lavoranti.eucalyptus {agridat} | R Documentation

## Height of Eucalyptus trees in southern Brazil

### Description

Height of Eucalyptus trees in southern Brazil

### Format

A data frame with 490 observations on the following 4 variables.

`gen`

  genotype (progeny) factor

`origin`

  origin of progeny

`loc`

  location

`height`

  height, meters

### Details

The genotypes originated from three different locations in Queensland, Australia, and were tested in southern Brazil. The experiment was conducted as a randomized complete block design with 6 plants per plot and 10 blocks. Mean tree height is reported.

The testing locations are described in the following table:

Loc | City | Lat (S) | Long (W) | Altitude | Avg min temp | Avg max temp | Avg temp (C) | Precip (mm)
L1 | Barra Ribeiro, RS | 30.33 | 51.23 | 30 | 9 | 25 | 19 | 1400
L2 | Telemaco Borba, PR | 24.25 | 20.48 | 850 | 11 | 26 | 19 | 1480
L3 | Boa Experanca de Sul, SP | 21.95 | 48.53 | 540 | 15 | 23 | 21 | 1300
L4 | Guanhaes, MG | 18.66 | 43 | 900 | 14 | 24 | 19 | 1600
L5 | Ipatinga, MG | 19.25 | 42.33 | 250 | 15 | 24 | 22 | 1250
L6 | Aracruz, ES | 19.8 | 40.28 | 50 | 15 | 26 | 24 | 1360
L7 | Cacapva, SP | 23.05 | 45.76 | 650 | 14 | 24 | 20 | 1260

Arciniegas-Alarcon (2010) used the 'Ravenshoe' subset of the data to illustrate imputation of missing values.

### Source

O J Lavoranti (2003). Estabilidade e adaptabilidade fenotipica atraves da reamostragem bootstrap no modelo AMMI, PhD thesis, University of Sao Paulo, Brazil.

### References

Arciniegas-Alarcon, S. and Garcia-Pena, M. and dos Santos Dias, C.T. and Krzanowski, W.J. (2010). An alternative methodology for imputing missing data in trials with genotype-by-environment interaction, Biometrical Letters, 47, 1-14. http://doi.org/10.2478/bile-2014-0006

### Examples

    # Arciniegas-Alarcon et al use SVD and regression to estimate missing values.
    # Partition the matrix X as a missing value xm, row vector xr1, column
    # vector xc1, and submatrix X11
    # X = [ xm  xr1 ]
    #     [ xc1 X11 ] and let X11 = UDV'.
    # Estimate the missing value xm = xr1 V D^{-1} U' xc1

    data(lavoranti.eucalyptus)
    dat <- lavoranti.eucalyptus

    require(lattice)
    levelplot(height~loc*gen, dat, main="lavoranti.eucalyptus - GxE heatmap")

    dat <- droplevels(subset(dat, origin=="Ravenshoe"))
    if(require(reshape2)){
    dat <- acast(dat, gen~loc, value.var='height')

    dat[1,1] <- NA
    x11 <- dat[-1,][,-1]
    X11.svd <- svd(x11)
    xc1 <- dat[-1,][,1]
    xr1 <- dat[,-1][1,]
    xm <- xr1
    xm # = 18.29, Original value was 17.4
    }

[Package agridat version 1.16 Index]