21 Day 21 (April 18)

21.1 Announcements

• In-class work day this Thursday (April 20)

• Please email me to request a 20-30 min time slot between May 1 and May 9 to give your final presentation. When you email me, please give 3 dates/time that work for you.

• Peer review is posted and due May 5

21.2 Data fusion

• See Ch. 25 in BBM2L
• Formative story (math and people working together)
• Many different names
  • Integrated modeling
  • Data reconciliation
  • Data fusion
• Why use Bayesian statistics for data fusion?
  • The probability someone has crafted the model you need (and criticism of my own work)?
  • Use of the hierarchical modeling framework
  • Recursive use of Bayes theorem (see here)
• Example where it is time consuming to get precise data
  • Age vs. height
  • Abundance vs. presence/absence data
  • Disease status of plants (see pgs 407-424 in BBM2L)
  • Percent cover data
• Ad-hoc approaches
  • Simple pooling
  • Transform high quality into low quality data and then pool
    
• Example: Konza percent grass cover
  • Exact cover data

  url <- "https://www.dropbox.com/s/8ohtahx99jox9a5/konza_grass_exact.csv?dl=1"
  df.grass.exact <- read.csv(url)
  head(df.grass.exact)

  ##   percgrass    elev
  ## 1        13 420.860
  ## 2        45 399.306
  ## 3        72 412.792
  ## 4         9 422.606
  ## 5        21 381.152
  ## 6        37 397.789

  plot(df.grass.exact$elev,df.grass.exact$percgrass,xlab="Elevation",ylab="Percent grass")

  
  • Cheap cover data

  url <- "https://www.dropbox.com/s/eef5hy8geyi73ke/konza_grass_cheap.csv?dl=1"
  df.grass.cheap <- read.csv(url)
  head(df.grass.cheap)

  ##   percgrass    elev
  ## 1      <50% 430.380
  ## 2      <50% 430.380
  ## 3      <50% 430.380
  ## 4      <50% 430.380
  ## 5      <50% 425.015
  ## 6      <50% 425.015

  • Model formulation (go over on white board)
  • Model implementation (go over on white board)
  • Live example (Download here)