• Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny Book Club
  • Welcome
    • Book club meetings
    • Pace
  • 1 Geospatial health
    • 1.1 Why?
      • 1.1.1 About the book
    • 1.2 How?
      • 1.2.1 Check/use R4ds Slack
      • 1.2.2 Using Git and Github
      • 1.2.3 Following the flow
    • 1.3 Geospatial health
      • 1.3.1 Disease mapping
      • 1.3.2 Communication of results
    • 1.4 Resources
    • 1.5 Meeting Videos
      • 1.5.1 Cohort 1
  • 2 Spatial data and R packages for mapping
    • 2.1 Spatial data and R packages for mapping
      • 2.1.1 Types of spatial data
    • 2.2 Coordinate reference systems
      • 2.2.1 Geographic coordinate systems
      • 2.2.2 Projected coordinate systems
      • 2.2.3 Setting Coordinate Reference Systems in R
    • 2.3 Shapefiles
    • 2.4 Making maps with R
      • 2.4.1 ggplot2
      • 2.4.2 leaflet
      • 2.4.3 mapview
      • 2.4.4 tmap
    • 2.5 Meeting Videos
      • 2.5.1 Cohort 1
  • 3 Bayesian inference and INLA
    • 3.1 Bayesian inference
      • 3.1.1 A small analogy with R: garden of forking data
    • 3.2 Integrated nested Laplace approximation
      • 3.2.1 Resources:
    • 3.3 Meeting Videos
      • 3.3.1 Cohort 1
  • 4 The R-INLA package
    • 4.1 Linear predictor (LP)
    • 4.2 Inla()
    • 4.3 Priors specification
    • 4.4 Example: mortality rates following surgery
      • 4.4.1 Model
      • 4.4.2 resuts!
    • 4.5 Control variables to compute approximations
    • 4.6 Meeting Videos
      • 4.6.1 Cohort 1
  • 5 Areal data
    • 5.1 Introduction
    • 5.2 Spatial neighborhood matrices
    • 5.3 Standardized incidence ratio
    • 5.4 Spatial small area disease risk estimation
      • 5.4.1 Spatial modeling of lung cancer in Pennsylvania
    • 5.5 Spatio-temporal small area disease risk estimation
    • 5.6 Conclusions
    • 5.7 Meeting Videos
      • 5.7.1 Cohort 1
  • 6 Spatial modeling of areal data. Lip cancer in Scotland
    • 6.1 Data set: wrangling
      • 6.1.1 Map of Scotland counties
      • 6.1.2 Data
    • 6.2 Mapping SIRs
    • 6.3 Modeling
      • 6.3.1 Our model:
      • 6.3.2 neighborhood matrix
      • 6.3.3 Using INLA
    • 6.4 Mapping relative risks
    • 6.5 Exceedance probabilities
    • 6.6 Meeting Videos
      • 6.6.1 Cohort 1
  • 7 Spatio-temporal modeling of areal data. Lung cancer in Ohio
    • 7.1 Data
    • 7.2 Expected cases
    • 7.3 SIRs
    • 7.4 Mapping
      • 7.4.1 Time plots of SIRs
    • 7.5 Modeling
      • 7.5.1 Neighborhood matrix
      • 7.5.2 Inference using INLA
    • 7.6 Mapping relative risks
    • 7.7 Meeting Videos
      • 7.7.1 Cohort 1
  • 8 Geostatistical data
    • 8.1 Geostatistical data
      • 8.1.1 Gaussian Random Fields (GRF)
      • 8.1.2 Stationarity
      • 8.1.3 Usefull covariance functions
    • 8.2 Stochastic partial differntial equation approach
    • 8.3 Spatial modeling of rainfall in Paraná, Brazil
      • 8.3.1 Data
      • 8.3.2 Model
    • 8.4 Meeting Videos
      • 8.4.1 Cohort 1
  • 9 Spatial modeling of geostatistical data. Malaria in The Gambia
    • 9.1 Data & data preparation
      • 9.1.1 data
      • 9.1.2 Prevalence
      • 9.1.3 Transforming coordinates
      • 9.1.4 Mapping prevalence
      • 9.1.5 Environmental covariates
    • 9.2 Modeling
      • 9.2.1 Mesh construction
      • 9.2.2 Building the SPDE model on the mesh
      • 9.2.3 Index set
      • 9.2.4 Projection matrix
      • 9.2.5 Prediction data
      • 9.2.6 Stack with data for estimation and prediction
      • 9.2.7 Model formula
    • 9.3 Mapping malaria prevalence
    • 9.4 Mapping exceedance probabilities
    • 9.5 Meeting Videos
      • 9.5.1 Cohort 1
  • 10 Spatio-temporal modeling of geostatistical data. Air pollution in Spain
    • 10.1 Map
      • 10.1.1 Getting Spain
      • 10.1.2 Filter to only get main territory
    • 10.2 Data!
      • 10.2.1 Getting it
      • 10.2.2 Cleaning it
    • 10.3 Modeling!
      • 10.3.1 Mesh construction
      • 10.3.2 SPDE model on the mesh with penalised complexity
      • 10.3.3 Index
      • 10.3.4 projection matrix:
      • 10.3.5 Grid:
      • 10.3.6 Ap + stack
      • 10.3.7 Model formula:
    • 10.4 Mapping
    • 10.5 More on space time model:
    • 10.6 Meeting Videos
      • 10.6.1 Cohort 1
  • 11 Introduction to R Markdown
    • 11.1 Introduction
    • 11.2 Steps in creating an Rmarkdown documents
    • 11.3 Structure of R Markdown
    • 11.4 Text Formating in R Markdown
    • 11.5 R Code Chunks
    • 11.6 Figures
    • 11.7 Tables
    • 11.8 Examples on how to compose a note in R Markdown
    • 11.9 Meeting Videos
      • 11.9.1 Cohort 1
  • 12 Building a dashboard to visualize spatial data with flexdashboard
    • 12.1 The R package flexdashboard
    • 12.2 R Markdown
    • 12.3 Layout
    • 12.4 Dashboard components
    • 12.5 A dashboard to visualize global air pollution
    • 12.6 Data
    • 12.7 Table using DT
    • 12.8 Map using leaflet
    • 12.9 Histogram using ggplot2
    • 12.10 Demonstration on how to build a flexDashboard in R
    • 12.11 Meeting Videos
      • 12.11.1 Cohort 1
  • 13 Introduction to Shiny
    • 13.1 Introduction
    • 13.2 Structure of a Shiny app
    • 13.3 Inputs
    • 13.4 Outputs
    • 13.5 Inputs, outputs and reactivity
    • 13.6 Examples of Shiny apps
    • 13.7 HTML content
    • 13.8 Layouts
    • 13.9 Sharing Shiny apps
    • 13.10 Meeting Videos
      • 13.10.1 Cohort 1
  • 14 Interactive dashboards with flexdashboard and Shiny
    • 14.1 Packages:
      • 14.1.1 We start making a flexdashboard:
    • 14.2 Build an awesome flexdadhboard
      • 14.2.1 Download the data
      • 14.2.2 Build the map
    • 14.3 Add some features with Shiny
      • 14.3.1 Add a table
      • 14.3.2 Make the histogram
    • 14.4 Meeting Videos
      • 14.4.1 Cohort 1
  • 15 Building a Shiny app to upload and visualize spatio-temporal data
    • 15.1 SLIDE 1
    • 15.2 Meeting Videos
      • 15.2.1 Cohort 1
  • 16 Disease surveillance with SpatialEpiApp
    • 16.1 SLIDE 1
    • 16.2 Meeting Videos
      • 16.2.1 Cohort 1
  • Published with bookdown

Geospatial Health Data: Modeling and Visualization with R-INLA and Shiny Book Club

10.5 More on space time model:

Here: https://becarioprecario.bitbucket.io/inla-gitbook/ch-temporal.html