gotway.hessianfly {agridat} | R Documentation |
Hessian fly damage to wheat varieties
block
block factor, 4 levels
genotype factor, 16 wheat varieties
lat
latitude, numeric
long
longitude, numeric
y
number of damaged plants
n
number of total plants
The response is binomial.
Each plot was square.
C. A. Gotway and W. W. Stroup. A Generalized Linear Model Approach to Spatial Data Analysis and Prediction Journal of Agricultural, Biological, and Environmental Statistics, 2, 157-178.
http://doi.org/10.2307/1400401
The GLIMMIX procedure. http://www.ats.ucla.edu/stat/SAS/glimmix.pdf
## FIXME Could the spaMM package be useful here...???? data(gotway.hessianfly) dat <- gotway.hessianfly dat$prop <- dat$y / dat$n if(require(desplot)){ desplot(prop~long*lat, dat, aspect=1, # true aspect out1=block, text=gen, cex=1, shorten='no', main="gotway.hessianfly") } # ---------------------------------------------------------------------------- # Block random. See Glimmix manual, output 1.18. # Note: (Different parameterization) ## Not run: ## require(lme4) ## l2 <- glmer(cbind(y, n-y) ~ gen + (1|block), data=dat, family=binomial, ## control=glmerControl(check.nlev.gtr.1="ignore")) ## coef(l2) ## End(Not run) # ---------------------------------------------------------------------------- ## Not run: # asreml3 require(asreml) a2 <- asreml(prop ~ gen, data=dat, random=~block, family=asreml.binomial(), weights=n) coef(a2) ## End(Not run) # ---------------------------------------------------------------------------- ## Not run: ## require(asreml4) ## a2 <- asreml(prop ~ gen, data=dat, random=~block, ## family=asr_binomial(), ## weights=n) ## coef(a2) ## End(Not run)