320 lines
10 KiB
R
320 lines
10 KiB
R
options(scipen = 999)
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library(dplyr)
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library(car)
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library(lmtest)
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library(stargazer)
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library(sandwich)
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library(lmtest)
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setwd("C:/Users/irina/Documents/DND/EOHI/eohi1")
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df <- read.csv("ehi1.csv")
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data <- df %>%
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select(eohiDGEN_mean, ehi_global_mean, demo_sex, demo_age_1) %>%
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filter(demo_sex != "Prefer not to say")
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str(data)
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colSums(is.na(data))
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sapply(data, class)
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# Create dummy variable for sex (0 = Male, 1 = Female)
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data$sex_dummy <- ifelse(data$demo_sex == "Female", 1, 0)
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# Verify the dummy coding
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print(table(data$demo_sex, data$sex_dummy))
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#descriptives
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# Descriptives for age
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print(summary(data$demo_age_1))
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print(sd(data$demo_age_1, na.rm = TRUE))
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# Center demo_age_1 (subtract the mean)
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data$age_centered <- data$demo_age_1 - mean(data$demo_age_1, na.rm = TRUE)
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# Verify the centering
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print(summary(data$age_centered))
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# Descriptives for sex (frequency table)
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print(table(data$demo_sex))
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print(prop.table(table(data$demo_sex)))
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# Descriptives for sex dummy variable
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print(table(data$sex_dummy))
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#### REGRESSION MODELS ####
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# MODEL 1: Age only - EOHI
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age_DGEN <- lm(eohiDGEN_mean ~ age_centered, data = data)
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par(mfrow = c(2, 2))
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plot(age_DGEN)
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print(shapiro.test(residuals(age_DGEN)))
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print(summary(age_DGEN))
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print(AIC(age_DGEN))
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# MODEL 1: Age only - EHI
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age_domain <- lm(ehi_global_mean ~ age_centered, data = data)
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par(mfrow = c(2, 2))
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plot(age_domain)
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print(shapiro.test(residuals(age_domain)))
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print(summary(age_domain))
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print(AIC(age_domain))
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# MODEL 2: Sex only - EOHI
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sex_DGEN <- lm(eohiDGEN_mean ~ sex_dummy, data = data)
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par(mfrow = c(2, 2))
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plot(sex_DGEN)
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print(shapiro.test(residuals(sex_DGEN)))
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print(summary(sex_DGEN))
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print(AIC(sex_DGEN))
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# P1 (res vs fitted) + P3 (scale location): test for homoscedasticity. relatively flat red line = homoscedasticity. relatively scattered points = homoscedasticity. this assumption is met.
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# P2 (qq plot): test for normality. points scattered around a relatively straight line = normality. this assumption is violated but large sample is robust.
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# P4 (residuals vs leverage): test for outliers. high leverage points = outliers. leverage > 2p/n.
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# p = parameters; for this model p = 2 (intercept + sex_dummy). n = 1061 (removed prefer not to say). threshold = 2*2/1061 = 0.00377. maximum leverage in plot is ~ 0.002 therefore no points have concerning leverage.
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# across the plots, there are 3 outliers: 258, 670, 872. this represents 0.28% of the data (much less than the acceptable threshold of 5%). therefore, analysis can proceed.
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# MODEL 2: Sex only - EHI
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sex_domain <- lm(ehi_global_mean ~ sex_dummy, data = data)
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par(mfrow = c(2, 2))
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plot(sex_domain)
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print(shapiro.test(residuals(sex_domain)))
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print(summary(sex_domain))
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print(AIC(sex_domain))
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# MODEL 3: Age + Sex + Interaction - EOHI
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interaction_DGEN <- lm(eohiDGEN_mean ~ age_centered + sex_dummy + age_centered:sex_dummy, data = data)
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par(mfrow = c(2, 2))
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plot(interaction_DGEN)
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print(shapiro.test(residuals(interaction_DGEN)))
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vif_DGEN <- vif(interaction_DGEN)
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print(vif_DGEN)
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print(summary(interaction_DGEN))
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print(AIC(interaction_DGEN))
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# MODEL 3: Age + Sex + Interaction - EHI
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interaction_domain <- lm(ehi_global_mean ~ age_centered + sex_dummy + age_centered:sex_dummy, data = data)
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par(mfrow = c(2, 2))
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plot(interaction_domain)
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print(shapiro.test(residuals(interaction_domain)))
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vif_domain <- vif(interaction_domain)
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print(vif_domain)
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print(summary(interaction_domain))
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print(AIC(interaction_domain))
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#### troubleshooting ####
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# Clear any existing plots
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# dev.off()
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#### PLOTS ####
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# Create visual figures for age models
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library(ggplot2)
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# Figure 1: age_DGEN model
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p1 <- ggplot(data, aes(x = age_centered, y = eohiDGEN_mean)) +
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geom_point(alpha = 0.6, color = "steelblue") +
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geom_smooth(method = "lm", se = TRUE, color = "red", linewidth = 1) +
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labs(
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title = "Age and EOHI-DGEN Relationship",
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x = "Age (centered)",
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y = "EOHI-DGEN Mean",
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subtitle = paste("R² =", round(summary(age_DGEN)$r.squared, 3),
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", p < 0.001")
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) +
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theme_minimal() +
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theme(
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plot.title = element_text(size = 14, face = "bold"),
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axis.title = element_text(size = 12),
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plot.subtitle = element_text(size = 10, color = "gray60")
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)
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# Figure 2: age_domain model
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p2 <- ggplot(data, aes(x = age_centered, y = ehi_global_mean)) +
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geom_point(alpha = 0.6, color = "darkgreen") +
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geom_smooth(method = "lm", se = TRUE, color = "red", linewidth = 1) +
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labs(
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title = "Age and EHI Domain Relationship",
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x = "Age (centered)",
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y = "EHI Domain Mean",
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subtitle = paste("R² =", round(summary(age_domain)$r.squared, 3),
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", p < 0.001")
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) +
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theme_minimal() +
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theme(
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plot.title = element_text(size = 14, face = "bold"),
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axis.title = element_text(size = 12),
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plot.subtitle = element_text(size = 10, color = "gray60")
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)
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# Save the plots
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ggsave("age_DGEN_plot.png", p1, width = 8, height = 6, dpi = 300)
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ggsave("age_domain_plot.png", p2, width = 8, height = 6, dpi = 300)
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# Display the plots
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print(p1)
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print(p2)
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#### HTML file ####
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# Create comprehensive HTML report grouped by model
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library(htmltools)
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# Start HTML document
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html_content <- htmltools::div(
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htmltools::h1("Regression Analysis: Age and Sex Effects on EOHI-DGEN and EHI Domain"),
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# EOHI-DGEN Models Section
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htmltools::h2("EOHI-DGEN Models"),
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# Age Model
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htmltools::h3("1. Age Model (age_DGEN)"),
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htmltools::div(
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style = "margin-bottom: 30px;",
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htmltools::h4("Model Results, AIC, and VIF"),
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htmltools::HTML(
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stargazer(
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age_DGEN,
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type = "html",
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title = "Age Model: EOHI-DGEN",
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dep.var.labels = "EOHI-DGEN Mean",
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covariate.labels = c("Age (centered)"),
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report = "vcsp*",
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add.lines = list(
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c("AIC", round(AIC(age_DGEN), 2)),
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c("Max VIF", "N/A")
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)
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)
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),
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htmltools::h4("Assumption Diagnostic Plots"),
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htmltools::img(src = "age_DGEN_assumptions.png", style = "width: 100%; max-width: 800px;"),
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htmltools::h4("Model Visualization"),
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htmltools::img(src = "age_DGEN_plot.png", style = "width: 100%; max-width: 800px;")
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),
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# Sex Model
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htmltools::h3("2. Sex Model (sex_DGEN)"),
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htmltools::div(
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style = "margin-bottom: 30px;",
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htmltools::h4("Model Results, AIC, and VIF"),
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htmltools::HTML(
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stargazer(
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sex_DGEN,
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type = "html",
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title = "Sex Model: EOHI-DGEN",
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dep.var.labels = "EOHI-DGEN Mean",
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covariate.labels = c("Sex (dummy)"),
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report = "vcsp*",
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add.lines = list(
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c("AIC", round(AIC(sex_DGEN), 2)),
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c("Max VIF", "N/A")
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)
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)
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),
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htmltools::h4("Assumption Diagnostic Plots"),
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htmltools::img(src = "sex_DGEN_assumptions.png", style = "width: 100%; max-width: 800px;")
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),
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# Interaction Model
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htmltools::h3("3. Interaction Model (interaction_DGEN)"),
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htmltools::div(
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style = "margin-bottom: 30px;",
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htmltools::h4("Model Results, AIC, and VIF"),
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htmltools::HTML(
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stargazer(
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interaction_DGEN,
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type = "html",
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title = "Interaction Model: EOHI-DGEN",
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dep.var.labels = "EOHI-DGEN Mean",
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covariate.labels = c("Age (centered)", "Sex (dummy)", "Age x Sex"),
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report = "vcsp*",
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add.lines = list(
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c("AIC", round(AIC(interaction_DGEN), 2)),
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c("Max VIF", max_vif_DGEN)
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)
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)
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),
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htmltools::h4("Assumption Diagnostic Plots"),
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htmltools::img(src = "interaction_DGEN_assumptions.png", style = "width: 100%; max-width: 800px;")
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),
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# EHI Domain Models Section
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htmltools::h2("EHI Domain Models"),
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# Age Model
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htmltools::h3("1. Age Model (age_domain)"),
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htmltools::div(
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style = "margin-bottom: 30px;",
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htmltools::h4("Model Results, AIC, and VIF"),
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htmltools::HTML(
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stargazer(
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age_domain,
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type = "html",
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title = "Age Model: EHI Domain",
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dep.var.labels = "EHI Domain Mean",
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covariate.labels = c("Age (centered)"),
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report = "vcsp*",
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add.lines = list(
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c("AIC", round(AIC(age_domain), 2)),
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c("Max VIF", "N/A")
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)
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)
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),
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htmltools::h4("Assumption Diagnostic Plots"),
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htmltools::img(src = "age_domain_assumptions.png", style = "width: 100%; max-width: 800px;"),
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htmltools::h4("Model Visualization"),
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htmltools::img(src = "age_domain_plot.png", style = "width: 100%; max-width: 800px;")
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),
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# Sex Model
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htmltools::h3("2. Sex Model (sex_domain)"),
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htmltools::div(
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style = "margin-bottom: 30px;",
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htmltools::h4("Model Results, AIC, and VIF"),
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htmltools::HTML(
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stargazer(
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sex_domain,
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type = "html",
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title = "Sex Model: EHI Domain",
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dep.var.labels = "EHI Domain Mean",
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covariate.labels = c("Sex (dummy)"),
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report = "vcsp*",
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add.lines = list(
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c("AIC", round(AIC(sex_domain), 2)),
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c("Max VIF", "N/A")
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)
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)
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),
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htmltools::h4("Assumption Diagnostic Plots"),
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htmltools::img(src = "sex_domain_assumptions.png", style = "width: 100%; max-width: 800px;")
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),
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# Interaction Model
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htmltools::h3("3. Interaction Model (interaction_domain)"),
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htmltools::div(
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style = "margin-bottom: 30px;",
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htmltools::h4("Model Results, AIC, and VIF"),
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htmltools::HTML(
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stargazer(
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interaction_domain,
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type = "html",
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title = "Interaction Model: EHI Domain",
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dep.var.labels = "EHI Domain Mean",
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covariate.labels = c("Age (centered)", "Sex (dummy)", "Age x Sex"),
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report = "vcsp*",
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add.lines = list(
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c("AIC", round(AIC(interaction_domain), 2)),
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c("Max VIF", max_vif_domain)
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)
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)
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),
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htmltools::h4("Assumption Diagnostic Plots"),
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htmltools::img(src = "interaction_domain_assumptions.png", style = "width: 100%; max-width: 800px;")
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)
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)
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# Save HTML content
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htmltools::save_html(html_content, "regression_analysis_report.html")
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print("HTML report created: regression_analysis_report.html") |