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4-main-analysis.R
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## -----------------------------------------------------------------------------------------
# Script: data_cleaning.R
# Author: Bisrat
# This R script performs data analysis and modeling using LSMS-ISA Ethiopia, focusing on maize yield, nitrogen application, and rainfall data. It includes:
# - Data cleaning and preparation using the `tidyverse` and `haven` packages.
# - Variable labeling with `sjlabelled` for better readability.
# - Statistical models such as Pooled OLS, Fixed Effects (FE), and Lasso regression.
# - Visualization of key variables like maize yield and nitrogen per hectare.
# - Handling of missing data and creation of balanced panel data.
# - Construction of regression results tables and model summaries with robust standard errors.
# - Saving the results as LaTeX tables for later use in reports.
#
# Libraries used:
# - `tidyverse`, `haven`, `sjPlot`, `ggplot2`, `gtsummary`, `plm`, `glmnet`, `margins`, and others.
# - Data is read, cleaned, and analyzed using multiple models for regression and visualization.
#
# Results from the models are stored in data frames and visualized for better understanding.
# The code also includes various plots saved for use in the paper
## -----------------------------------------------------------------------------------------
knitr::opts_chunk$set(
message = FALSE,
echo = FALSE,
warning = FALSE,
tidy = TRUE,
tidy.opts = list(width.cutoff = 50),
out.width = "100%",
fig.width = 7,
fig.asp = 0.75
)
options(width = 100)
if (!requireNamespace("pacman", quietly = TRUE)) {
install.packages("pacman")
}
pacman::p_load(
haven,
here,
plotly,
leaflet,
ggthemes,
ggstatsplot,
sjPlot,
sjlabelled,
gtsummary,
geosphere,
ggpie,
ggthemr,
webr,
gtools,
plm,
lmtest,
sandwich,
kableExtra,
lfe,
glmnet,
plotmo,
modelsummary,
tidyverse,
margins
)
extrafont::loadfonts(quiet = T)
my_font <- "Frutiger"
my_font_2 <- "Segoe UI"
my_size <- 12
my_colors <- list(
"#F09C26",
"#50AE5E",
"#BFCCB5",
"#7C96AB",
"#B7B7B7",
"#EDC6B1",
"#F5F5DC",
"#A9A9A9",
"#FFC0CB",
"#8B0000",
"#8A2BE2",
"#8F9779"
)
colors <- c(
"#8F9779",
"#FFD700",
"#8B4513",
"#87CEEB",
"#FFA500",
"#F5F5DC",
"#A9A9A9",
"#FFC0CB",
"#8B0000",
"#596baf",
"#8A2BE2"
)
my_colors_1 <- c(
"#ffae49",
"#44b7c2",
"#024b7a",
"#ee4144",
"#1f5e77",
"#c792e9",
"#5eeda0",
"#019d9c",
"#83329b"
)
likert_colors <- c("#a3336d", "#b55b8a", "#7a88bf", "#596baf", "#e7e7e7")
my_colors <- c(
"#1f77b4",
"#ff7f0e",
"#2ca02c",
"#d62728",
"#9467bd",
"#8c564b",
"#e377c2",
"#7f7f7f",
"#bcbd22",
"#17becf"
)
color_new <- c(
"#F2DFEB",
"#AEDFF2",
"#72DBF2",
"#F2C879",
"#F2D8A7",
"#8990B3",
"#FFD3C4",
"#DEE3FF",
"#DEFFC4",
"#A0B392"
)
Darjeeling1 <- wesanderson::wes_palettes$Darjeeling1
Darjeeling2 <- wesanderson::wes_palettes$Darjeeling2
Royal2 <- wesanderson::wes_palettes$Royal2
Moonrise1 <- wesanderson::wes_palettes$Moonrise1
Zissou1 <- wesanderson::wes_palettes$Zissou1Continuous
Chevalier1 <- wesanderson::wes_palettes$Chevalier1
Cavalcanti1 <- wesanderson::wes_palettes$Cavalcanti1
GrandBudapest1 <- wesanderson::wes_palettes$GrandBudapest1
bar_colors <- c("#1B81BB",
"#288D8D",
"#5F4C60",
"#7570B3",
"#9F0000",
"#FFBB70",
"#bcbd22")
# devtools::install_github('cttobin/ggthemr')
ggthemr("pale")
## -----------------------------------------------------------------------------------------
cleaned_data_path <- here("data", "cleaned_data")
main_data_0 <- as_factor(read_dta(paste0(
cleaned_data_path, "/main_data_with_rainfall.dta"
)), only_labelled = TRUE)
main_data <- main_data_0 %>%
mutate(year = as.factor(year)) %>%
sjlabelled::var_labels(year = "Year") %>%
filter(nitrogen_per_hectare > 0) %>%
filter(saq01 != "TIGRAY" &
saq01 != "AFAR" & saq01 != "SOMALI" & saq01 != "GAMBELLA") %>%
# refactor the region variable
mutate(saq01 = droplevels(saq01)) %>%
mutate(predominant_soil_type = ifelse(predominant_soil_type == "", NA, predominant_soil_type)) %>%
mutate(
male_plot_manager = factor(male_plot_manager),
organic_fertilizer = factor(organic_fertilizer),
farmer_purchased_seed = factor(farmer_purchased_seed),
irrigation = factor(irrigation),
under_extension = factor(under_extension),
agro_chemical_use = factor(agro_chemical_use),
mechanization = factor(mechanization),
no_other_crop = factor(no_other_crop),
one_other_crop = factor(one_other_crop),
two_other_crops = factor(two_other_crops),
three_or_more_other_crops = factor(three_or_more_other_crops),
legume = factor(legume),
document_for_plot = factor(document_for_plot),
soil_erosion_measure = factor(soil_erosion_measure),
number_of_tillage = factor(number_of_tillage),
year = factor(year)
) %>%
sjlabelled::copy_labels(main_data_0)
## -----------------------------------------------------------------------------------------
pdata <- pdata.frame(main_data, index = c("unique_plot_id", "year")) %>%
sjlabelled::copy_labels(main_data)
# Create a balanced panel by removing individuals with missing periods
# balanced_pdata <- make.pbalanced(main_data, balance.type = "shared.individuals", index = # c("unique_plot_id", "year"))%>%
# mutate(log_maize_yield = log(maize_yield))%>%
# filter(nitrogen_per_hectare > 0)
## -----------------------------------------------------------------------------------------
plt_maize_yield <- ggstatsplot::ggbetweenstats(
data = main_data,
x = year,
y = maize_yield,
type = "parametric",
messages = FALSE,
ylab = "Maize yield (Kilograms per hectare)",
title = "Maize yield by year",
package = "wesanderson",
palette = "Cavalcanti1"
) +
theme(plot.subtitle = element_text(size = 8),
plot.caption = element_text(size = 8))
plt_nitrogen_per_hectare <- ggstatsplot::ggbetweenstats(
data = main_data,
x = year,
y = nitrogen_per_hectare,
type = "parametric",
messages = FALSE,
ylab = "Nitrogen (Kilograms per hectare )",
title = "Nitrogen per hectare by year",
package = "wesanderson",
palette = "Cavalcanti1"
) +
theme(plot.subtitle = element_text(size = 8),
plot.caption = element_text(size = 8))
# combine the plots into a single plot using patchwork
interest_vars_mean <- plt_maize_yield + plt_nitrogen_per_hectare
interest_vars_mean
ggsave(
here::here("reports", "interest_vars_mean.png"),
interest_vars_mean,
width = 12,
height = 6,
units = "in",
dpi = 600
)
plt_maize_yield_median <- ggstatsplot::ggbetweenstats(
data = main_data,
x = year,
y = maize_yield,
type = "np",
messages = FALSE,
ylab = "Maize yield (Kilograms per hectare)",
title = "Maize yield by year",
package = "wesanderson",
palette = "Cavalcanti1",
summary.fun = "median"
) +
theme(plot.subtitle = element_text(size = 8),
plot.caption = element_text(size = 8))
plt_nitrogen_per_hectare_median <- ggstatsplot::ggbetweenstats(
data = main_data,
x = year,
y = nitrogen_per_hectare,
type = "np",
messages = FALSE,
ylab = "Nitrogen (Kilograms per hectare )",
title = "Nitrogen per hectare by year",
package = "wesanderson",
palette = "Cavalcanti1",
summary.fun = "median"
) +
theme(plot.subtitle = element_text(size = 8),
plot.caption = element_text(size = 8))
interest_vars_median <- plt_maize_yield_median + plt_nitrogen_per_hectare_median
interest_vars_median
ggsave(
here::here("reports", "interest_vars_median.png"),
interest_vars_median,
width = 10,
height = 5,
units = "in",
dpi = 600
)
## -----------------------------------------------------------------------------------------
rainfall_data_database <- read_csv("../utilities/rainfall_data_database.csv") %>%
select(-Introduction) %>%
separate(Link, into = c("link_1", "link_2"), sep = "\\]") %>%
mutate(link_1 = str_remove_all(link_1, "\\[")) %>%
separate(Reference,
into = c("reference_1", "reference_2"),
sep = "\\]") %>%
mutate(reference_1 = str_remove_all(reference_1, "\\[")) %>%
mutate(Reference = paste0("\\href{", reference_2, "}{", reference_1, "}")) %>%
mutate(Link = paste0("\\href{", link_2, "}{", link_1, "}")) %>%
select(-link_1, -link_2, -reference_1, -reference_2)
rain_db_tbl <- rainfall_data_database %>%
kableExtra::kbl(
linesep = "",
escape = T,
booktabs = TRUE,
longtable = TRUE,
caption = "Pooled OLS-Model-2",
align = "lllllllllllllll",
format = "pipe"
) %>%
kableExtra::kable_styling(latex_options = c("repeat_header", "striped"),
font_size = 5) %>%
landscape()
rain_db_tbl <- gsub("\\\\textbackslash\\{\\}href", "\\\\href", rain_db_tbl)
rain_db_tbl <- gsub("\\\\\\{", "\\{", rain_db_tbl)
rain_db_tbl <- gsub("\\\\\\}", "\\}", rain_db_tbl)
## -----------------------------------------------------------------------------------------
knitr::include_graphics(here::here("reports", "correlation_matrix.png"))
## -----------------------------------------------------------------------------------------
summary_table <- gtsummary::tbl_summary(
main_data,
include = -c(unique_plot_id, ea_id, household_id, wave),
by = year,
type = list(
twi = "continuous",
no_other_crop = "dichotomous",
one_other_crop = "dichotomous",
two_other_crops = "dichotomous",
three_or_more_other_crops = "dichotomous",
legume = "dichotomous",
document_for_plot = "dichotomous",
soil_erosion_measure = "dichotomous",
under_extension = "dichotomous",
irrigation = "dichotomous",
agro_chemical_use = "dichotomous",
mechanization = "dichotomous",
farmer_purchased_seed = "dichotomous",
male_plot_manager = "dichotomous",
animal_traction = "dichotomous",
organic_fertilizer = "dichotomous",
number_of_tillage = "categorical"
),
value = list(
no_other_crop ~ 1,
one_other_crop ~ 1,
two_other_crops ~ 1,
three_or_more_other_crops ~ 1,
legume ~ 1,
document_for_plot ~ 1,
soil_erosion_measure ~ 1,
under_extension ~ 1,
irrigation ~ 1,
agro_chemical_use ~ 1,
mechanization ~ 1,
farmer_purchased_seed ~ 1,
male_plot_manager ~ 1,
animal_traction ~ 1,
organic_fertilizer ~ 1
),
statistic = list(
all_continuous() ~ "{mean} ({sd})",
all_categorical() ~ "{n}({p}%)",
all_dichotomous() ~ "{n}({p}%)"
),
missing = "no"
) %>%
add_overall() %>%
add_difference(all_continuous() ~ "smd") %>%
modify_column_hide(columns = c(p.value, ci)) %>%
modify_table_body( ~ .x %>% select(-p.value, -std.error)) %>%
modify_footnote(estimate = NA) %>%
add_p(test.args = all_tests("t.test") ~ list(simulate.p.value = T)) %>%
add_significance_stars(thresholds = c(0.01, 0.05, 0.10)) %>%
modify_header(all_stat_cols() ~ "N = {n}") %>%
as_kable_extra(
format = "latex",
booktabs = TRUE,
longtable = TRUE,
caption = "Descriptive statistics for key study variables",
align = "lrrrrr"
) %>%
add_header_above(c(
" " = 1,
"Pooled" = 1,
"2018" = 1,
"2021" = 1
)) %>%
kable_styling(
latex_options = c("scale_down", "repeat_header", "hold_position", "striped"),
font_size = 8,
stripe_color = "gray!3"
) %>%
row_spec(63, hline_after = TRUE)
summary_table <- gsub("midrule\\*", "midrule", summary_table)
summary_table
## -----------------------------------------------------------------------------------------
# Define the rainfall columns and region variable
rainfall_columns <- pdata %>%
select(ends_with("rainfall")) %>%
names()
region_var <- "saq01"
# Define the list of all independent variables excluding the dependent variable and the rainfall columns
model_0 <- c("year")
model_1 <- c(
"plot_area",
"seeding_rate",
"household_ae",
"male_plot_manager",
"age_plot_manager",
"organic_fertilizer",
"farmer_purchased_seed",
"irrigation",
"under_extension",
"agro_chemical_use",
"mechanization",
"twi",
"no_other_crop",
"one_other_crop",
"two_other_crops",
"legume",
"document_for_plot",
"soil_erosion_measure",
"number_of_tillage",
"year"
)
# fit lasso for variable selection
X <- makeX(pdata[, model_1], n = nrow(pdata), p = length(model_1))
Y <- pdata$maize_yield
lasso_model <- glmnet::cv.glmnet(
x = X,
y = Y,
alpha = 1,
nfolds = 20
)
plot(lasso_model)
lasso_fit <- glmnet::glmnet(
x = X,
y = Y,
alpha = 01,
lambda = lasso_model$lambda.min
)
lasso_coef <- coef(lasso_fit, s = "lambda.min")
model_2 <- rownames(lasso_coef)[which(lasso_coef != 0)]
model_4 <- model_2 <- c(
"plot_area",
"seeding_rate",
"household_ae",
"male_plot_manager",
"age_plot_manager",
"organic_fertilizer",
"farmer_purchased_seed",
"irrigation",
"under_extension",
"agro_chemical_use",
"mechanization",
"twi",
"no_other_crop",
"one_other_crop",
"two_other_crops",
"legume",
"document_for_plot",
"soil_erosion_measure",
"number_of_tillage",
"year"
)
## -----------------------------------------------------------------------------------------
# Create empty lists to store the results
regression_results_pooled <- list()
regression_results_fe <- list()
regression_results_felm <- list()
# Loop over each rainfall column
# remove na
cleaned_data <- na.omit(main_data)
write_dta(cleaned_data,
here::here("data", "cleaned_data", "cleaned_data_stata.dta"))
for (rainfall_col in rainfall_columns) {
#rainfall_col <- "chirps_rainfall"
# Loop over each independent variable set (model_0, model_1, model_2)
for (model_name in c("model_0", "model_1", "model_2", "model_4")) {
# model_name <- "model_1"
# Get the variables associated with the model_name
independent_var <- get(model_name)
# Construct the formula dynamically by including the current independent variables
formula_1 <- as.formula(
paste(
"maize_yield ~ nitrogen_per_hectare + I(nitrogen_per_hectare^2) +",
paste(independent_var, collapse = " + "),
"+",
rainfall_col
)
)
formula_2 <- as.formula(
paste(
"maize_yield ~ nitrogen_per_hectare + I(nitrogen_per_hectare^2) +",
paste(independent_var, collapse = " + "),
"+",
rainfall_col,
"|",
"factor(unique_plot_id)"
)
)
formula_3 <- as.formula(
paste(
"maize_yield ~ nitrogen_per_hectare + I(nitrogen_per_hectare^2) +",
paste(independent_var, collapse = " + ")
)
)
if (model_name == "model_4") {
formula_1 <- formula_2 <- formula_3
}
### Pooled OLS model with robust standard errors
ols_model <- lm(formula_1, data = cleaned_data)
# Compute robust standard errors
ols_robust_se <- broom::tidy(ols_model, se.type = "robust")
# Get confidence intervals for OLS model
ols_ci <- confint(ols_model, level = 0.95)
# Get marginal effects
ols_marginal_effects <- margins::margins(ols_model)
### Fixed Effects model with robust standard errors
fe_model <- plm(formula_1, data = pdata, model = "within")
# Compute robust standard errors for the FE model
fe_robust_se <- broom::tidy(fe_model, se.type = "robust")
# Get confidence intervals for Fixed Effects model
fe_ci <- confint(fe_model, level = 0.95)
# Get marginal effects
#beta_1 <- fe_model$coefficients["nitrogen_per_hectare"]
#beta_2 <- fe_model$coefficients["I(nitrogen_per_hectare^2)"]
#
#pdata2 <- pdata
#pdata2$marginal_effect <- beta_1 + 2 * beta_2 * pdata2$nitrogen_per_hectare
#
#fe_marginal_effects <- mean(pdata2$marginal_effect)
felm_model <- felm(formula_2, data = pdata)
felm_robust_se <- broom::tidy(felm_model, se.type = "robust")
felm_ci <- confint(felm_model, level = 0.95)
#beta_1 <- as.data.frame(t(felm_model$coefficients))["nitrogen_per_hectare"][,1]#
#beta_2 <- as.data.frame(t(felm_model$coefficients))["I(nitrogen_per_hectare^2)"]#[,1]
#
#pdata2$flm_marginal_effect <- beta_1 + 2 * beta_2 * pdat#a2$nitrogen_per_hectare
#felm_marginal_effects <- mean(pdata2$flm_marginal_effect)
# Ensure the list is initialized before assignment
if (is.null(regression_results_pooled[[rainfall_col]])) {
regression_results_pooled[[rainfall_col]] <- list()
}
if (is.null(regression_results_fe[[rainfall_col]])) {
regression_results_fe[[rainfall_col]] <- list()
}
if (is.null(regression_results_felm[[rainfall_col]])) {
regression_results_felm[[rainfall_col]] <- list()
}
# Store the results using the model name (e.g., model_0, model_1, model_2)
regression_results_pooled[[rainfall_col]][[model_name]] <- list(
summary = ols_model,
confidence_intervals = ols_ci,
se = ols_robust_se,
marginal_effects = ols_marginal_effects
)
regression_results_fe[[rainfall_col]][[model_name]] <- list(summary = fe_model,
confidence_intervals = fe_ci,
se = fe_robust_se)
regression_results_felm[[rainfall_col]][[model_name]] <- list(summary = felm_model,
confidence_intervals = felm_ci,
se = felm_robust_se)
}
}
## -----------------------------------------------------------------------------------------
# Collect the nitrogen coefficients, standard errors, p-values, and confidence intervals in a single data frame
results_df_0 <- data.frame()
get_significance_stars <- function(p_value) {
if (p_value < 0.001) {
return("***")
} else if (p_value < 0.01) {
return("**")
} else if (p_value < 0.05) {
return("*")
} else if (p_value < 0.1) {
return(".")
} else {
return("")
}
}
for (rainfall_col in rainfall_columns) {
# rainfall_col <- "chirps_rainfall"
for (model_name in c("model_0", "model_1", "model_2", "model_4")) {
# model_name <- "model_1"
pooled_result <- regression_results_pooled[[rainfall_col]][[model_name]]
print(rainfall_col)
marginal_effects <- summary(pooled_result$marginal_effects) %>%
filter(factor == "nitrogen_per_hectare")
nitrogen_row <- pooled_result$se %>%
filter(term == "nitrogen_per_hectare")
if (nrow(nitrogen_row) > 0) {
nitrogen_coef <- nitrogen_row$estimate
nitrogen_se <- nitrogen_row$std.error
nitrogen_p <- nitrogen_row$p.value
nitrogen_ci_lower <- pooled_result$confidence_intervals["nitrogen_per_hectare", "2.5 %"]
nitrogen_ci_upper <- pooled_result$confidence_intervals["nitrogen_per_hectare", "97.5 %"]
me <- marginal_effects$AME
me_se <- marginal_effects$SE
me_p <- marginal_effects$p
me_ci_lower <- marginal_effects$lower
me_ci_upper <- marginal_effects$upper
significance <- get_significance_stars(nitrogen_p)
row <- data.frame(
rainfall_column = rainfall_col,
model = model_name,
model_type = "pooled",
coefficient = nitrogen_coef,
standard_error = nitrogen_se,
lower_bound_ci = nitrogen_ci_lower,
upper_bound_ci = nitrogen_ci_upper,
significance = significance,
marginal_effects = me,
marginal_effect_se = me_se,
marginal_effect_lower = me_ci_lower,
marginal_effect_upper = me_ci_upper,
marginal_effect_significance = get_significance_stars(me_p)
)
results_df_0 <- rbind(results_df_0, row)
}
fe_result <- regression_results_felm[[rainfall_col]][[model_name]]
nitrogen_row <- fe_result$se %>%
filter(term == "nitrogen_per_hectare")
if (nrow(nitrogen_row) > 0) {
nitrogen_coef <- nitrogen_row$estimate
nitrogen_se <- nitrogen_row$std.error
nitrogen_p <- nitrogen_row$p.value
nitrogen_ci_lower <- fe_result$confidence_intervals["nitrogen_per_hectare", "2.5 %"]
nitrogen_ci_upper <- fe_result$confidence_intervals["nitrogen_per_hectare", "97.5 %"]
significance <- get_significance_stars(nitrogen_p)
row <- data.frame(
rainfall_column = rainfall_col,
model = model_name,
model_type = "fixed",
coefficient = nitrogen_coef,
standard_error = nitrogen_se,
lower_bound_ci = nitrogen_ci_lower,
upper_bound_ci = nitrogen_ci_upper,
significance = significance,
marginal_effects = NA,
marginal_effect_se = NA,
marginal_effect_lower = NA,
marginal_effect_upper = NA,
marginal_effect_significance = NA
)
results_df_0 <- rbind(results_df_0, row)
}
}
}
results_df <- results_df_0 %>%
mutate(
rainfall_column_2 = case_when(
rainfall_column == "chirps_rainfall" ~ "CHIRPS v2.0",
rainfall_column == "cmap_rainfall" ~ "CMAP Standard",
rainfall_column == "cmorph_rainfall" ~ "CMORPH",
rainfall_column == "cpc_rainfall" ~ "CPC-Global",
rainfall_column == "cru_ts_rainfall" ~ "CRU TS v4.06",
rainfall_column == "era5_rainfall" ~ "ERA5",
rainfall_column == "fldas_rainfall" ~ "FLDAS",
rainfall_column == "gpcp_rainfall" ~ "GPCP v2.3",
rainfall_column == "jra55_rainfall" ~ "JRA-55",
rainfall_column == "merra2_rainfall" ~ "MERRA-2",
rainfall_column == "mswep_rainfall" ~ "MSWEP v2.8",
rainfall_column == "ncep_doe_rainfall" ~ "NCEP/DOE R2",
rainfall_column == "persiann_rainfall" ~ "PERSIANN-CDR",
rainfall_column == "precl_rainfall" ~ "PREC/L",
rainfall_column == "terraclimate_rainfall" ~ "TerraClimate"
)
) %>%
mutate(
model_2 = case_when(
model == "model_0" ~ "Maize_yield = Nitrogen + Nitrogen^2 + Year + rainfall",
model == "model_1" ~ "Maize_yield = Nitrogen + Nitrogen^2 + Year + Controls + rainfall",
model == "model_4" ~ "Maize_yield = Nitrogen + Nitrogen^2 + Controls"
)
)
## -----------------------------------------------------------------------------------------
# plot marginal effects
me <- results_df %>% filter(model != "model_2" &
model_type == "pooled") %>%
filter(model != "model_4") %>%
mutate(
model_label = case_when(
model == "model_0" ~ "[A] With-controls",
model == "model_1" ~ "[B] Without-controls"
)
)
latex_font <- "Frutiger"
my_font <- "Frutiger"
facet_labels_pooled <- c("model_0" = "Y[ijt] == beta[0] + beta[1]*N[ijt] + beta[2]*N[ijt]^2 + beta[r]*R[ijt] + epsilon[ijt]", "model_1" = "Y[ijt] == beta[0] + beta[1]*N[ijt] + beta[2]*N[ijt]^2 + sum(beta[k]*X[kijt], k==3, K) + beta[r]*R[ijt] + epsilon[ijt]")
facet_labels_pooled_2 <- c("model_0" = "[A] With-controls", "model_1" = "[B] Without-controls")
plot_me <- ggplot(me, aes(x = rainfall_column_2, y = marginal_effects)) +
geom_point(aes(color = rainfall_column_2),
size = 3,
shape = 15) +
geom_errorbar(
aes(ymin = marginal_effect_lower, ymax = marginal_effect_upper),
width = 0.2,
color = "black"
) +
geom_text(
aes(label = paste0(round(marginal_effects, 1), significance)),
vjust = -0.2,
hjust = -0.5,
angle = 0,
size = 5,
family = my_font
) +
#geom_hline(yintercept = mean(results_pooled$coefficient), linetype = "dashed", color = "#09447D") +
labs(title = "", x = "Data set", y = "APE") +
coord_flip() +
scale_color_manual(values = c(GrandBudapest1, Cavalcanti1, Darjeeling1, Moonrise1)) +
ggthemes::theme_pander(base_family = my_font, base_size = 16) +
theme(
axis.text.x = element_text(angle = 0, hjust = 1),
legend.position = "none",
panel.grid.minor = element_blank(),
strip.text = element_text(
family = latex_font,
size = 14,
face = "bold"
)
) +
facet_wrap( ~ model_label, ncol = 2)
plot_me
ggsave(
here::here("reports", "plot_me.png"),
plot_me,
width = 6,
height = 8,
units = "in",
dpi = 400
)
## -----------------------------------------------------------------------------------------
# Create separate data frames for pooled and fixed effects models
results_pooled <- results_df %>% filter(model_type == "pooled" &
model != "model_2")
results_fixed <- results_df %>% filter(model_type == "fixed" &
model != "model_2")
facet_labels_pooled <- c("model_0" = "Y[ijt] == beta[0] + beta[1]*N[ijt] + beta[2]*N[ijt]^2 + beta[r]*R[ijt] + epsilon[ijt]", "model_1" = "Y[ijt] == beta[0] + beta[1]*N[ijt] + beta[2]*N[ijt]^2 + sum(beta[k]*X[kijt], k==3, K) + beta[r]*R[ijt] + epsilon[ijt]")
facet_labels_fixed <- c("model_0" = "Y[ijt] == beta[0] + beta[1]*N[ijt] + beta[2]*N[ijt]^2 + beta[r]*R[ijt] + alpha[i] + epsilon[ijt]", "model_1" = "Y[ijt] == beta[0] + beta[1]*N[ijt] + beta[2]*N[ijt]^2 + sum(beta[k]*X[kijt], k==3, K) + beta[r]*R[ijt] + alpha[i] + epsilon[ijt]")
latex_font <- "Frutiger"
my_font <- "Frutiger"
plot_pooled <- ggplot(results_pooled, aes(x = rainfall_column_2, y = coefficient)) +
geom_point(aes(color = rainfall_column_2),
size = 3,
shape = 15) +
geom_errorbar(
aes(ymin = lower_bound_ci, ymax = upper_bound_ci),
width = 0.2,
color = "black"
) +
geom_text(
aes(label = paste0(round(coefficient, 2), significance)),
vjust = -1,
angle = 90,
size = 4,
family = my_font
) +
#geom_hline(yintercept = mean(results_pooled$coefficient), linetype = "dashed", color = "#09447D") +
labs(title = "Pooled OLS Model", x = "Data set", y = "Coefficient") +
scale_color_manual(values = c(GrandBudapest1, Cavalcanti1, Darjeeling1, Moonrise1)) +
ggthemes::theme_pander(base_family = my_font, base_size = 12) +
theme(
axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = "none",
panel.grid.minor = element_blank(),
strip.text = element_text(
family = latex_font,
size = 14,
face = "bold"
)
) +
facet_wrap( ~ model,
ncol = 1,
labeller = as_labeller(facet_labels_pooled, label_parsed))
plot_pooled
# Plot for fixed effects models (faceted by model)
plot_fixed <- ggplot(results_fixed, aes(x = rainfall_column_2, y = coefficient)) +
geom_point(size = 4,
aes(color = rainfall_column_2),
shape = 15) +
geom_errorbar(
aes(ymin = lower_bound_ci, ymax = upper_bound_ci),
width = 0.2,
color = "black"
) +
geom_text(
aes(label = paste0(round(coefficient, 2), significance)),
vjust = -1,
angle = 90,
size = 4,
family = my_font
) +
#geom_hline(yintercept = mean(results_fixed$coefficient), linetype = "dashed", color = "#09447D") +
labs(title = "Fixed Effects Model", x = "Data sets", y = "Coefficient") +
scale_color_manual(values = c(GrandBudapest1, Cavalcanti1, Darjeeling1, Moonrise1)) +
ggthemes::theme_pander(base_family = my_font, base_size = 12) +
theme(
axis.text.x = element_text(angle = 90, hjust = 1),
legend.position = "none",
panel.grid.minor = element_blank()
) +
facet_wrap( ~ model,
ncol = 1,
labeller = as_labeller(facet_labels_fixed, label_parsed))
# Display the plots
ggsave(
here::here("reports", "plot_pooled.png"),
plot_pooled,
width = 10,
height = 5,
units = "in",
dpi = 600
)
plot_fixed
ggsave(
here::here("reports", "plot_fixed.png"),
plot_fixed,
width = 10,
height = 5,
units = "in",
dpi = 600
)
## -----------------------------------------------------------------------------------------
# from the list filter model_0 and sumary
regression_results_pooled_0 <- lapply(regression_results_pooled, function(x)
x$model_4$summary)
names(regression_results_pooled_0) <- c(
"Pooled OLS",
"CMAP Standard",
"CMORPH",
"CPC-Global",
"CRU TS v4.06",
"ERA5",
"FLDAS",
"GPCP v2.3",
"JRA-55",
"MERRA-2",
"MSWEP v2.8",
"NCEP/DOE R2",
"PERSIANN-CDR",
"PREC/L",
"TerraClimate"
)
regression_results_fe_0 <- lapply(regression_results_fe, function(x)
x$model_4$summary)
names(regression_results_fe_0) <- c(
"FE",
"CMAP Standard",
"CMORPH",
"CPC-Global",
"CRU TS v4.06",
"ERA5",
"FLDAS",
"GPCP v2.3",
"JRA-55",
"MERRA-2",
"MSWEP v2.8",
"NCEP/DOE R2",
"PERSIANN-CDR",
"PREC/L",
"TerraClimate"
)
## -----------------------------------------------------------------------------------------
ols_model_0_0 <- modelsummary::msummary(
c(regression_results_pooled_0[1], regression_results_fe_0[1]),
stars = TRUE,
statistic = "({std.error})",
ci = TRUE,
digits = 3,
vcov = "robust",
coef_omit = '(Intercept)',
coef_rename = c(
"nitrogen_per_hectare" = "Nitrogen (Kg/ha)",
"I(nitrogen_per_hectare^2)" = "Nitrogen^2 (Kg/ha)",
"chirps_rainfall" = "CHIRPS v2.0",
"cmap_rainfall" = "CMAP Standard",
"cmorph_rainfall" = "CMORPH",
"cpc_rainfall" = "CPC-Global",
"cru_ts_rainfall" = "CRU TS v4.06",
"era5_rainfall" = "ERA5",
"fldas_rainfall" = "FLDAS",
"gpcp_rainfall" = "GPCP v2.3",
"jra55_rainfall" = "JRA-55",
"merra2_rainfall" = "MERRA-2",
"mswep_rainfall" = "MSWEP v2.8",
'ncep_doe_rainfall' = "NCEP/DOE R2",
"persiann_rainfall" = "PERSIANN-CDR",
"precl_rainfall" = "PREC/L",
"terraclimate_rainfall" = "TerraClimate",
"plot_area" = "Plot area (ha)",
"seeding_rate" = "Seeding rate (Kg/ha)",
"household_ae" = "Household AE",
"male_plot_manager1" = "Male plot manager",
"age_plot_manager" = "Age of plot manager",
"organic_fertilizer1" = "Organic fertilizer",
"farmer_purchased_seed1" = "Farmer purchased seed",
"irrigation1" = "Irrigation",
"under_extension1" = "Under extension",
"agro_chemical_use1" = "Agro chemical use",
"mechanization1" = "Mechanization (1/0)",
"twi" = "Topographic wetness index",
"no_other_crop1" = "No other crop",
"one_other_crop1" = "One other crop",
"two_other_crops1" = "Two other crops",
"legume1" = "Cultivated Legumes",
"document_for_plot1" = "Document for plot=1",
"soil_erosion_measure1" = "Soil erosion measure=1",
"number_of_tillageOnce" = "Number of tillage (Once)",
"number_of_tillageTwice" = "Number of tillage (Twice)",
"year2021" = "Year=2021"
)
)