# Be carefull that one need to change the columns name again after cbine several matrix
context("FDA_regr_fgam")
test_that("FDA_regr_fgam", {
# requirePackagesOrSkip("refund")
# data(DTI)
# DTI1 = DTI[DTI$visit == 1 & complete.cases(DTI),]
# # Fit model with additive functional term for CCA, using tensor product basis
# #fit.af = refund::pfr(formula = pasat ~ af(cca, Qtransform=TRUE, k=c(7,7)), data = DTI1)
# #predict(fit.af, newdata = DTI1, type = 'response')
# #########################################################################
# #FIXME: the current implementation is not gneric
# trafoListMat2df = function(list4mat, target, covariates){
# mdata = as.data.frame(Reduce(cbind, list(DTI1$cca, DTI1$pasat)))
# colnames(mdata)[length(colnames(mdata))] = target
# channel.list = list(cca = 1:dim(DTI1$cca)[2] )
# return(list(mdata = mdata, target = target, channel.list = channel.list ))
# }
# lrn = makeLearner("fdaregr.fgam", mgcv.s.k = -1L )
# mu = trafoListMat2df(list4mat = DTI1, target = "pasat", covariates = c("cca"))
# task = makeFDARegrTask(data = mu$mdata, target = mu$target, fd.features = mu$channel.list)
# mod1f = train(learner = lrn, task = task)
# predict(object = mod1f, newdata = mu$mdata) # input data frame
})
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.