tests/next/test_mixudt.R

In Fuzzy-Logix/AdapteR: Converts R syntax to SQL to provide fast analytics using in-database C++ library DB Lytix.

# rm(list = setdiff(ls(),"connection"))
## implement as.R for FLSimpleVector.
## using 1 Random Effects.
FLenv <- new.env(parent = globalenv())
fltbl  <- FLTable("tblMixedModel", "ObsID")
Renv <- as.R(FLenv)
rtbl <- as.R(fltbl)

## use in 
FLenv$mod <- mixUDT(yVal ~ FixVal + (1 | RanVal), data = fltbl)
Renv$mod <- lmer(yVal ~ FixVal + (1 | RanVal), data = rtbl)

## AIC, Log-Likehhood
test_that("AIC, LogLik:", {expect_equal(FLenv$mod$logLik,6970.309,tolerance = .001 )
    expect_equal(FLenv$mod$AIC,6974.309,tolerance = .001 )  })

## Covar Random
test_that("", {
    expect_equal(FLenv$mod$CovRandom,260.573310,tolerance = .001 )
})




## for 2 Random Effect:
# FLenv <- new.env(parent = globalenv())
fltbl  <- FLTable("tblMixedModelInt", "ObsID")
# Renv <- as.R(FLenv)
rtbl <- as.R(fltbl)

FLenv$mod <- mixUDT(yVal ~ FixVal + (1 |   RanVal1) + (1 | RanVal2 ), fltbl)

## AIC, Log-Likehhood
test_that("AIC, LogLik:", {expect_equal(FLenv$mod$logLik,38.6181,tolerance = .001 )
    expect_equal(FLenv$mod$AIC,42.6181,tolerance = .001 )  })

## Covar Random
test_that("", {
    expect_equal(FLenv$mod$CovRandom,c(0.01482593 ,2.38859471),tolerance = .001 )
})