projectII

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library(shiny)

## Warning: package 'shiny' was built under R version 4.4.3

library(car)

## Warning: package 'car' was built under R version 4.4.3

## Loading required package: carData

## Warning: package 'carData' was built under R version 4.4.3

#1
df <- read.csv("health_indicators_omn.csv", skip = 1) # by pass the header column. 
df <- na.omit(df)
num_variables <- names(df)[sapply(df, is.numeric)]

#set user interface:
ui <- fluidPage(
  #set the app title:
  titlePanel("Health Indicators Regression"),

  sidebarLayout(
    sidebarPanel(
      # menu for selecting the response variable:
      selectInput("response", "Response Variable:", choices = num_variables),
      # check  box group to  select predictor variable :
      checkboxGroupInput("predictors", "Predictor(s):", choices = num_variables)
    ),
    #set the menu panel:
    mainPanel(
      verbatimTextOutput("summary"),
      tableOutput("vif"),
      plotOutput("residualPlot"),
      plotOutput("qqPlot")
)))


#set the server function:
server <- function(input, output) {
  model <- reactive({
    # reactive to fit the lin model based on  the user input:
    req(input$response, input$predictors)
    formula <- as.formula(paste(input$response, "~", paste(in_put$predictors, collapse = "+")))
    lm(formula, data = df )
  })

  # output the model summary:
  output$summary <- renderPrint({ summary(model()) })
  output$vif <- renderTable({ as.data.frame(vif(model())) })
  # output the vif (variance inflation factor): 
  output$residualPlot <- renderPlot({
    plot(model(), which = 1)  # Residuals vs Fitted
  })
  # plot for residuals vs fitted values:
  output$qqPlot <- renderPlot({
    plot(model(), which = 2)  
  })
}

shinyApp(ui = ui, server = server)

Shiny applications not supported in static R Markdown documents

#Interpretation:

#Coefficients: The model coefficients represent the effect of each predictor on 
#the response variable. Significant predictors (p < 0.05) are significant in 
#explaining the response.

#VIF (Variance Inflation Factor): High VIF values (greater than 5 or 10) indicate
#multicollinearity among predictors, which suggests potential instability in the
#model's coefficient estimates.

#Diagnostic Plots:
#Residuals vs Fitted Plot: Check for non-linearity and homoscedasticity. Random 
#scatter suggests a good fit, while patterns suggest issues.

#Q-Q Plot: Checks normality of residuals. Non-normality occurs where the line 
#is not followed.

#Model Fit:
#R-squared: Checks proportion of variance in response captured by the model. 
#Large values show a good fit.

#Assumptions: Model makes assumptions of linearity, homoscedasticity, and 
#normality of residuals. Violation means going back to the model or 
#transformations.

#Conclusion: Validity of the model depends on significance of predictors, 
#VIF values, and check of assumptions

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