## here goes all stuff that I am working on and that is
## not yet ready for inclusion in the test suite
## gk @dhruv: please use this for reating a benchmarking for lm
N <- 100
sdResidual <- .1
sdZ <- 1
Renv <- new.env(parent = globalenv())
FLenv <- as.FL(Renv)
## simulation step
Renv$D <- data.frame(x=rbinom(N,1,.5),
z=rnorm(N,0,sdZ),
eps=rnorm(N,0,sdResidual))
## gk: TODO implement R function for creating such a simulated dataset:
FLenv$D <- FLTable(x=rbinom(N,1,.5),
z=rnorm(N,0,1),
eps=rnorm(N,0,sdResidual))
"SELECT a.SerialVal,
FLSimBinomial(a.SerialVal, 0.5, 1) AS x,
FLSimNormal(a.SerialVal, 0.0, ",sdZ,") AS z,
FLSimNormal(a.SerialVal, 0.0, ",sdResidual,") AS eps
FROM fzzlSerial a WHERE a.SerialVal <= ",N," ORDER BY 1;"
test_that("Kmeans returns objects correctly",{
eval_expect_equal({
D$y <- 2+1*D$x+3*D$z + D$eps
N
}, Renv, FLenv,
"simulate outcomes")
eval_expect_equal({
## data mining / recovery
fit <- lm(y ~ x*z, data=D)
}, Renv, FLenv,
"linear model")
})
## next: extend this to 100 columns
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