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. https://doi.org/10.2478/bile-2014-0006

Examples

# \dontrun{

# 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

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

dat <- droplevels(subset(dat, origin=="Ravenshoe"))
libs(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
#>    L2    L3    L4    L5    L6    L7 
#> 25.00 18.67 20.61 13.88 19.84 14.72 

# }