battese.survey {agridat} | R Documentation

## Survey and satellite data for corn and soy areas in Iowa

### Description

Survey and satellite data for corn and soy areas in Iowa

### Usage

    data("battese.survey")

### Format

A data frame with 37 observations on the following 9 variables.

`county`

  county name

`segment`

  sample segment number (within county)

`countysegs`

  number of segments in county

`cornhect`

  hectares of corn in segment

`soyhect`

  hectares of soy

`cornpix`

  pixels of corn in segment

`soypix`

  pixels of soy

`cornmean`

  county mean of corn pixels per segment

`soymean`

  county mean of soy pixels per segment

### Details

The data are for 12 counties in north-central Iowa in 1978.

The USDA determined the area of soybeans in 37 area sampling units (called 'segments'). Each segment is about one square mile (about 259 hectares). The number of pixels of that were classified as corn and soybeans came from Landsat images obtained in Aug/Sep 1978. Each pixel represents approximately 0.45 hectares.

Data originally compiled by USDA.

This data is also available in R packages: 'rsae::landsat' and 'JoSAE::landsat'.

### Source

Battese, George E and Harter, Rachel M and Fuller, Wayne A. (1988). An error-components model for prediction of county crop areas using survey and satellite data. emphJournal of the American Statistical Association, 83, 28-36. http://doi.org/10.2307/2288915

Battese (1982) preprint version. http://www.une.edu.au/__data/assets/pdf_file/0017/15542/emetwp15.pdf

### References

Pushpal K Mukhopadhyay and Allen McDowell. (2011). Small Area Estimation for Survey Data Analysis Using SAS Software SAS Global Forum 2011.

### Examples

    data(battese.survey)
    dat <- battese.survey

    # Battese fig 1 & 2.  Corn plot shows outlier in Hardin county
    library("lattice")
    dat <- dat[order(dat$cornpix),]
    xyplot(cornhect ~ cornpix, data=dat, group=county, type=c('p','l'),
           main="battese.survey", xlab="Pixels of corn", ylab="Hectares of corn",
           auto.key=list(columns=3))

    ## Not run:
      dat <- dat[order(dat$soypix),]
      xyplot(soyhect ~ soypix, data=dat, group=county, type=c('p','l'),
             main="battese.survey", xlab="Pixels of soy", ylab="Hectares of soy",
             auto.key=list(columns=3))

    ## End(Not run)

    ## Not run:
      require("lme4")
      require("lucid")

      # Fit the models of Battese 1982, p.18.  Results match
      m1 <- lmer(cornhect ~ 1 + cornpix + (1|county), data=dat)
      fixef(m1)
      ## (Intercept)     cornpix
      ##   5.4661899   0.3878358
      vc(m1)
      ##      grp        var1 var2   vcov  sdcor
      ##   county (Intercept) <NA>  62.83  7.926
      ## Residual        <NA> <NA> 290.4  17.04
      m2 <- lmer(soyhect ~ 1 + soypix + (1|county), data=dat)
      fixef(m2)
      ## (Intercept)      soypix
      ##  -3.8223566   0.4756781
      vc(m2)
      ##      grp        var1 var2  vcov sdcor
      ##   county (Intercept) <NA> 239.2 15.47
      ## Residual        <NA> <NA> 180   13.42

      # Predict for Humboldt county as in Battese 1982 table 2
      5.4662+.3878*290.74
      # 118.2152 # mu_i^0
      5.4662+.3878*290.74+ -2.8744
      # 115.3408 # mu_i^gamma
      (185.35+116.43)/2
      # 150.89 # y_i bar

      # Survey regression estimator of Battese 1988

      # Delete the outlier
      dat2 <- subset(dat, !(county=="Hardin" & soyhect < 30))

      # Results match top-right of Battese 1988, p. 33
      m3 <- lmer(cornhect ~ cornpix + soypix + (1|county), data=dat2)
      fixef(m3)
      ## (Intercept)     cornpix      soypix
      ##  51.0703979   0.3287217  -0.1345684
      vc(m3)
      ##      grp        var1 var2  vcov sdcor
      ##   county (Intercept) <NA> 140   11.83
      ## Residual        <NA> <NA> 147.3 12.14
      m4 <- lmer(soyhect ~ cornpix + soypix + (1|county), data=dat2)
      fixef(m4)
      ##  (Intercept)      cornpix       soypix
      ## -15.59027098   0.02717639   0.49439320
      vc(m4)
      ##      grp        var1 var2  vcov sdcor
      ##   county (Intercept) <NA> 247.5 15.73
      ## Residual        <NA> <NA> 190.5 13.8

    ## End(Not run)

[Package agridat version 1.16 Index]