a function to model curves using smoothing splines anova using gss library

this function models each curve of genotype using smoothing splines anova

fitGSS(datain, trait, loopId)

Arguments

datain

input dataframe
trait

character, trait of interest to model (example biovolume, PH ...)
loopId

a column name that contains ident of Genotype-Scenario

Value

a list containing 2 objects

a list of each output of ssanova

a dataframe of kullback-Leibler projection

Details

the input dataframe must contain the following columns: the trait to model, the ident of Genotype-Scenario, thermalTime, repetition columns

Each time course is modelled by a nonparametric smoothing spline. This is a piecewise cubic polynomial (Eubank, 1999). Then a functional ANOVA decomposition (Gu, 2014) of all the fitted splines for each genotype by environmental treatment combination is realised, by taking into account the replicate effect and a temporal functional effect. The smoothing spline fitting and the functional ANOVA decompositions are be performed with the gss R package.

See also

project.ssanova, ssanova

Examples

# \donttest{
data(plant1)
selec<-c("Lo1199_H","Lo1124_H","Lo1038_H","A3_H")
mydata<-plant1[plant1[,"genotypeAlias"] %in% selec,]
 fm1<-fitGSS(datain=mydata,trait="biovolume",loopId="genotypeAlias")
# }