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# Be careful that one need to change the columns name again after cbine several
# matrix
test_that("testif FDA_regr_fgam generate same prediction with refund::pfr", {
# errors on R 4.1
skip_if(sessionInfo()$R.version$status == "Under development (unstable)")
requirePackagesOrSkip("refund")
data(DTI)
dti1 = DTI[DTI$visit == 1 & complete.cases(DTI), ]
# subset a portion of the data(complete.cases select non missing value rows)
# dti1 is already a matrix dataframe
fit.af = pfr(
formula = pasat ~ af(cca, Qtransform = TRUE, k = 7, m = 2),
data = dti1)
prd.refund = predict(fit.af, newdata = dti1, type = "response")
# makeFunctionalData require plain dataframe
df = data.frame(as.list(dti1[, c("pasat", "cca")]))
fdf = makeFunctionalData(df, fd.features = list("cca" = 2:94))
lrn = makeLearner("regr.fgam",
Qtransform = TRUE, mgcv.te_ti.k = 7,
mgcv.te_ti.m = 2)
task = makeRegrTask(data = fdf, target = "pasat")
mod1f = train(learner = lrn, task = task)
prd.mlr = predict(object = mod1f, newdata = fdf)
res = all.equal(as.numeric(prd.refund), prd.mlr$data$response)
expect_true(res)
})
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