centered -0.2132 0.0191 -11.1724 0.0000 *** -0.2506 rh_centered -0.0069 0.0022 -3.0944 0.0020 ** -0.0113 ln_precip_centered 0.0091 0.0061 1.4954 0.1348 -0.0028 lag1_ln_Weekly_AQI 0.1423 0.0138 10.2720 0.0000 *** 0.1151 lag1_temp_centered 0.0054 0.0054 1.0078 0.3135 -0.0051 lag2_temp_centered -0.0068 0.0046 -1.4902 0.1362 -0.0157 lag1_ln_wind_centered 0.0518 0.0112 4.6373 0.0000 *** 0.0299 lag2_ln_wind_centered -0.0623 0.0105 -5.9435 0.0000 *** -0.0828 lag1_rh_centered 0.0039 0.0013 2.9263 0.0034 ** 0.0013 lag2_rh_centered -0.0027 0.0011 -2.4028 0.0163 * -0.0050 lag1_ln_precip_centered -0.0169 0.0051 -3.3244 0.0009 *** -0.0269 lag2_ln_precip_centered -0.0021 0.0041 -0.4980 0.6185 -0.0102 CI 97.5% (Intercept) -0.2414 temp_centered 0.0409 ln_wind_centered -0.1758 rh_centered -0.0025 ln_precip_centered 0.0211 lag1_ln_Weekly_AQI 0.1694 lag1_temp_centered 0.0160 lag2_temp_centered 0.0021 lag1_ln_wind_centered 0.0737 lag2_ln_wind_centered -0.0418 lag1_rh_centered 0.0064 lag2_rh_centered -0.0005 lag1_ln_precip_centered -0.0069 lag2_ln_precip_centered 0.0061

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.8577 0.0138 -61.9375 0 *** -0.8849 -0.8306

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0247 0.0165 1.4931 0.1354 -0.0077 0.0571 lr_ln_wind_centered -0.2764 0.0333 -8.2962 0.0000 *** -0.3418 -0.2111 lr_rh_centered -0.0047 0.0031 -1.4845 0.1377 -0.0108 0.0015 lr_ln_precip_centered -0.0231 0.0147 -1.5718 0.1160 -0.0520 0.0057 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag2_temp_centered, lag1_ln_wind_centered, lag2_ln_wind_centered, lag1_rh_centered, lag2_rh_centered, lag1_ln_precip_centered, lag2_ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=0) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(2,2)_CSA(0)

Output for specification: ARDL(2,2)_CSA(0)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7450 R-squared (mg): 0.7075

CD = 14.766, p = 0 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% (Intercept) -0.4207 0.1402 -3.0008 0.0027 ** -0.6955 temp_centered 0.0144 0.0126 1.1405 0.2541 -0.0103 ln_wind_centered -0.2116 0.0190 -11.1600 0.0000 *** -0.2488 rh_centered -0.0070 0.0022 -3.1823 0.0015 ** -0.0113 ln_precip_centered 0.0096 0.0061 1.5727 0.1158 -0.0024 lag1_ln_Weekly_AQI 0.1342 0.0137 9.7962 0.0000 *** 0.1073 lag2_ln_Weekly_AQI -0.0031 0.0105 -0.2912 0.7709 -0.0237 lag1_temp_centered 0.0049 0.0056 0.8885 0.3742 -0.0060 lag2_temp_centered -0.0030 0.0046 -0.6412 0.5214 -0.0120 lag1_ln_wind_centered 0.0498 0.0117 4.2402 0.0000 *** 0.0268 lag2_ln_wind_centered -0.0643 0.0112 -5.7358 0.0000 *** -0.0862 lag1_rh_centered 0.0031 0.0013 2.3333 0.0196 * 0.0005 lag2_rh_centered -0.0024 0.0011 -2.1022 0.0355 * -0.0046 lag1_ln_precip_centered -0.0136 0.0052 -2.6275 0.0086 ** -0.0237 lag2_ln_precip_centered -0.0007 0.0041 -0.1806 0.8567 -0.0089 CI 97.5% (Intercept) -0.1459 temp_centered 0.0391 ln_wind_centered -0.1745 rh_centered -0.0027 ln_precip_centered 0.0216 lag1_ln_Weekly_AQI 0.1610 lag2_ln_Weekly_AQI 0.0176 lag1_temp_centered 0.0158 lag2_temp_centered 0.0061 lag1_ln_wind_centered 0.0728 lag2_ln_wind_centered -0.0423 lag1_rh_centered 0.0058 lag2_rh_centered -0.0002 lag1_ln_precip_centered -0.0034 lag2_ln_precip_centered 0.0074

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.8689 0.0183 -47.4523 0 *** -0.9048 -0.833

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0186 0.0183 1.0180 0.3087 -0.0172 0.0545 lr_ln_wind_centered -0.3105 0.0585 -5.3046 0.0000 *** -0.4252 -0.1958 lr_rh_centered -0.0062 0.0035 -1.7700 0.0767 . -0.0131 0.0007 lr_ln_precip_centered -0.0192 0.0154 -1.2462 0.2127 -0.0493 0.0110 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, lag2_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag2_temp_centered, lag1_ln_wind_centered, lag2_ln_wind_centered, lag1_rh_centered, lag2_rh_centered, lag1_ln_precip_centered, lag2_ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=0) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(1,0)_CSA(1)

Output for specification: ARDL(1,0)_CSA(1)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7599 R-squared (mg): 0.7123

CD = -0.7054, p = 0.4806 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% (Intercept) -0.0983 0.1423 -0.6907 0.4898 -0.3772 0.1806 temp_centered 0.0411 0.0106 3.8740 0.0001 *** 0.0203 0.0619 ln_wind_centered -0.1566 0.0177 -8.8486 0.0000 *** -0.1913 -0.1219 rh_centered -0.0031 0.0017 -1.8737 0.0610 . -0.0063 0.0001 ln_precip_centered 0.0076 0.0052 1.4744 0.1404 -0.0025 0.0177 lag1_ln_Weekly_AQI 0.1596 0.0144 11.1207 0.0000 *** 0.1315 0.1878

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.8404 0.0144 -58.5408 0 *** -0.8685 -0.8122

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0602 0.0144 4.1887 0.0000 *** 0.0320 0.0884 lr_ln_wind_centered -0.2068 0.0249 -8.3181 0.0000 *** -0.2555 -0.1581 lr_rh_centered -0.0028 0.0022 -1.2841 0.1991 -0.0070 0.0015 lr_ln_precip_centered 0.0075 0.0074 1.0088 0.3131 -0.0071 0.0220 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=1) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(2,0)_CSA(1)

Output for specification: ARDL(2,0)_CSA(1)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7450 R-squared (mg): 0.7159

CD = 3.6403, p = 3e-04 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% (Intercept) -0.2926 0.1638 -1.7863 0.0741 . -0.6136 0.0285 temp_centered 0.0481 0.0108 4.4356 0.0000 *** 0.0268 0.0693 ln_wind_centered -0.1558 0.0179 -8.6907 0.0000 *** -0.1910 -0.1207 rh_centered -0.0016 0.0018 -0.9167 0.3593 -0.0050 0.0018 ln_precip_centered 0.0052 0.0051 1.0208 0.3073 -0.0048 0.0151 lag1_ln_Weekly_AQI 0.1330 0.0143 9.2905 0.0000 *** 0.1049 0.1610 lag2_ln_Weekly_AQI 0.0576 0.0094 6.1431 0.0000 *** 0.0392 0.0759

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.8095 0.0175 -46.3701 0 *** -0.8437 -0.7752

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0717 0.0174 4.1292 0.0000 *** 0.0377 0.1057 lr_ln_wind_centered -0.2396 0.0329 -7.2839 0.0000 *** -0.3041 -0.1752 lr_rh_centered -0.0002 0.0031 -0.0602 0.9520 -0.0062 0.0058 lr_ln_precip_centered -0.0004 0.0097 -0.0435 0.9653 -0.0193 0.0185 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, lag2_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=1) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(1,1)_CSA(1)

Output for specification: ARDL(1,1)_CSA(1)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7599 R-squared (mg): 0.7156

CD = 0.775, p = 0.4384 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% (Intercept) -0.0955 0.1299 -0.7352 0.4622 -0.3501 temp_centered 0.0329 0.0106 3.1050 0.0019 ** 0.0121 ln_wind_centered -0.1739 0.0185 -9.4217 0.0000 *** -0.2100 rh_centered -0.0059 0.0019 -3.0988 0.0019 ** -0.0096 ln_precip_centered 0.0134 0.0053 2.5146 0.0119 * 0.0030 lag1_ln_Weekly_AQI 0.1647 0.0163 10.1314 0.0000 *** 0.1329 lag1_temp_centered -0.0120 0.0116 -1.0329 0.3016 -0.0346 lag1_ln_wind_centered 0.0631 0.0154 4.0991 0.0000 *** 0.0330 lag1_rh_centered 0.0027 0.0016 1.6902 0.0910 . -0.0004 lag1_ln_precip_centered -0.0160 0.0047 -3.3815 0.0007 *** -0.0253 CI 97.5% (Intercept) 0.1591 temp_centered 0.0536 ln_wind_centered -0.1377 rh_centered -0.0022 ln_precip_centered 0.0239 lag1_ln_Weekly_AQI 0.1966 lag1_temp_centered 0.0107 lag1_ln_wind_centered 0.0933 lag1_rh_centered 0.0058 lag1_ln_precip_centered -0.0067

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.8353 0.0163 -51.3715 0 *** -0.8671 -0.8034

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0318 0.0187 1.6971 0.0897 . -0.0049 0.0685 lr_ln_wind_centered -0.1209 0.0356 -3.3946 0.0007 *** -0.1907 -0.0511 lr_rh_centered -0.0015 0.0027 -0.5626 0.5737 -0.0067 0.0037 lr_ln_precip_centered -0.0089 0.0103 -0.8657 0.3866 -0.0290 0.0112 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag1_ln_wind_centered, lag1_rh_centered, lag1_ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=1) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(2,1)_CSA(1)

Output for specification: ARDL(2,1)_CSA(1)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7450 R-squared (mg): 0.719

CD = 4.5649, p = 0 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% (Intercept) -0.2859 0.1528 -1.8712 0.0613 . -0.5853 temp_centered 0.0380 0.0111 3.4224 0.0006 *** 0.0162 ln_wind_centered -0.1695 0.0192 -8.8231 0.0000 *** -0.2072 rh_centered -0.0048 0.0020 -2.4137 0.0158 * -0.0087 ln_precip_centered 0.0105 0.0052 2.0078 0.0447 * 0.0003 lag1_ln_Weekly_AQI 0.1364 0.0168 8.1001 0.0000 *** 0.1034 lag2_ln_Weekly_AQI 0.0550 0.0105 5.2552 0.0000 *** 0.0345 lag1_temp_centered -0.0125 0.0128 -0.9769 0.3286 -0.0376 lag1_ln_wind_centered 0.0482 0.0163 2.9625 0.0031 ** 0.0163 lag1_rh_centered 0.0031 0.0018 1.6708 0.0948 . -0.0005 lag1_ln_precip_centered -0.0180 0.0048 -3.7409 0.0002 *** -0.0274 CI 97.5% (Intercept) 0.0136 temp_centered 0.0597 ln_wind_centered -0.1319 rh_centered -0.0009 ln_precip_centered 0.0208 lag1_ln_Weekly_AQI 0.1694 lag2_ln_Weekly_AQI 0.0755 lag1_temp_centered 0.0126 lag1_ln_wind_centered 0.0801 lag1_rh_centered 0.0066 lag1_ln_precip_centered -0.0086

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.8086 0.0187 -43.3257 0 *** -0.8452 -0.772

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0319 0.0219 1.4553 0.1456 -0.0111 0.0748 lr_ln_wind_centered -0.1495 0.0575 -2.5990 0.0093 ** -0.2623 -0.0368 lr_rh_centered 0.0021 0.0042 0.4917 0.6229 -0.0062 0.0103 lr_ln_precip_centered -0.0274 0.0156 -1.7521 0.0798 . -0.0580 0.0032 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, lag2_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag1_ln_wind_centered, lag1_rh_centered, lag1_ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=1) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(1,2)_CSA(1)

Output for specification: ARDL(1,2)_CSA(1)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7450 R-squared (mg): 0.7214

CD = 5.1178, p = 0 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% (Intercept) -0.1330 0.1459 -0.9112 0.3622 -0.4190 temp_centered 0.0287 0.0135 2.1289 0.0333 * 0.0023 ln_wind_centered -0.1755 0.0203 -8.6310 0.0000 *** -0.2154 rh_centered -0.0060 0.0024 -2.4694 0.0135 * -0.0108 ln_precip_centered 0.0133 0.0062 2.1403 0.0323 * 0.0011 lag1_ln_Weekly_AQI 0.1523 0.0172 8.8667 0.0000 *** 0.1186 lag1_temp_centered -0.0178 0.0135 -1.3199 0.1869 -0.0443 lag2_temp_centered 0.0003 0.0048 0.0563 0.9551 -0.0091 lag1_ln_wind_centered 0.0466 0.0167 2.7929 0.0052 ** 0.0139 lag2_ln_wind_centered -0.0592 0.0122 -4.8515 0.0000 *** -0.0831 lag1_rh_centered 0.0026 0.0020 1.3037 0.1923 -0.0013 lag2_rh_centered -0.0015 0.0013 -1.1217 0.2620 -0.0040 lag1_ln_precip_centered -0.0210 0.0059 -3.5836 0.0003 *** -0.0325 lag2_ln_precip_centered -0.0014 0.0045 -0.3031 0.7618 -0.0102 CI 97.5% (Intercept) 0.1531 temp_centered 0.0551 ln_wind_centered -0.1357 rh_centered -0.0012 ln_precip_centered 0.0255 lag1_ln_Weekly_AQI 0.1860 lag1_temp_centered 0.0086 lag2_temp_centered 0.0096 lag1_ln_wind_centered 0.0793 lag2_ln_wind_centered -0.0353 lag1_rh_centered 0.0064 lag2_rh_centered 0.0011 lag1_ln_precip_centered -0.0095 lag2_ln_precip_centered 0.0074

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.8477 0.0172 -49.3453 0 *** -0.8814 -0.814

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0221 0.0211 1.0469 0.2951 -0.0193 0.0635 lr_ln_wind_centered -0.2319 0.0387 -5.9920 0.0000 *** -0.3077 -0.1560 lr_rh_centered -0.0032 0.0039 -0.8227 0.4107 -0.0108 0.0044 lr_ln_precip_centered -0.0193 0.0151 -1.2831 0.1995 -0.0489 0.0102 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag2_temp_centered, lag1_ln_wind_centered, lag2_ln_wind_centered, lag1_rh_centered, lag2_rh_centered, lag1_ln_precip_centered, lag2_ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=1) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(2,2)_CSA(1)

Output for specification: ARDL(2,2)_CSA(1)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7450 R-squared (mg): 0.7248

CD = 7.0874, p = 0 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% (Intercept) -0.1347 0.1708 -0.7887 0.4303 -0.4695 temp_centered 0.0283 0.0131 2.1548 0.0312 * 0.0026 ln_wind_centered -0.1714 0.0205 -8.3739 0.0000 *** -0.2115 rh_centered -0.0060 0.0024 -2.5173 0.0118 * -0.0106 ln_precip_centered 0.0150 0.0062 2.4059 0.0161 * 0.0028 lag1_ln_Weekly_AQI 0.1366 0.0167 8.1583 0.0000 *** 0.1038 lag2_ln_Weekly_AQI 0.0251 0.0133 1.8927 0.0584 . -0.0009 lag1_temp_centered -0.0097 0.0144 -0.6704 0.5026 -0.0379 lag2_temp_centered 0.0079 0.0049 1.6095 0.1075 -0.0017 lag1_ln_wind_centered 0.0325 0.0179 1.8218 0.0685 . -0.0025 lag2_ln_wind_centered -0.0481 0.0138 -3.4869 0.0005 *** -0.0751 lag1_rh_centered 0.0035 0.0021 1.6579 0.0973 . -0.0006 lag2_rh_centered 0.0004 0.0014 0.3099 0.7566 -0.0023 lag1_ln_precip_centered -0.0197 0.0059 -3.3519 0.0008 *** -0.0312 lag2_ln_precip_centered 0.0005 0.0045 0.1123 0.9105 -0.0084 CI 97.5% (Intercept) 0.2001 temp_centered 0.0541 ln_wind_centered -0.1313 rh_centered -0.0013 ln_precip_centered 0.0272 lag1_ln_Weekly_AQI 0.1694 lag2_ln_Weekly_AQI 0.0512 lag1_temp_centered 0.0186 lag2_temp_centered 0.0176 lag1_ln_wind_centered 0.0676 lag2_ln_wind_centered -0.0211 lag1_rh_centered 0.0076 lag2_rh_centered 0.0031 lag1_ln_precip_centered -0.0082 lag2_ln_precip_centered 0.0094

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.8383 0.0199 -42.1217 0 *** -0.8773 -0.7993

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0360 0.0239 1.5029 0.1329 -0.0109 0.0828 lr_ln_wind_centered -0.2594 0.0609 -4.2598 0.0000 *** -0.3787 -0.1400 lr_rh_centered 0.0001 0.0045 0.0267 0.9787 -0.0087 0.0090 lr_ln_precip_centered -0.0132 0.0154 -0.8590 0.3903 -0.0434 0.0170 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, lag2_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag2_temp_centered, lag1_ln_wind_centered, lag2_ln_wind_centered, lag1_rh_centered, lag2_rh_centered, lag1_ln_precip_centered, lag2_ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=1) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(1,0)_CSA(2)

Output for specification: ARDL(1,0)_CSA(2)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7450 R-squared (mg): 0.7233

CD = -0.5667, p = 0.5709 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% (Intercept) -0.1067 0.2038 -0.5233 0.6007 -0.5061 0.2928 temp_centered 0.0709 0.0101 7.0084 0.0000 *** 0.0511 0.0908 ln_wind_centered -0.1454 0.0189 -7.6798 0.0000 *** -0.1825 -0.1083 rh_centered 0.0019 0.0018 1.0268 0.3045 -0.0017 0.0054 ln_precip_centered 0.0040 0.0058 0.6774 0.4982 -0.0075 0.0154 lag1_ln_Weekly_AQI 0.0890 0.0151 5.9073 0.0000 *** 0.0595 0.1185

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.911 0.0151 -60.4795 0 *** -0.9405 -0.8815

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0833 0.0119 6.9844 0.0000 *** 0.0599 0.1066 lr_ln_wind_centered -0.1755 0.0233 -7.5442 0.0000 *** -0.2211 -0.1299 lr_rh_centered 0.0020 0.0021 0.9640 0.3351 -0.0020 0.0060 lr_ln_precip_centered 0.0037 0.0077 0.4789 0.6320 -0.0114 0.0188 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=2) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(2,0)_CSA(2)

Output for specification: ARDL(2,0)_CSA(2)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7450 R-squared (mg): 0.723

CD = 0.2407, p = 0.8098 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% (Intercept) -0.0888 0.1945 -0.4563 0.6482 -0.4701 0.2925 temp_centered 0.0680 0.0101 6.7441 0.0000 *** 0.0482 0.0877 ln_wind_centered -0.1393 0.0199 -7.0010 0.0000 *** -0.1784 -0.1003 rh_centered 0.0021 0.0018 1.1292 0.2588 -0.0015 0.0056 ln_precip_centered 0.0021 0.0060 0.3474 0.7283 -0.0096 0.0138 lag1_ln_Weekly_AQI 0.0817 0.0142 5.7376 0.0000 *** 0.0538 0.1096 lag2_ln_Weekly_AQI 0.0619 0.0131 4.7252 0.0000 *** 0.0362 0.0876

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.8564 0.0183 -46.7174 0 *** -0.8923 -0.8204

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0861 0.0135 6.3942 0.0000 *** 0.0597 0.1125 lr_ln_wind_centered -0.1869 0.0289 -6.4614 0.0000 *** -0.2436 -0.1302 lr_rh_centered 0.0038 0.0027 1.4348 0.1513 -0.0014 0.0090 lr_ln_precip_centered -0.0049 0.0098 -0.5048 0.6137 -0.0241 0.0142 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, lag2_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=2) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(1,1)_CSA(2)

Output for specification: ARDL(1,1)_CSA(2)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7450 R-squared (mg): 0.729

CD = 0.3333, p = 0.7389 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% (Intercept) -0.0492 0.2006 -0.2451 0.8064 -0.4424 temp_centered 0.0624 0.0118 5.2706 0.0000 *** 0.0392 ln_wind_centered -0.1576 0.0204 -7.7242 0.0000 *** -0.1976 rh_centered -0.0024 0.0022 -1.1263 0.2600 -0.0066 ln_precip_centered 0.0154 0.0061 2.5361 0.0112 * 0.0035 lag1_ln_Weekly_AQI 0.0856 0.0171 5.0027 0.0000 *** 0.0520 lag1_temp_centered 0.0212 0.0130 1.6314 0.1028 -0.0043 lag1_ln_wind_centered 0.0589 0.0179 3.2890 0.0010 ** 0.0238 lag1_rh_centered 0.0097 0.0020 4.7937 0.0000 *** 0.0057 lag1_ln_precip_centered -0.0171 0.0054 -3.1552 0.0016 ** -0.0277 CI 97.5% (Intercept) 0.3441 temp_centered 0.0855 ln_wind_centered -0.1176 rh_centered 0.0018 ln_precip_centered 0.0273 lag1_ln_Weekly_AQI 0.1191 lag1_temp_centered 0.0466 lag1_ln_wind_centered 0.0939 lag1_rh_centered 0.0136 lag1_ln_precip_centered -0.0065

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.9144 0.0171 -53.4612 0 *** -0.948 -0.8809

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0990 0.0186 5.3299 0.0000 *** 0.0626 0.1354 lr_ln_wind_centered -0.1065 0.0337 -3.1599 0.0016 ** -0.1725 -0.0404 lr_rh_centered 0.0097 0.0030 3.1972 0.0014 ** 0.0037 0.0156 lr_ln_precip_centered -0.0044 0.0095 -0.4614 0.6445 -0.0229 0.0142 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag1_ln_wind_centered, lag1_rh_centered, lag1_ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=2) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(2,1)_CSA(2)

Output for specification: ARDL(2,1)_CSA(2)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7450 R-squared (mg): 0.7276

CD = 0.9939, p = 0.3203 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% (Intercept) -0.0294 0.1925 -0.1529 0.8785 -0.4067 temp_centered 0.0593 0.0118 5.0066 0.0000 *** 0.0361 ln_wind_centered -0.1503 0.0217 -6.9255 0.0000 *** -0.1929 rh_centered -0.0025 0.0022 -1.1705 0.2418 -0.0067 ln_precip_centered 0.0138 0.0062 2.2279 0.0259 * 0.0017 lag1_ln_Weekly_AQI 0.0787 0.0163 4.8253 0.0000 *** 0.0467 lag2_ln_Weekly_AQI 0.0565 0.0135 4.1974 0.0000 *** 0.0301 lag1_temp_centered 0.0237 0.0133 1.7899 0.0735 . -0.0023 lag1_ln_wind_centered 0.0473 0.0177 2.6719 0.0075 ** 0.0126 lag1_rh_centered 0.0099 0.0021 4.8076 0.0000 *** 0.0059 lag1_ln_precip_centered -0.0162 0.0057 -2.8358 0.0046 ** -0.0274 CI 97.5% (Intercept) 0.3479 temp_centered 0.0825 ln_wind_centered -0.1078 rh_centered 0.0017 ln_precip_centered 0.0259 lag1_ln_Weekly_AQI 0.1106 lag2_ln_Weekly_AQI 0.0829 lag1_temp_centered 0.0497 lag1_ln_wind_centered 0.0820 lag1_rh_centered 0.0139 lag1_ln_precip_centered -0.0050

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.8648 0.0203 -42.6334 0 *** -0.9045 -0.825

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0978 0.0197 4.9579 0.0000 *** 0.0591 0.1365 lr_ln_wind_centered -0.1232 0.0465 -2.6483 0.0081 ** -0.2144 -0.0320 lr_rh_centered 0.0107 0.0034 3.1143 0.0018 ** 0.0040 0.0174 lr_ln_precip_centered -0.0112 0.0111 -1.0092 0.3129 -0.0330 0.0106 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, lag2_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag1_ln_wind_centered, lag1_rh_centered, lag1_ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=2) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(1,2)_CSA(2)

Output for specification: ARDL(1,2)_CSA(2)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7450 R-squared (mg): 0.7305

CD = 2.5438, p = 0.011 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% (Intercept) -0.0538 0.2198 -0.2447 0.8067 -0.4846 temp_centered 0.0438 0.0149 2.9428 0.0033 ** 0.0146 ln_wind_centered -0.1569 0.0216 -7.2580 0.0000 *** -0.1993 rh_centered -0.0027 0.0026 -1.0429 0.2970 -0.0078 ln_precip_centered 0.0102 0.0072 1.4235 0.1546 -0.0038 lag1_ln_Weekly_AQI 0.0990 0.0188 5.2772 0.0000 *** 0.0622 lag1_temp_centered 0.0275 0.0152 1.8102 0.0703 . -0.0023 lag2_temp_centered -0.0042 0.0134 -0.3129 0.7543 -0.0305 lag1_ln_wind_centered 0.0616 0.0206 2.9940 0.0028 ** 0.0213 lag2_ln_wind_centered -0.1192 0.0166 -7.1630 0.0000 *** -0.1518 lag1_rh_centered 0.0087 0.0024 3.6416 0.0003 *** 0.0040 lag2_rh_centered 0.0023 0.0024 0.9574 0.3384 -0.0024 lag1_ln_precip_centered -0.0183 0.0060 -3.0232 0.0025 ** -0.0301 lag2_ln_precip_centered -0.0004 0.0050 -0.0728 0.9420 -0.0101 CI 97.5% (Intercept) 0.3770 temp_centered 0.0729 ln_wind_centered -0.1146 rh_centered 0.0024 ln_precip_centered 0.0243 lag1_ln_Weekly_AQI 0.1357 lag1_temp_centered 0.0573 lag2_temp_centered 0.0221 lag1_ln_wind_centered 0.1020 lag2_ln_wind_centered -0.0866 lag1_rh_centered 0.0134 lag2_rh_centered 0.0071 lag1_ln_precip_centered -0.0064 lag2_ln_precip_centered 0.0094

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.901 0.0188 -48.0439 0 *** -0.9378 -0.8643

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0834 0.0257 3.2409 0.0012 ** 0.0330 0.1339 lr_ln_wind_centered -0.2569 0.0443 -5.8009 0.0000 *** -0.3436 -0.1701 lr_rh_centered 0.0120 0.0047 2.5234 0.0116 * 0.0027 0.0213 lr_ln_precip_centered -0.0093 0.0142 -0.6496 0.5160 -0.0372 0.0187 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag2_temp_centered, lag1_ln_wind_centered, lag2_ln_wind_centered, lag1_rh_centered, lag2_rh_centered, lag1_ln_precip_centered, lag2_ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=2) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

ARDL(2,2)_CSA(2)

Output for specification: ARDL(2,2)_CSA(2)

csdm summary: Cross-Sectional ARDL (CS-ARDL) Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered + ln_precip_centered N: 149, T: 52 Number of obs: 7450 R-squared (mg): 0.7277

CD = 2.4382, p = 0.0148 (For additional CD diagnostics, use cd_test())

Short Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% (Intercept) 0.0096 0.2176 0.0439 0.9650 -0.4170 temp_centered 0.0457 0.0149 3.0652 0.0022 ** 0.0165 ln_wind_centered -0.1490 0.0229 -6.5194 0.0000 *** -0.1938 rh_centered -0.0021 0.0026 -0.8216 0.4113 -0.0073 ln_precip_centered 0.0098 0.0074 1.3228 0.1859 -0.0047 lag1_ln_Weekly_AQI 0.0939 0.0183 5.1397 0.0000 *** 0.0581 lag2_ln_Weekly_AQI 0.0149 0.0152 0.9833 0.3255 -0.0148 lag1_temp_centered 0.0269 0.0152 1.7652 0.0775 . -0.0030 lag2_temp_centered 0.0020 0.0141 0.1437 0.8857 -0.0256 lag1_ln_wind_centered 0.0524 0.0211 2.4900 0.0128 * 0.0112 lag2_ln_wind_centered -0.1109 0.0176 -6.2895 0.0000 *** -0.1454 lag1_rh_centered 0.0084 0.0025 3.3771 0.0007 *** 0.0035 lag2_rh_centered 0.0030 0.0025 1.2332 0.2175 -0.0018 lag1_ln_precip_centered -0.0173 0.0064 -2.7026 0.0069 ** -0.0299 lag2_ln_precip_centered 0.0020 0.0051 0.3840 0.7010 -0.0081 CI 97.5% (Intercept) 0.4361 temp_centered 0.0750 ln_wind_centered -0.1042 rh_centered 0.0030 ln_precip_centered 0.0243 lag1_ln_Weekly_AQI 0.1298 lag2_ln_Weekly_AQI 0.0447 lag1_temp_centered 0.0568 lag2_temp_centered 0.0297 lag1_ln_wind_centered 0.0937 lag2_ln_wind_centered -0.0763 lag1_rh_centered 0.0133 lag2_rh_centered 0.0079 lag1_ln_precip_centered -0.0048 lag2_ln_precip_centered 0.0120

Adjust. Term Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_ln_Weekly_AQI -0.8911 0.0232 -38.4724 0 *** -0.9365 -0.8457

Long Run Est. Coef. Std. Err. z P>|z| Signif. CI 2.5% CI 97.5% lr_temp_centered 0.0883 0.0266 3.3195 0.0009 *** 0.0362 0.1405 lr_ln_wind_centered -0.2266 0.0808 -2.8049 0.0050 ** -0.3849 -0.0683 lr_rh_centered 0.0158 0.0056 2.8323 0.0046 ** 0.0049 0.0267 lr_ln_precip_centered -0.0154 0.0162 -0.9524 0.3409 -0.0471 0.0163 n_used lr_temp_centered 149 lr_ln_wind_centered 149 lr_rh_centered 149 lr_ln_precip_centered 149

Mean Group Variables: lag1_ln_Weekly_AQI, lag2_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag2_temp_centered, lag1_ln_wind_centered, lag2_ln_wind_centered, lag1_rh_centered, lag2_rh_centered, lag1_ln_precip_centered, lag2_ln_precip_centered Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=2) Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered Cointegration variable(s): ln_Weekly_AQI

Signif. codes: 0 ‘’ 0.001 ’’ 0.01 ’’ 0.05 ‘.’ 0.1 ’ ’ 1

Best Model Selection:

# Initialize the summary data frame
ic_summary <- data.frame()

#Loop through the top_list
for (m_name in names(results_list_csardl)) {
  fit <- results_list_csardl[[m_name]]$csardl
  if (!is.null(fit)) {
    
    # Extract residuals and handle any NAs
    res <- residuals(fit)
    sse <- sum(res^2, na.rm = TRUE) 
    n   <- sum(!is.na(res)) # Actual number of observations
    k   <- length(coef(fit)) # Number of parameters 
    fitted_val <- fitted(fit)
    actual <- fitted_val + res
    
    # Manual Calculation of Information Criteria using SSE based formula
    aic_val <- n * log(sse / n) + 2 * k
    bic_val <- n * log(sse / n) + k * log(n) #BIC
    rmse_val <- sqrt(mean(res^2, na.rm = TRUE))
    mape_val <- mean(abs(res / actual), na.rm = TRUE) * 100
    
    
    
    # Append results to the data frame
    ic_summary <- rbind(ic_summary, data.frame(
      Model = m_name,
      AIC   = round(aic_val, 2),
      BIC   = round(bic_val, 2),
      RMSE = rmse_val,
      MAPE = mape_val,
      Obs   = n,
      K     = k
    ))
  }
}

# Sort by SIC 
ic_summary <- ic_summary[order(ic_summary$AIC), ]

#Print the final table
print(ic_summary)

##               Model       AIC       BIC       RMSE     MAPE  Obs  K
## 18 ARDL(2,2)_CSA(2) -36568.66 -36430.34 0.08569531 1.898897 7450 20
## 17 ARDL(1,2)_CSA(2) -36283.67 -36152.26 0.08736191 1.934820 7450 19
## 16 ARDL(2,1)_CSA(2) -35214.77 -35104.11 0.09389717 2.083626 7450 16
## 15 ARDL(1,1)_CSA(2) -34953.00 -34849.26 0.09557421 2.120898 7450 15
## 12 ARDL(2,2)_CSA(1) -34828.29 -34689.97 0.09631286 2.135410 7450 20
## 11 ARDL(1,2)_CSA(1) -34446.28 -34314.88 0.09882730 2.186313 7450 19
## 14 ARDL(2,0)_CSA(2) -33951.80 -33868.81 0.10225814 2.267927 7450 12
## 9  ARDL(1,1)_CSA(1) -33871.66 -33767.62 0.10745688 2.388230 7599 15
## 13 ARDL(1,0)_CSA(2) -33699.87 -33623.80 0.10401576 2.305520 7450 11
## 10 ARDL(2,1)_CSA(1) -33574.37 -33463.72 0.10482518 2.328749 7450 16
## 6  ARDL(2,2)_CSA(0) -33015.62 -32877.30 0.10877238 2.420412 7450 20
## 7  ARDL(1,0)_CSA(1) -32870.12 -32793.83 0.11483718 2.550915 7599 11
## 5  ARDL(1,2)_CSA(0) -32791.56 -32660.16 0.11043521 2.456495 7450 19
## 8  ARDL(2,0)_CSA(1) -32537.42 -32454.43 0.11244061 2.487674 7450 12
## 3  ARDL(1,1)_CSA(0) -32430.91 -32326.88 0.11814206 2.632802 7599 15
## 4  ARDL(2,1)_CSA(0) -32041.59 -31930.94 0.11618285 2.580940 7450 16
## 1  ARDL(1,0)_CSA(0) -31405.35 -31329.06 0.12645599 2.799976 7599 11
## 2  ARDL(2,0)_CSA(0) -31003.34 -30920.35 0.12463425 2.752084 7450 12

BASED FROM THE BIC, RMSE, MAPE

top_list <- results_list_csardl[c("ARDL(2,2)_CSA(2)","ARDL(1,2)_CSA(2)","ARDL(2,1)_CSA(2)")]

# Loop through the renamed list 
for (m_name in names(top_list)) {
  # Print a separator for readability
  cat("\n", rep("=", 60), "\n")
  cat("   FULL SUMMARY FOR:", m_name, "\n")
  cat(rep("=", 60), "\n")
  # Access the model object and print summary
  print(summary(top_list[[m_name]]$csardl))
  
  cat("\n") 
}

## 
##  = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 
##    FULL SUMMARY FOR: ARDL(2,2)_CSA(2) 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 
## csdm summary: Cross-Sectional ARDL (CS-ARDL)
## Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered +     ln_precip_centered
## N: 149, T: 52
## Number of obs: 7450
## R-squared (mg): 0.7277
## 
## CD = 2.4382, p = 0.0148
## (For additional CD diagnostics, use cd_test())
## 
## Short Run Est.
##                           Coef. Std. Err.       z  P>|z| Signif. CI 2.5%
## (Intercept)              0.0096    0.2176  0.0439 0.9650         -0.4170
## temp_centered            0.0457    0.0149  3.0652 0.0022      **  0.0165
## ln_wind_centered        -0.1490    0.0229 -6.5194 0.0000     *** -0.1938
## rh_centered             -0.0021    0.0026 -0.8216 0.4113         -0.0073
## ln_precip_centered       0.0098    0.0074  1.3228 0.1859         -0.0047
## lag1_ln_Weekly_AQI       0.0939    0.0183  5.1397 0.0000     ***  0.0581
## lag2_ln_Weekly_AQI       0.0149    0.0152  0.9833 0.3255         -0.0148
## lag1_temp_centered       0.0269    0.0152  1.7652 0.0775       . -0.0030
## lag2_temp_centered       0.0020    0.0141  0.1437 0.8857         -0.0256
## lag1_ln_wind_centered    0.0524    0.0211  2.4900 0.0128       *  0.0112
## lag2_ln_wind_centered   -0.1109    0.0176 -6.2895 0.0000     *** -0.1454
## lag1_rh_centered         0.0084    0.0025  3.3771 0.0007     ***  0.0035
## lag2_rh_centered         0.0030    0.0025  1.2332 0.2175         -0.0018
## lag1_ln_precip_centered -0.0173    0.0064 -2.7026 0.0069      ** -0.0299
## lag2_ln_precip_centered  0.0020    0.0051  0.3840 0.7010         -0.0081
##                         CI 97.5%
## (Intercept)               0.4361
## temp_centered             0.0750
## ln_wind_centered         -0.1042
## rh_centered               0.0030
## ln_precip_centered        0.0243
## lag1_ln_Weekly_AQI        0.1298
## lag2_ln_Weekly_AQI        0.0447
## lag1_temp_centered        0.0568
## lag2_temp_centered        0.0297
## lag1_ln_wind_centered     0.0937
## lag2_ln_wind_centered    -0.0763
## lag1_rh_centered          0.0133
## lag2_rh_centered          0.0079
## lag1_ln_precip_centered  -0.0048
## lag2_ln_precip_centered   0.0120
## 
## Adjust. Term
##                    Coef. Std. Err.        z P>|z| Signif. CI 2.5% CI 97.5%
## lr_ln_Weekly_AQI -0.8911    0.0232 -38.4724     0     *** -0.9365  -0.8457
## 
## Long Run Est.
##                         Coef. Std. Err.       z  P>|z| Signif. CI 2.5% CI 97.5%
## lr_temp_centered       0.0883    0.0266  3.3195 0.0009     ***  0.0362   0.1405
## lr_ln_wind_centered   -0.2266    0.0808 -2.8049 0.0050      ** -0.3849  -0.0683
## lr_rh_centered         0.0158    0.0056  2.8323 0.0046      **  0.0049   0.0267
## lr_ln_precip_centered -0.0154    0.0162 -0.9524 0.3409         -0.0471   0.0163
##                       n_used
## lr_temp_centered         149
## lr_ln_wind_centered      149
## lr_rh_centered           149
## lr_ln_precip_centered    149
## 
## Mean Group Variables: lag1_ln_Weekly_AQI, lag2_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag2_temp_centered, lag1_ln_wind_centered, lag2_ln_wind_centered, lag1_rh_centered, lag2_rh_centered, lag1_ln_precip_centered, lag2_ln_precip_centered
## Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=2)
## Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered
## Cointegration variable(s): ln_Weekly_AQI
## 
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
##  = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 
##    FULL SUMMARY FOR: ARDL(1,2)_CSA(2) 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 
## csdm summary: Cross-Sectional ARDL (CS-ARDL)
## Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered +     ln_precip_centered
## N: 149, T: 52
## Number of obs: 7450
## R-squared (mg): 0.7305
## 
## CD = 2.5438, p = 0.011
## (For additional CD diagnostics, use cd_test())
## 
## Short Run Est.
##                           Coef. Std. Err.       z  P>|z| Signif. CI 2.5%
## (Intercept)             -0.0538    0.2198 -0.2447 0.8067         -0.4846
## temp_centered            0.0438    0.0149  2.9428 0.0033      **  0.0146
## ln_wind_centered        -0.1569    0.0216 -7.2580 0.0000     *** -0.1993
## rh_centered             -0.0027    0.0026 -1.0429 0.2970         -0.0078
## ln_precip_centered       0.0102    0.0072  1.4235 0.1546         -0.0038
## lag1_ln_Weekly_AQI       0.0990    0.0188  5.2772 0.0000     ***  0.0622
## lag1_temp_centered       0.0275    0.0152  1.8102 0.0703       . -0.0023
## lag2_temp_centered      -0.0042    0.0134 -0.3129 0.7543         -0.0305
## lag1_ln_wind_centered    0.0616    0.0206  2.9940 0.0028      **  0.0213
## lag2_ln_wind_centered   -0.1192    0.0166 -7.1630 0.0000     *** -0.1518
## lag1_rh_centered         0.0087    0.0024  3.6416 0.0003     ***  0.0040
## lag2_rh_centered         0.0023    0.0024  0.9574 0.3384         -0.0024
## lag1_ln_precip_centered -0.0183    0.0060 -3.0232 0.0025      ** -0.0301
## lag2_ln_precip_centered -0.0004    0.0050 -0.0728 0.9420         -0.0101
##                         CI 97.5%
## (Intercept)               0.3770
## temp_centered             0.0729
## ln_wind_centered         -0.1146
## rh_centered               0.0024
## ln_precip_centered        0.0243
## lag1_ln_Weekly_AQI        0.1357
## lag1_temp_centered        0.0573
## lag2_temp_centered        0.0221
## lag1_ln_wind_centered     0.1020
## lag2_ln_wind_centered    -0.0866
## lag1_rh_centered          0.0134
## lag2_rh_centered          0.0071
## lag1_ln_precip_centered  -0.0064
## lag2_ln_precip_centered   0.0094
## 
## Adjust. Term
##                   Coef. Std. Err.        z P>|z| Signif. CI 2.5% CI 97.5%
## lr_ln_Weekly_AQI -0.901    0.0188 -48.0439     0     *** -0.9378  -0.8643
## 
## Long Run Est.
##                         Coef. Std. Err.       z  P>|z| Signif. CI 2.5% CI 97.5%
## lr_temp_centered       0.0834    0.0257  3.2409 0.0012      **  0.0330   0.1339
## lr_ln_wind_centered   -0.2569    0.0443 -5.8009 0.0000     *** -0.3436  -0.1701
## lr_rh_centered         0.0120    0.0047  2.5234 0.0116       *  0.0027   0.0213
## lr_ln_precip_centered -0.0093    0.0142 -0.6496 0.5160         -0.0372   0.0187
##                       n_used
## lr_temp_centered         149
## lr_ln_wind_centered      149
## lr_rh_centered           149
## lr_ln_precip_centered    149
## 
## Mean Group Variables: lag1_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag2_temp_centered, lag1_ln_wind_centered, lag2_ln_wind_centered, lag1_rh_centered, lag2_rh_centered, lag1_ln_precip_centered, lag2_ln_precip_centered
## Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=2)
## Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered
## Cointegration variable(s): ln_Weekly_AQI
## 
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## 
##  = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 
##    FULL SUMMARY FOR: ARDL(2,1)_CSA(2) 
## = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = 
## csdm summary: Cross-Sectional ARDL (CS-ARDL)
## Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered +     ln_precip_centered
## N: 149, T: 52
## Number of obs: 7450
## R-squared (mg): 0.7276
## 
## CD = 0.9939, p = 0.3203
## (For additional CD diagnostics, use cd_test())
## 
## Short Run Est.
##                           Coef. Std. Err.       z  P>|z| Signif. CI 2.5%
## (Intercept)             -0.0294    0.1925 -0.1529 0.8785         -0.4067
## temp_centered            0.0593    0.0118  5.0066 0.0000     ***  0.0361
## ln_wind_centered        -0.1503    0.0217 -6.9255 0.0000     *** -0.1929
## rh_centered             -0.0025    0.0022 -1.1705 0.2418         -0.0067
## ln_precip_centered       0.0138    0.0062  2.2279 0.0259       *  0.0017
## lag1_ln_Weekly_AQI       0.0787    0.0163  4.8253 0.0000     ***  0.0467
## lag2_ln_Weekly_AQI       0.0565    0.0135  4.1974 0.0000     ***  0.0301
## lag1_temp_centered       0.0237    0.0133  1.7899 0.0735       . -0.0023
## lag1_ln_wind_centered    0.0473    0.0177  2.6719 0.0075      **  0.0126
## lag1_rh_centered         0.0099    0.0021  4.8076 0.0000     ***  0.0059
## lag1_ln_precip_centered -0.0162    0.0057 -2.8358 0.0046      ** -0.0274
##                         CI 97.5%
## (Intercept)               0.3479
## temp_centered             0.0825
## ln_wind_centered         -0.1078
## rh_centered               0.0017
## ln_precip_centered        0.0259
## lag1_ln_Weekly_AQI        0.1106
## lag2_ln_Weekly_AQI        0.0829
## lag1_temp_centered        0.0497
## lag1_ln_wind_centered     0.0820
## lag1_rh_centered          0.0139
## lag1_ln_precip_centered  -0.0050
## 
## Adjust. Term
##                    Coef. Std. Err.        z P>|z| Signif. CI 2.5% CI 97.5%
## lr_ln_Weekly_AQI -0.8648    0.0203 -42.6334     0     *** -0.9045   -0.825
## 
## Long Run Est.
##                         Coef. Std. Err.       z  P>|z| Signif. CI 2.5% CI 97.5%
## lr_temp_centered       0.0978    0.0197  4.9579 0.0000     ***  0.0591   0.1365
## lr_ln_wind_centered   -0.1232    0.0465 -2.6483 0.0081      ** -0.2144  -0.0320
## lr_rh_centered         0.0107    0.0034  3.1143 0.0018      **  0.0040   0.0174
## lr_ln_precip_centered -0.0112    0.0111 -1.0092 0.3129         -0.0330   0.0106
##                       n_used
## lr_temp_centered         149
## lr_ln_wind_centered      149
## lr_rh_centered           149
## lr_ln_precip_centered    149
## 
## Mean Group Variables: lag1_ln_Weekly_AQI, lag2_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag1_ln_wind_centered, lag1_rh_centered, lag1_ln_precip_centered
## Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=2)
## Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered
## Cointegration variable(s): ln_Weekly_AQI
## 
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Discussion:

• CD TEST: CS-ARDL 212 reveals that there is no cross-sectional dependence in the residuals, hence these models fully captured the spatial correlation in the variables.

• ECT: The ECT are negative, statistically significant, and between 0 and -1. This indicates a stable and dynamically well-specified model. This confirms that the variables have a long-run relationship and not the effect is not spurious. This suggests that the systemic patterns have been effectively moved from the “noise” (error term) into the “signal” (model coefficients), by avoiding dynamic misspecification issue which is a cause of serial correlation.

M212 <- results_list_csardl[["ARDL(2,1)_CSA(2)"]]$csardl
summary(M212)

## csdm summary: Cross-Sectional ARDL (CS-ARDL)
## Formula: ln_Weekly_AQI ~ temp_centered + ln_wind_centered + rh_centered +     ln_precip_centered
## N: 149, T: 52
## Number of obs: 7450
## R-squared (mg): 0.7276
## 
## CD = 0.9939, p = 0.3203
## (For additional CD diagnostics, use cd_test())
## 
## Short Run Est.
##                           Coef. Std. Err.       z  P>|z| Signif. CI 2.5%
## (Intercept)             -0.0294    0.1925 -0.1529 0.8785         -0.4067
## temp_centered            0.0593    0.0118  5.0066 0.0000     ***  0.0361
## ln_wind_centered        -0.1503    0.0217 -6.9255 0.0000     *** -0.1929
## rh_centered             -0.0025    0.0022 -1.1705 0.2418         -0.0067
## ln_precip_centered       0.0138    0.0062  2.2279 0.0259       *  0.0017
## lag1_ln_Weekly_AQI       0.0787    0.0163  4.8253 0.0000     ***  0.0467
## lag2_ln_Weekly_AQI       0.0565    0.0135  4.1974 0.0000     ***  0.0301
## lag1_temp_centered       0.0237    0.0133  1.7899 0.0735       . -0.0023
## lag1_ln_wind_centered    0.0473    0.0177  2.6719 0.0075      **  0.0126
## lag1_rh_centered         0.0099    0.0021  4.8076 0.0000     ***  0.0059
## lag1_ln_precip_centered -0.0162    0.0057 -2.8358 0.0046      ** -0.0274
##                         CI 97.5%
## (Intercept)               0.3479
## temp_centered             0.0825
## ln_wind_centered         -0.1078
## rh_centered               0.0017
## ln_precip_centered        0.0259
## lag1_ln_Weekly_AQI        0.1106
## lag2_ln_Weekly_AQI        0.0829
## lag1_temp_centered        0.0497
## lag1_ln_wind_centered     0.0820
## lag1_rh_centered          0.0139
## lag1_ln_precip_centered  -0.0050
## 
## Adjust. Term
##                    Coef. Std. Err.        z P>|z| Signif. CI 2.5% CI 97.5%
## lr_ln_Weekly_AQI -0.8648    0.0203 -42.6334     0     *** -0.9045   -0.825
## 
## Long Run Est.
##                         Coef. Std. Err.       z  P>|z| Signif. CI 2.5% CI 97.5%
## lr_temp_centered       0.0978    0.0197  4.9579 0.0000     ***  0.0591   0.1365
## lr_ln_wind_centered   -0.1232    0.0465 -2.6483 0.0081      ** -0.2144  -0.0320
## lr_rh_centered         0.0107    0.0034  3.1143 0.0018      **  0.0040   0.0174
## lr_ln_precip_centered -0.0112    0.0111 -1.0092 0.3129         -0.0330   0.0106
##                       n_used
## lr_temp_centered         149
## lr_ln_wind_centered      149
## lr_rh_centered           149
## lr_ln_precip_centered    149
## 
## Mean Group Variables: lag1_ln_Weekly_AQI, lag2_ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered, lag1_temp_centered, lag1_ln_wind_centered, lag1_rh_centered, lag1_ln_precip_centered
## Cross Sectional Averaged Variables: ln_Weekly_AQI, temp_centered, ln_wind_centered, rh_centered, ln_precip_centered (lags=2)
## Long Run Variables: temp_centered, ln_wind_centered, rh_centered, ln_precip_centered
## Cointegration variable(s): ln_Weekly_AQI
## 
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

City-Level Diagnostic

library(dplyr)
library(tidyr)

# Convert the wide residual matrix (149x52) into a long vector (7748)
long_resids <- as.vector(t(M212$residuals_e))

#Add THE residuals back to the main dataframe for analysis
city_data_transformed$resids <- long_resids

box_city_results <- city_data_transformed |> 
  group_by(city_name) |> 
  filter(!is.na(resids)) |> 
  summarise(
    lb_Statistic = Box.test(resids, lag = 24,type = "Ljung-Box")$statistic,
    P_Value = Box.test(resids)$p.value
  )


print(box_city_results)

## # A tibble: 149 × 3
##    city_name    lb_Statistic P_Value
##    <chr>               <dbl>   <dbl>
##  1 Alaminos             19.3   0.508
##  2 Angeles City         14.9   0.613
##  3 Antipolo             24.6   0.572
##  4 Bacolod City         20.4   0.371
##  5 Bacoor               29.7   0.994
##  6 Bago City            14.3   0.820
##  7 Baguio               16.6   0.283
##  8 Bais                 17.4   0.533
##  9 Balanga              13.1   0.706
## 10 Baliwag              36.6   0.343
## # ℹ 139 more rows

passed_count <- sum(box_city_results$P_Value < 0.05)
total_cities <- nrow(box_city_results)

cat("Summary of Residual Serial Correlation:\n")

## Summary of Residual Serial Correlation:

cat(passed_count, "out of", total_cities, "cities have serially-correlated residuals (p < 0.05).\n")

## 0 out of 149 cities have serially-correlated residuals (p < 0.05).

library(lmtest)
library(tidyverse)


actual_obs <- !is.na(M212$residuals_e[,1]) 

residual_data <- data.frame(
  city = rep(rownames(M212$residuals_e), each = ncol(M212$residuals_e)),
  res_e = as.vector(t(M212$residuals_e)),
  fitted = as.vector(t(M212$fitted_xb))) |> 
  filter(!is.na(res_e)) 

#run the loop on the data
res_tests <- residual_data |> 
  group_by(city) |> 
  summarize(BP_statistic = if(n() > 10) bptest(lm(res_e ~ fitted, data = cur_data()))$statistic else NA,
    
    BP_p_value = if(n() > 10) bptest(lm(res_e ~ fitted, data = cur_data()))$p.value else NA,
    Status = case_when(
      is.na(BP_p_value) ~ "Insufficient Data",
      BP_p_value < 0.05 ~ "Heteroskedastic",
      TRUE ~ "Homoskedastic"))

print(res_tests)

## # A tibble: 149 × 4
##    city         BP_statistic BP_p_value Status       
##    <chr>               <dbl>      <dbl> <chr>        
##  1 Alaminos           0.602      0.438  Homoskedastic
##  2 Angeles City       0.774      0.379  Homoskedastic
##  3 Antipolo           0.789      0.374  Homoskedastic
##  4 Bacolod City       1.10       0.294  Homoskedastic
##  5 Bacoor             0.0629     0.802  Homoskedastic
##  6 Bago City          1.71       0.191  Homoskedastic
##  7 Baguio             0.0462     0.830  Homoskedastic
##  8 Bais               2.78       0.0956 Homoskedastic
##  9 Balanga            0.421      0.516  Homoskedastic
## 10 Baliwag            0.211      0.646  Homoskedastic
## # ℹ 139 more rows

# Summarize the results and calculate percentages
homoskedastic_summary <- res_tests |> 
  count(Status) |> 
  mutate(Percentage = (n / sum(n)) * 100)

# print the Homoskedastic row
print(homoskedastic_summary |>  filter(Status == "Homoskedastic"))

## # A tibble: 1 × 3
##   Status            n Percentage
##   <chr>         <int>      <dbl>
## 1 Homoskedastic   144       96.6

# Run Shapiro.test test for each city
normality_results <- residual_data |> 
  group_by(city) |> 
  summarize(
    S_W_value = shapiro.test(na.omit(res_e))$p.value,
    Is_Normal = ifelse(S_W_value > 0.05, "Normal", "Non-normal")
  )

# Calculate percentage of normally distributed residuals
norm_pct <- mean(normality_results$Is_Normal == "Normal") * 100
print(paste0("Percentage of cities with normal residuals: ", norm_pct, "%"))

## [1] "Percentage of cities with normal residuals: 95.3020134228188%"

CS-ARDL shows how weather affects pollution. The second-stage examines the impact of city-level characteristics on air quality. The city-specific intercepts were extracted from CS-ARDL(2,1,2) to represent the city‑specific fixed effects, capturing unobserved baseline differences in air quality across cities after accounting for meteorological variables.These are values that tell how much each city’s baseline air quality differs from the overall average in the dataset. OLS Regression is performed to determine which structural factors explain the variation in the baseline AQI across the 149 cities, after controlling for climatic variables.

library(dplyr)
city_data_transformed$Classification <- as.factor(city_data_transformed$Classification)
city_data_transformed$Classification <- relevel(city_data_transformed$Classification, ref = "Component")

#Extract the intercepts and convert row names to a column
coef_df <- as.data.frame(M212$coef_i) 
coef_df$city_name <- rownames(coef_df)

# Select only the city name and the Intercept
df_second_stage <- coef_df |> 
  select(city_name, baseline_AQI = `(Intercept)`) |> 
  mutate(city_name = trimws(city_name)) 

#Collapse the main data to get one row per city
city_static_factors <- city_data_transformed  |> 
  group_by(city_name) |> 
  summarise(
    pop_density = first(population_density),
    active_est_score = first(active_establishment_score),
    motor_vehicle_score = first(motor_vehicles_scores),
    land_area = first(Land_area_sqkm),
    tree_cover = first(tree_cover),
    Classification = first(Classification),
    Region = first(Region)) |> 
  mutate(city_name = trimws(city_name))

# Merge the datasets
final_analysis_df <- merge(df_second_stage, city_static_factors, by = "city_name")

# Run the OLS Regression
model1 <- lm(baseline_AQI ~ log(pop_density)+tree_cover+ 
                       active_est_score + motor_vehicle_score+log(land_area),
                       data = final_analysis_df)

#Display the results
summary(model1)

## 
## Call:
## lm(formula = baseline_AQI ~ log(pop_density) + tree_cover + active_est_score + 
##     motor_vehicle_score + log(land_area), data = final_analysis_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8761 -1.4968  0.0351  1.5014  5.7182 
## 
## Coefficients:
##                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)         -3.258784   3.898987  -0.836   0.4047  
## log(pop_density)     0.047724   0.322976   0.148   0.8827  
## tree_cover          -0.017672   0.009712  -1.820   0.0709 .
## active_est_score    -0.191013   0.601768  -0.317   0.7514  
## motor_vehicle_score -0.634824   1.255734  -0.506   0.6140  
## log(land_area)       0.733240   0.382303   1.918   0.0571 .
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.303 on 143 degrees of freedom
## Multiple R-squared:  0.07171,    Adjusted R-squared:  0.03925 
## F-statistic: 2.209 on 5 and 143 DF,  p-value: 0.05654

Assumption checking:

library(car)

## Loading required package: carData

## 
## Attaching package: 'car'

## The following object is masked from 'package:psych':
## 
##     logit

## The following object is masked from 'package:dplyr':
## 
##     recode

## The following object is masked from 'package:purrr':
## 
##     some

vif(model1)

##    log(pop_density)          tree_cover    active_est_score motor_vehicle_score 
##            6.473946            1.714142            1.662841            1.680764 
##      log(land_area) 
##            5.104363

• No multicollinearity issue.

#shows if the functional form is correctly specified

library(lmtest)
resettest(model1) 

## 
##  RESET test
## 
## data:  model1
## RESET = 0.2298, df1 = 2, df2 = 141, p-value = 0.795

Conclusion: There is no functional form misspecification.

bptest(model1)

## 
##  studentized Breusch-Pagan test
## 
## data:  model1
## BP = 12.924, df = 5, p-value = 0.0241

Conclusion: Residuals are heteroscedastic.

shapiro.test(rstandard(model1))

## 
##  Shapiro-Wilk normality test
## 
## data:  rstandard(model1)
## W = 0.98959, p-value = 0.3364

Ho: The residuals are normally distributed.

Concusion: The residuals is distributed non-normal but Central Limit Theorem makes OLS estimates and hypothesis tests robust even if residuals are not perfectly normal.

outlierTest(model1)

## No Studentized residuals with Bonferroni p < 0.05
## Largest |rstudent|:
##   rstudent unadjusted p-value Bonferroni p
## 9 2.548971           0.011866           NA

influencePlot(model1)

• Cook’s Distance Threshold: 4/149 = 0.02684564.

model2 <-  lm(baseline_AQI ~ log(pop_density)+tree_cover+ 
                       active_est_score+motor_vehicle_score+log(land_area)+tree_cover*active_est_score,
                       data = final_analysis_df)


#Display the results
summary(model2)

## 
## Call:
## lm(formula = baseline_AQI ~ log(pop_density) + tree_cover + active_est_score + 
##     motor_vehicle_score + log(land_area) + tree_cover * active_est_score, 
##     data = final_analysis_df)
## 
## Residuals:
##    Min     1Q Median     3Q    Max 
## -4.776 -1.561  0.063  1.502  5.737 
## 
## Coefficients:
##                             Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                 -3.58590    3.94467  -0.909   0.3649  
## log(pop_density)             0.05766    0.32411   0.178   0.8591  
## tree_cover                  -0.01277    0.01265  -1.009   0.3147  
## active_est_score             0.15650    0.83184   0.188   0.8510  
## motor_vehicle_score         -0.67943    1.26066  -0.539   0.5908  
## log(land_area)               0.75439    0.38473   1.961   0.0519 .
## tree_cover:active_est_score -0.01188    0.01958  -0.607   0.5451  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.308 on 142 degrees of freedom
## Multiple R-squared:  0.07411,    Adjusted R-squared:  0.03499 
## F-statistic: 1.894 on 6 and 142 DF,  p-value: 0.08575

vif(model2)

## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif

##            log(pop_density)                  tree_cover 
##                    6.490519                    2.896798 
##            active_est_score         motor_vehicle_score 
##                    3.163321                    1.686503 
##              log(land_area) tree_cover:active_est_score 
##                    5.146630                    3.493629

resettest(model2)

## 
##  RESET test
## 
## data:  model2
## RESET = 0.37965, df1 = 2, df2 = 140, p-value = 0.6848

bptest(model2)

## 
##  studentized Breusch-Pagan test
## 
## data:  model2
## BP = 12.709, df = 6, p-value = 0.04789

shapiro.test(rstandard(model2))

## 
##  Shapiro-Wilk normality test
## 
## data:  rstandard(model2)
## W = 0.98935, p-value = 0.3179

outlierTest(model2)

## No Studentized residuals with Bonferroni p < 0.05
## Largest |rstudent|:
##   rstudent unadjusted p-value Bonferroni p
## 9 2.552198           0.011769           NA

influencePlot(model2)

model3<-  lm(baseline_AQI ~ log(pop_density)+tree_cover+ 
                       active_est_score+motor_vehicle_score+log(land_area)+tree_cover*motor_vehicle_score,
                       data = final_analysis_df)

summary(model3)

## 
## Call:
## lm(formula = baseline_AQI ~ log(pop_density) + tree_cover + active_est_score + 
##     motor_vehicle_score + log(land_area) + tree_cover * motor_vehicle_score, 
##     data = final_analysis_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.8646 -1.4080  0.0431  1.5131  5.7029 
## 
## Coefficients:
##                                Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                    -2.87527    3.96065  -0.726   0.4691  
## log(pop_density)                0.03307    0.32464   0.102   0.9190  
## tree_cover                     -0.02089    0.01114  -1.876   0.0628 .
## active_est_score               -0.27197    0.61830  -0.440   0.6607  
## motor_vehicle_score            -1.06333    1.45013  -0.733   0.4646  
## log(land_area)                  0.69559    0.38836   1.791   0.0754 .
## tree_cover:motor_vehicle_score  0.03047    0.05122   0.595   0.5529  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.308 on 142 degrees of freedom
## Multiple R-squared:  0.07402,    Adjusted R-squared:  0.03489 
## F-statistic: 1.892 on 6 and 142 DF,  p-value: 0.08618

vif(model3)

## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif

##               log(pop_density)                     tree_cover 
##                       6.511426                       2.244934 
##               active_est_score            motor_vehicle_score 
##                       1.747509                       2.231320 
##                 log(land_area) tree_cover:motor_vehicle_score 
##                       5.243638                       2.329780

resettest(model3)

## 
##  RESET test
## 
## data:  model3
## RESET = 0.35072, df1 = 2, df2 = 140, p-value = 0.7048

bptest(model3)

## 
##  studentized Breusch-Pagan test
## 
## data:  model3
## BP = 14.365, df = 6, p-value = 0.02582

shapiro.test(rstandard(model3))

## 
##  Shapiro-Wilk normality test
## 
## data:  rstandard(model3)
## W = 0.98899, p-value = 0.2916

outlierTest(model3)

## No Studentized residuals with Bonferroni p < 0.05
## Largest |rstudent|:
##   rstudent unadjusted p-value Bonferroni p
## 9 2.536318           0.012292           NA

influencePlot(model3)

model4<-  lm(baseline_AQI ~ log(pop_density)+tree_cover+ 
                       active_est_score+motor_vehicle_score+log(land_area)+ active_est_score*motor_vehicle_score,
                       data = final_analysis_df)


#Display the results
summary(model4)

## 
## Call:
## lm(formula = baseline_AQI ~ log(pop_density) + tree_cover + active_est_score + 
##     motor_vehicle_score + log(land_area) + active_est_score * 
##     motor_vehicle_score, data = final_analysis_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.7289 -1.5215 -0.0191  1.5699  5.7374 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                          -2.741693   3.904067  -0.702   0.4837  
## log(pop_density)                      0.046252   0.321928   0.144   0.8860  
## tree_cover                           -0.017991   0.009684  -1.858   0.0653 .
## active_est_score                     -0.771074   0.730593  -1.055   0.2930  
## motor_vehicle_score                  -2.430450   1.798312  -1.352   0.1787  
## log(land_area)                        0.695023   0.382050   1.819   0.0710 .
## active_est_score:motor_vehicle_score  2.232638   1.605491   1.391   0.1665  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.296 on 142 degrees of freedom
## Multiple R-squared:  0.08418,    Adjusted R-squared:  0.04549 
## F-statistic: 2.175 on 6 and 142 DF,  p-value: 0.04875

car::vif(model4)

## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif

##                     log(pop_density)                           tree_cover 
##                             6.474016                             1.715103 
##                     active_est_score                  motor_vehicle_score 
##                             2.467006                             3.469514 
##                       log(land_area) active_est_score:motor_vehicle_score 
##                             5.130912                             4.730910

lmtest::resettest(model4)

## 
##  RESET test
## 
## data:  model4
## RESET = 3.0653, df1 = 2, df2 = 140, p-value = 0.04978

bptest(model4)

## 
##  studentized Breusch-Pagan test
## 
## data:  model4
## BP = 11.225, df = 6, p-value = 0.08167

shapiro.test(rstandard(model4))

## 
##  Shapiro-Wilk normality test
## 
## data:  rstandard(model4)
## W = 0.98723, p-value = 0.1882

outlierTest(model4)

## No Studentized residuals with Bonferroni p < 0.05
## Largest |rstudent|:
##   rstudent unadjusted p-value Bonferroni p
## 9 2.567064           0.011299           NA

influencePlot(model4)

model5<-  lm(baseline_AQI ~ log(pop_density)+tree_cover+ 
                       active_est_score+motor_vehicle_score+log(land_area)+ tree_cover*active_est_score+tree_cover*motor_vehicle_score+active_est_score*motor_vehicle_score,
                       data = final_analysis_df)


#Display the results
summary(model5)

## 
## Call:
## lm(formula = baseline_AQI ~ log(pop_density) + tree_cover + active_est_score + 
##     motor_vehicle_score + log(land_area) + tree_cover * active_est_score + 
##     tree_cover * motor_vehicle_score + active_est_score * motor_vehicle_score, 
##     data = final_analysis_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.6699 -1.4748  0.0819  1.6309  5.7354 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                          -2.28295    3.97931  -0.574   0.5671  
## log(pop_density)                      0.02469    0.32277   0.077   0.9391  
## tree_cover                           -0.01645    0.01274  -1.290   0.1990  
## active_est_score                     -0.27315    1.00517  -0.272   0.7862  
## motor_vehicle_score                  -4.01660    2.14629  -1.871   0.0634 .
## log(land_area)                        0.62594    0.38845   1.611   0.1093  
## tree_cover:active_est_score          -0.02744    0.02457  -1.117   0.2660  
## tree_cover:motor_vehicle_score        0.09226    0.06480   1.424   0.1568  
## active_est_score:motor_vehicle_score  2.46350    1.65400   1.489   0.1386  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.294 on 140 degrees of freedom
## Multiple R-squared:  0.098,  Adjusted R-squared:  0.04646 
## F-statistic: 1.901 on 8 and 140 DF,  p-value: 0.0643

vif(model5)

## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif

##                     log(pop_density)                           tree_cover 
##                             6.514752                             2.974021 
##                     active_est_score                  motor_vehicle_score 
##                             4.674562                             4.947174 
##                       log(land_area)          tree_cover:active_est_score 
##                             5.309559                             5.568793 
##       tree_cover:motor_vehicle_score active_est_score:motor_vehicle_score 
##                             3.773928                             5.026260

resettest(model5)

## 
##  RESET test
## 
## data:  model5
## RESET = 1.5975, df1 = 2, df2 = 138, p-value = 0.2061

bptest(model5)

## 
##  studentized Breusch-Pagan test
## 
## data:  model5
## BP = 13.237, df = 8, p-value = 0.1039

shapiro.test(rstandard(model5))

## 
##  Shapiro-Wilk normality test
## 
## data:  rstandard(model5)
## W = 0.99024, p-value = 0.3918

outlierTest(model5)

## No Studentized residuals with Bonferroni p < 0.05
## Largest |rstudent|:
##   rstudent unadjusted p-value Bonferroni p
## 9 2.568428           0.011271           NA

influencePlot(model5)

model6 <- lm(baseline_AQI ~ log(pop_density)+tree_cover+ 
                       active_est_score + motor_vehicle_score+log(land_area)+Classification,
                       data = final_analysis_df)


#Display the results
summary(model6)

## 
## Call:
## lm(formula = baseline_AQI ~ log(pop_density) + tree_cover + active_est_score + 
##     motor_vehicle_score + log(land_area) + Classification, data = final_analysis_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.3110 -1.4833 -0.0351  1.7119  5.6601 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)  
## (Intercept)                          0.971653   4.950139   0.196   0.8447  
## log(pop_density)                    -0.348748   0.431316  -0.809   0.4201  
## tree_cover                          -0.018591   0.009774  -1.902   0.0592 .
## active_est_score                     0.248551   0.682167   0.364   0.7161  
## motor_vehicle_score                 -0.878309   1.268281  -0.693   0.4898  
## log(land_area)                       0.394615   0.453998   0.869   0.3862  
## ClassificationHighly Urbanized       0.936986   0.683468   1.371   0.1726  
## ClassificationIndependent Component -0.152689   1.071100  -0.143   0.8868  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.304 on 141 degrees of freedom
## Multiple R-squared:  0.08426,    Adjusted R-squared:  0.0388 
## F-statistic: 1.854 on 7 and 141 DF,  p-value: 0.08167

library(lmtest)
vif(model6)

##                          GVIF Df GVIF^(1/(2*Df))
## log(pop_density)    11.540337  1        3.397107
## tree_cover           1.735069  1        1.317220
## active_est_score     2.135853  1        1.461456
## motor_vehicle_score  1.713720  1        1.309091
## log(land_area)       7.195016  1        2.682353
## Classification       2.329228  2        1.235387

resettest(model6)

## 
##  RESET test
## 
## data:  model6
## RESET = 0.14808, df1 = 2, df2 = 139, p-value = 0.8625

bptest(model6)

## 
##  studentized Breusch-Pagan test
## 
## data:  model6
## BP = 14.991, df = 7, p-value = 0.03612

shapiro.test(rstandard(model6))

## 
##  Shapiro-Wilk normality test
## 
## data:  rstandard(model6)
## W = 0.98499, p-value = 0.1056

outlierTest(model6)

## No Studentized residuals with Bonferroni p < 0.05
## Largest |rstudent|:
##   rstudent unadjusted p-value Bonferroni p
## 9 2.523112            0.01275           NA

influencePlot(model6)

model7 <- lm(baseline_AQI ~ log(pop_density)+tree_cover+ 
                       active_est_score + motor_vehicle_score+Classification,
                       data = final_analysis_df)


#Display the results
summary(model7)

## 
## Call:
## lm(formula = baseline_AQI ~ log(pop_density) + tree_cover + active_est_score + 
##     motor_vehicle_score + Classification, data = final_analysis_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2996 -1.4501 -0.1065  1.7266  5.5214 
## 
## Coefficients:
##                                      Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                          5.047865   1.583370   3.188  0.00176 **
## log(pop_density)                    -0.677904   0.206289  -3.286  0.00128 **
## tree_cover                          -0.017482   0.009682  -1.806  0.07309 . 
## active_est_score                     0.561100   0.579201   0.969  0.33432   
## motor_vehicle_score                 -0.697632   1.250053  -0.558  0.57767   
## ClassificationHighly Urbanized       1.256448   0.575738   2.182  0.03073 * 
## ClassificationIndependent Component -0.154910   1.070174  -0.145  0.88511   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.302 on 142 degrees of freedom
## Multiple R-squared:  0.07936,    Adjusted R-squared:  0.04046 
## F-statistic:  2.04 on 6 and 142 DF,  p-value: 0.06413

vif(model7)

##                         GVIF Df GVIF^(1/(2*Df))
## log(pop_density)    2.644389  1        1.626158
## tree_cover          1.705478  1        1.305939
## active_est_score    1.542400  1        1.241934
## motor_vehicle_score 1.667686  1        1.291389
## Classification      1.652425  2        1.133784

resettest(model7)

## 
##  RESET test
## 
## data:  model7
## RESET = 0.3291, df1 = 2, df2 = 140, p-value = 0.7201

bptest(model7)

## 
##  studentized Breusch-Pagan test
## 
## data:  model7
## BP = 15.074, df = 6, p-value = 0.01969

shapiro.test(rstandard(model7))

## 
##  Shapiro-Wilk normality test
## 
## data:  rstandard(model7)
## W = 0.98421, p-value = 0.08606

outlierTest(model7)

## No Studentized residuals with Bonferroni p < 0.05
## Largest |rstudent|:
##   rstudent unadjusted p-value Bonferroni p
## 9 2.454245           0.015336           NA

influencePlot(model7)

model8 <- lm(baseline_AQI ~ log(pop_density)+tree_cover+ 
                       active_est_score+motor_vehicle_score +Classification+tree_cover* 
                       active_est_score,
                       data = final_analysis_df)


#Display the results
summary(model8)

## 
## Call:
## lm(formula = baseline_AQI ~ log(pop_density) + tree_cover + active_est_score + 
##     motor_vehicle_score + Classification + tree_cover * active_est_score, 
##     data = final_analysis_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.4025 -1.5186 -0.1353  1.7450  5.5357 
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                          4.94911    1.59450   3.104  0.00231 **
## log(pop_density)                    -0.68827    0.20738  -3.319  0.00115 **
## tree_cover                          -0.01235    0.01267  -0.975  0.33145   
## active_est_score                     0.94049    0.83702   1.124  0.26309   
## motor_vehicle_score                 -0.74210    1.25471  -0.591  0.55517   
## ClassificationHighly Urbanized       1.29073    0.57953   2.227  0.02752 * 
## ClassificationIndependent Component -0.12931    1.07323  -0.120  0.90427   
## tree_cover:active_est_score         -0.01232    0.01958  -0.629  0.53031   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.307 on 141 degrees of freedom
## Multiple R-squared:  0.08194,    Adjusted R-squared:  0.03636 
## F-statistic: 1.798 on 7 and 141 DF,  p-value: 0.09218

vif(model8)

## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif

##                                 GVIF Df GVIF^(1/(2*Df))
## log(pop_density)            2.661193  1        1.631316
## tree_cover                  2.910262  1        1.705949
## active_est_score            3.207443  1        1.790934
## motor_vehicle_score         1.672996  1        1.293443
## Classification              1.668718  2        1.136569
## tree_cover:active_est_score 3.499103  1        1.870589

resettest(model8)

## 
##  RESET test
## 
## data:  model8
## RESET = 0.54064, df1 = 2, df2 = 139, p-value = 0.5836

bptest(model8)

## 
##  studentized Breusch-Pagan test
## 
## data:  model8
## BP = 14.109, df = 7, p-value = 0.04928

shapiro.test(rstandard(model8))

## 
##  Shapiro-Wilk normality test
## 
## data:  rstandard(model8)
## W = 0.98604, p-value = 0.1387

outlierTest(model8)

## No Studentized residuals with Bonferroni p < 0.05
## Largest |rstudent|:
##   rstudent unadjusted p-value Bonferroni p
## 9 2.455831            0.01528           NA

influencePlot(model8)

model9 <- lm(baseline_AQI ~ log(pop_density)+tree_cover+ 
                       active_est_score+motor_vehicle_score+Classification+tree_cover* 
                       motor_vehicle_score,
                       data = final_analysis_df)


#Display the results
summary(model9)

## 
## Call:
## lm(formula = baseline_AQI ~ log(pop_density) + tree_cover + active_est_score + 
##     motor_vehicle_score + Classification + tree_cover * motor_vehicle_score, 
##     data = final_analysis_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.2898 -1.4651 -0.0677  1.5500  5.5172 
## 
## Coefficients:
##                                     Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                          5.02248    1.58758   3.164  0.00191 **
## log(pop_density)                    -0.65812    0.20944  -3.142  0.00204 **
## tree_cover                          -0.02074    0.01115  -1.859  0.06506 . 
## active_est_score                     0.44703    0.61167   0.731  0.46609   
## motor_vehicle_score                 -1.12852    1.44899  -0.779  0.43738   
## ClassificationHighly Urbanized       1.20705    0.58306   2.070  0.04026 * 
## ClassificationIndependent Component -0.15833    1.07265  -0.148  0.88286   
## tree_cover:motor_vehicle_score       0.03021    0.05103   0.592  0.55479   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.307 on 141 degrees of freedom
## Multiple R-squared:  0.08164,    Adjusted R-squared:  0.03605 
## F-statistic: 1.791 on 7 and 141 DF,  p-value: 0.09359

vif(model9)

## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif

##                                    GVIF Df GVIF^(1/(2*Df))
## log(pop_density)               2.713460  1        1.647258
## tree_cover                     2.252487  1        1.500829
## active_est_score               1.712310  1        1.308552
## motor_vehicle_score            2.230464  1        1.493474
## Classification                 1.687029  2        1.139674
## tree_cover:motor_vehicle_score 2.315392  1        1.521641

resettest(model9)

## 
##  RESET test
## 
## data:  model9
## RESET = 0.0073525, df1 = 2, df2 = 139, p-value = 0.9927

bptest(model9)

## 
##  studentized Breusch-Pagan test
## 
## data:  model9
## BP = 16.667, df = 7, p-value = 0.01967

shapiro.test(rstandard(model9))

## 
##  Shapiro-Wilk normality test
## 
## data:  rstandard(model9)
## W = 0.98454, p-value = 0.09391

outlierTest(model9)

## No Studentized residuals with Bonferroni p < 0.05
## Largest |rstudent|:
##   rstudent unadjusted p-value Bonferroni p
## 9 2.446745           0.015654           NA

influencePlot(model9)

model10 <- lm(baseline_AQI ~ log(pop_density)+tree_cover+ 
                       active_est_score+motor_vehicle_score+Classification+active_est_score*motor_vehicle_score,
                       data = final_analysis_df)


#Display the results
summary(model10)

## 
## Call:
## lm(formula = baseline_AQI ~ log(pop_density) + tree_cover + active_est_score + 
##     motor_vehicle_score + Classification + active_est_score * 
##     motor_vehicle_score, data = final_analysis_df)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -4.1805 -1.5141 -0.0327  1.6922  5.5488 
## 
## Coefficients:
##                                       Estimate Std. Error t value Pr(>|t|)   
## (Intercept)                           5.064927   1.580821   3.204  0.00168 **
## log(pop_density)                     -0.637604   0.208614  -3.056  0.00268 **
## tree_cover                           -0.017566   0.009666  -1.817  0.07129 . 
## active_est_score                     -0.004887   0.743201  -0.007  0.99476   
## motor_vehicle_score                  -2.251477   1.788970  -1.259  0.21028   
## ClassificationHighly Urbanized        1.160293   0.580235   2.000  0.04745 * 
## ClassificationIndependent Component  -0.037681   1.072776  -0.035  0.97203   
## active_est_score:motor_vehicle_score  1.971468   1.626266   1.212  0.22744   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 2.298 on 141 degrees of freedom
## Multiple R-squared:  0.08885,    Adjusted R-squared:  0.04362 
## F-statistic: 1.964 on 7 and 141 DF,  p-value: 0.06402

vif(model10)

## there are higher-order terms (interactions) in this model
## consider setting type = 'predictor'; see ?vif

##                                          GVIF Df GVIF^(1/(2*Df))
## log(pop_density)                     2.713290  1        1.647207
## tree_cover                           1.705566  1        1.305973
## active_est_score                     2.547914  1        1.596219
## motor_vehicle_score                  3.426866  1        1.851180
## Classification                       1.700963  2        1.142020
## active_est_score:motor_vehicle_score 4.844677  1        2.201063

resettest(model10)

## 
##  RESET test
## 
## data:  model10
## RESET = 3.5221, df1 = 2, df2 = 139, p-value = 0.0322

bptest(model10)

## 
##  studentized Breusch-Pagan test
## 
## data:  model10
## BP = 13.462, df = 7, p-value = 0.06162

shapiro.test(rstandard(model10))

## 
##  Shapiro-Wilk normality test
## 
## data:  rstandard(model10)
## W = 0.98279, p-value = 0.05936

outlierTest(model10)

## No Studentized residuals with Bonferroni p < 0.05
## Largest |rstudent|:
##   rstudent unadjusted p-value Bonferroni p
## 9 2.471631           0.014649           NA

influencePlot(model10)

library(modelsummary)
a <- modelsummary(
  list(
    "M1" = model1,
    "M2" = model2,
    "M3" =model3,
    "M4" =model4,
    "M5" =model5,
    "M6" =model6,
     "M7" =model7,
     "M8" =model8,
     "M9" =model9,
     "M10" =model10
    
  ),
  stars = TRUE
)
a

	M1	M2	M3	M4	M5	M6	M7	M8	M9	M10
+ p < 0.1, * p < 0.05, ** p < 0.01, *** p < 0.001
(Intercept)	-3.259	-3.586	-2.875	-2.742	-2.283	0.972	5.048**	4.949**	5.022**	5.065**
	(3.899)	(3.945)	(3.961)	(3.904)	(3.979)	(4.950)	(1.583)	(1.594)	(1.588)	(1.581)
log(pop_density)	0.048	0.058	0.033	0.046	0.025	-0.349	-0.678**	-0.688**	-0.658**	-0.638**
	(0.323)	(0.324)	(0.325)	(0.322)	(0.323)	(0.431)	(0.206)	(0.207)	(0.209)	(0.209)
tree_cover	-0.018+	-0.013	-0.021+	-0.018+	-0.016	-0.019+	-0.017+	-0.012	-0.021+	-0.018+
	(0.010)	(0.013)	(0.011)	(0.010)	(0.013)	(0.010)	(0.010)	(0.013)	(0.011)	(0.010)
active_est_score	-0.191	0.157	-0.272	-0.771	-0.273	0.249	0.561	0.940	0.447	-0.005
	(0.602)	(0.832)	(0.618)	(0.731)	(1.005)	(0.682)	(0.579)	(0.837)	(0.612)	(0.743)
motor_vehicle_score	-0.635	-0.679	-1.063	-2.430	-4.017+	-0.878	-0.698	-0.742	-1.129	-2.251
	(1.256)	(1.261)	(1.450)	(1.798)	(2.146)	(1.268)	(1.250)	(1.255)	(1.449)	(1.789)
log(land_area)	0.733+	0.754+	0.696+	0.695+	0.626	0.395
	(0.382)	(0.385)	(0.388)	(0.382)	(0.388)	(0.454)
tree_cover × active_est_score		-0.012			-0.027			-0.012
		(0.020)			(0.025)			(0.020)
tree_cover × motor_vehicle_score			0.030		0.092				0.030
			(0.051)		(0.065)				(0.051)
active_est_score × motor_vehicle_score				2.233	2.464					1.971
				(1.605)	(1.654)					(1.626)
ClassificationHighly Urbanized						0.937	1.256*	1.291*	1.207*	1.160*
						(0.683)	(0.576)	(0.580)	(0.583)	(0.580)
ClassificationIndependent Component						-0.153	-0.155	-0.129	-0.158	-0.038
						(1.071)	(1.070)	(1.073)	(1.073)	(1.073)
Num.Obs.	149	149	149	149	149	149	149	149	149	149
R2	0.072	0.074	0.074	0.084	0.098	0.084	0.079	0.082	0.082	0.089
R2 Adj.	0.039	0.035	0.035	0.045	0.046	0.039	0.040	0.036	0.036	0.044
AIC	679.3	680.9	681.0	679.3	681.1	681.3	680.1	681.7	681.7	680.6
BIC	700.4	705.0	705.0	703.3	711.1	708.3	704.1	708.7	708.8	707.6
Log.Lik.	-332.666	-332.473	-332.480	-331.658	-330.525	-331.651	-332.049	-331.840	-331.864	-331.277
F	2.209	1.894	1.892	2.175	1.901	1.854	2.040	1.798	1.791	1.964
RMSE	2.26	2.25	2.25	2.24	2.22	2.24	2.25	2.24	2.24	2.24

Mapping the Baseline Pollution Levels

# Load Essential Libraries
library(sf)
library(readxl)
library(dplyr)
library(ggplot2)
library(gstat)
library(stars)

## Loading required package: abind

library(ggrepel)
library(ggspatial)
library(patchwork)

#Data Import & Join
ph_regions <- st_read("gadm36_PHL_1.shp") |>  st_make_valid()

## Reading layer `gadm36_PHL_1' from data source 
##   `C:\Users\user\Documents\For research proposal\Data\For data processing\FINAL\gadm36_PHL_1.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 81 features and 10 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 116.9283 ymin: 4.58694 xmax: 126.6053 ymax: 21.07014
## Geodetic CRS:  WGS 84

ph_cities  <- st_read("gadm36_PHL_2.shp") |>  st_make_valid()

## Reading layer `gadm36_PHL_2' from data source 
##   `C:\Users\user\Documents\For research proposal\Data\For data processing\FINAL\gadm36_PHL_2.shp' 
##   using driver `ESRI Shapefile'
## Simple feature collection with 1647 features and 13 fields
## Geometry type: MULTIPOLYGON
## Dimension:     XY
## Bounding box:  xmin: 116.9283 ymin: 4.58694 xmax: 126.6053 ymax: 21.07014
## Geodetic CRS:  WGS 84

aqi_df     <- read_excel("Philippine_Cities_Baseline_AQI.xlsx")

## New names:
## • `` -> `...8`

ph_data <- ph_cities |> 
  mutate(JOIN_CITY = toupper(NAME_2), JOIN_PROV = toupper(NAME_1)) |> 
  left_join(
    aqi_df |>  mutate(City_U = toupper(city_name), Prov_U = toupper(province)),
    by = c("JOIN_CITY" = "City_U", "JOIN_PROV" = "Prov_U"))

ph_points <- ph_data |>  
  filter(!is.na(baseline_AQI)) |> 
  st_centroid(of_largest_polygon = TRUE)

## Warning: st_centroid assumes attributes are constant over geometries

# Smooth Interpolation 
grid <- st_as_stars(st_bbox(ph_regions),n = 200000, dx = 0.01, dy = 0.01) 
grid <- grid[st_union(ph_regions)] 
idw_res <- idw(baseline_AQI ~ 1, ph_points, grid, idp = 2)

## [inverse distance weighted interpolation]

idw_res$var1.pred[is.na(idw_res$var1.pred)] <- -999

#AQI Pallete
aqi_scale <- scale_fill_gradientn(
  colours = c(
    "aliceblue",   # <-5
    "#F2E8D2",   # -5 to -2.5
    "#F6D04D",   # -2.5 to 0
    "#F68B54",   # 0 to 2.5
    "#E84A2F",   # 2.5 to 5
    "#B80000"),   # >5
  
  values = scales::rescale(
    c(-6, -2.5, -1, 1, 2.5, 6)),
  limits = c(-6, 6),
  oob = scales::squish,
  name = "Baseline AQI")

main_map <- ggplot() +
  geom_stars(data = idw_res, aes(fill = var1.pred)) +
  geom_sf(data = ph_regions, fill = NA, color = "black", linewidth = 0.1) +
  geom_sf(data = ph_points, shape = 1, size = 0.7,
          color = "black", alpha = 0.6) +
  aqi_scale +
 coord_sf(
  xlim = c(114.5, 127),
  ylim = c(5, 21),
  expand = FALSE,
  default_crs = sf::st_crs(4326),
  datum = sf::st_crs(4326)
  ) +
  annotation_scale(location = "bl", style = "bar") +
  annotation_north_arrow(
    location = "br",
    style = north_arrow_orienteering()
  ) 

map_theme <- theme_minimal() +
  theme(
    panel.background = element_rect(
      fill = "aliceblue",
      colour = NA),
    plot.background = element_rect(
      fill = "white",
      colour = NA),
    panel.grid.major = element_blank(),
    panel.grid.minor = element_blank(),
    axis.title = element_blank(),
    axis.text = element_text(
      size = 50,
      colour = "grey20"),
    panel.border = element_rect(
      colour = "black",
      linewidth = 1,
      fill = NA))

inset_base <- function(xlim, ylim) {
  ggplot() +
    geom_stars(data = idw_res, aes(fill = var1.pred)) +
    geom_sf(data = ph_regions, fill = NA, color = "black", linewidth = 0.2) +
    geom_sf(data = ph_points, shape = 1, size = 1.2, color = "black") +
    geom_text_repel(
      data = ph_points  |> filter(baseline_AQI > 0),
      aes(label = NAME_2, geometry = geometry),
      stat = "sf_coordinates",
      size = 3,
      fontface = "bold",
      segment.size = 0.2,
      max.overlaps = 45,
      bg.color = "white",
      bg.r = 0.1) +
    coord_sf(
      xlim = xlim,
      ylim = ylim,
      expand = FALSE,
      default_crs = sf::st_crs(4326),
      datum = NA) +
    aqi_scale +
    theme_void() +
    theme(
      legend.position = "none",
      panel.border = element_rect(
        color = "black",
        fill = NA,
        linewidth = 0.5),
      panel.background = element_rect(
        fill = "aliceblue",
        color = NA),
      plot.background = element_rect(
        fill = "aliceblue",
        color = NA))
}

#Luzon inset
luzon_in <- inset_base(
  c(119.95, 121.55),
  c(13.95, 15.55)) +
  annotate(
    "rect",
    xmin = 119.95,
    xmax = 121.55,
    ymin = 15.40,
    ymax = 15.55,
    fill = "#0B7A28",
    colour = "#0B7A28") +
  annotate(
    "text",
    x = 120.75,
    y = 15.475,
    label = "LUZON",
    colour = "white",
    fontface = "bold",
    size = 5) +
  annotate(
    "text",
    x = 120.29,
    y = 15.08,
    label = "CENTRAL\nLUZON",
    colour = "#006400",
    fontface = "bold",
    size = 3,
    lineheight = 0.9) +
  annotate(
    "text",
    x = 120.72,
    y = 14.67,
    label = "NCR",
    colour = "#006400",
    fontface = "bold",
    size = 3) +
  annotate(
    "text",
    x = 120.44,
    y = 14.39,
    label = "CALABARZON",
    colour = "#006400",
    fontface = "bold",
    size = 3) +
  theme(
    plot.margin = margin(0,0,5,0),

    panel.background = element_rect(
      fill = "aliceblue",
      colour = NA ),
  plot.background = element_rect(
    fill = "white",
    colour = NA),
    panel.border = element_rect(
      colour = "#0B7A28",
      linewidth = 1.2,
      fill = NA))

# VISAYAS INSET
visayas_in <- inset_base(
  c(122.10, 123.90),
  c(9.30, 11.10)) +
  annotate(
    "rect",
    xmin = 122.10,
    xmax = 123.90,
    ymin = 10.95,
    ymax = 11.10,
    fill = "#0057B8",
    colour = "#0057B8") +
  annotate(
    "text",
    x = 123.00,
    y = 11.025,
    label = "VISAYAS",
    colour = "white",
    fontface = "bold",
    size = 5) +
   annotate(
    "text",
    x = 122.43,
    y = 10.18,
    label = "WESTERN \nVISAYAS",
    colour = "#006400",
    fontface = "bold",
    size = 3) +
  theme(
    plot.margin = margin(0,0,5,0),
    panel.background = element_rect(
      fill = "aliceblue",
      colour = NA),
  plot.background = element_rect(
    fill = "white", 
    colour = NA),
    panel.border = element_rect(
      colour = "#0057B8",
      linewidth = 1.2,
      fill = NA))


# MINDANAO INSET

davao_in <- inset_base(
  c(124.85, 126.45),
  c(6.30, 7.80)) +
  annotate(
    "rect",
    xmin = 124.85,
    xmax = 126.45,
    ymin = 7.65,
    ymax = 7.80,
    fill = "#6A1B9A",
    colour = "#6A1B9A") +
  annotate(
    "text",
    x = 125.65,
    y = 7.725,
    label = "MINDANAO",
    colour = "white",
    fontface = "bold",
    size = 5) +
  annotate(
    "text",
    x = 125.70,
    y = 6.70,
    label = "DAVAO REGION",
    colour = "#006400",
    fontface = "bold",
    size = 3) +
  theme(
    plot.margin = margin(0,0,0,0),
    panel.background = element_rect(
      fill = "aliceblue",
      colour = NA), 
  plot.background = element_rect(
    fill = "white", 
    colour = NA),
    panel.border = element_rect(
      colour = "#6A1B9A",
      linewidth = 1.2,
      fill = NA))



# MAIN MAP 
main_map_clean <-main_map +
  coord_sf(
    xlim = c(116, 127),
    ylim = c(4, 22),
    expand = TRUE,
    datum = sf::st_crs(4326),
    label_axes = list(bottom = "E",right  = "N")) +
  annotate(
    "rect",
    xmin = 119.95,
    xmax = 121.55,
    ymin = 13.95,
    ymax = 15.55,
    colour = "#0B7A28",
    fill = NA,
    linewidth = 1) +
  annotate(
    "rect",
    xmin = 122.10,
    xmax = 123.90,
    ymin = 9.30,
    ymax = 11.10,
    colour = "#0057B8",
    fill = NA,
    linewidth = 1) +
  annotate(
    "rect",
    xmin = 124.85,
    xmax = 126.45,
    ymin = 6.30,
    ymax = 7.48,
    colour = "#6A1B9A",
    fill = NA,
    linewidth = 1) +
  aqi_scale +
  map_theme +
  theme(plot.margin = margin(15, 30, 30, 5),
         axis.text.x = element_text(size = 16),
        axis.text.y = element_text(size = 16),
    legend.position = "none",
    legend.title = element_text(
      size = 16,
      face = "bold"),
    legend.text = element_text(
      size = 11),
    legend.background = element_rect(
      fill = scales::alpha("white", 0.95),
      colour = "grey70"),
     panel.background = element_rect(fill = "aliceblue", colour = NA),  
    plot.background  = element_rect(fill = "white", colour = NA))

## Coordinate system already present.
## ℹ Adding new coordinate system, which will replace the existing one.

## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.

# FINAL LAYOUT
final_layout <-
  (luzon_in / visayas_in / davao_in) | main_map_clean

final_layout +
  plot_layout(
    widths = c(1.05, 1.97), heights = c(1, 1, 1))

## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data

## `geom_raster()` only works with linear coordinate systems, not `coord_sf()`.
## ℹ Falling back to drawing as `geom_rect()`.

## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data

## `geom_raster()` only works with linear coordinate systems, not `coord_sf()`.
## ℹ Falling back to drawing as `geom_rect()`.

## Warning in st_point_on_surface.sfc(sf::st_zm(x)): st_point_on_surface may not
## give correct results for longitude/latitude data

## `geom_raster()` only works with linear coordinate systems, not `coord_sf()`.
## ℹ Falling back to drawing as `geom_rect()`.