Nothing
context("fit")
test_that("rfhfit is working", {
library("saeSim")
set.seed(2)
dat <- base_id(100, 1) %>%
sim_gen_e() %>%
sim_gen_x() %>%
sim_gen_v() %>%
sim_resp_eq(y = 100 + 2 * x + v + e) %>%
as.data.frame()
y <- dat$y
X <- cbind(1, dat$x)
samplingVar <- rep(16, nrow(dat))
out <- saeRobust:::fitrfh(y, X, samplingVar)
expect_is(out, "list")
expect_is(out$coefficients, "numeric")
expect_is(out$variance, "numeric")
testthat::expect_equal(
sum(score(out)$delta, score(out)$beta),
0,
tolerance = 1e-04
)
})
test_that("fitrsfh is working", {
library("saeSim")
set.seed(2)
nDomains <- 40
dat <- base_id(nDomains, 1) %>%
sim_gen_e() %>%
sim_gen_x() %>%
sim_gen(gen_v_sar(rho = 0.5, sd = 4, name = "v")) %>%
sim_resp_eq(y = 100 + 2 * x + v + e) %>%
as.data.frame()
y <- dat$y
X <- cbind(1, dat$x)
samplingVar <- rep(16, nrow(dat))
W <- spdep::nb2mat(spdep::cell2nb(nDomains, 1, "rook"), style = "W")
out <- saeRobust:::fitrsfh(
y, X, samplingVar, W,
x0Var = c(0.5, 16),
maxIter = 100, maxIterParam = 1, maxIterRe = 1 # speed up
)
# lapply(out$iterations, nrow)
# out$variance
# out$iterations$correlation
# score(out)$delta
# Atlas - OpenBLAS - and standard BLAS all have different results. Maybe we
# should use the mean?
# testthat::expect_equal(
# out$variance[2], 15.28,
# tolerance = 1e-03, check.attributes = FALSE
# )
#
# testthat::expect_equal(
# out$variance[1], 0.742,
# tolerance = 1e-03, check.attributes = FALSE
# )
expect_is(out, "list")
expect_is(out$coefficients, "numeric")
expect_is(out$variance, "numeric")
testthat::expect_equal(sum(score(out)$delta), 0, tolerance = 1.5e-04)
})
test_that("fitrtfh", {
library("saeSim")
set.seed(3)
nDomains <- 20
nTime <- 10
dat <- base_id_temporal(nDomains, 1, nTime) %>%
sim_gen_e(sd = 10) %>%
sim_gen_x() %>%
sim_gen_v(sd = 10) %>%
sim_gen(gen_v_ar1(rho = 0.5, sd = 10, name = "ar")) %>%
sim_resp_eq(y = 100 + 2 * x + v + ar + e) %>%
as.data.frame()
y <- dat$y
x <- cbind(1, dat$x)
samplingVar <- rep(100, nrow(dat))
out <- saeRobust:::fitrtfh(
y, x, samplingVar,
nTime = nTime, x0Var = c(0.5, 100, 100),
maxIter = 100, maxIterParam = 20, maxIterRe = 1 # speed up
)
# lapply(out$iterations, nrow)
# out$variance
# out$iterations$correlation
# out$iterations$variance
testthat::expect_equal(sum(score(out)$delta), 0, tolerance = 1e-05)
})
test_that("fitrtfh", {
library("saeSim")
set.seed(3)
nDomains <- 20
nTime <- 10
dat <- base_id_temporal(nDomains, 1, nTime) %>%
sim_gen_e(sd = 4) %>%
sim_gen_x() %>%
sim_gen(gen_v_sar(sd = 4, name = "v")) %>%
sim_gen(gen_v_ar1(rho = 0.5, sd = 4, name = "ar")) %>%
sim_resp_eq(y = 100 + 2 * x + v + ar + e) %>%
as.data.frame()
y <- dat$y
x <- cbind(1, dat$x)
samplingVar <- rep(16, nrow(dat))
W <- testRook(nDomains)
out <- saeRobust:::fitrstfh(
y, x, samplingVar,
nTime = nTime, W, x0Var = c(0.3, 0.5, 16, 16),
maxIter = 100, maxIterParam = 5, maxIterRe = 1 # speed up
)
# lapply(out$iterations, nrow)
# out$variance
# out$iterations$SAR
# out$iterations$AR
# out$iterations$variance
# score(out)$delta
testthat::expect_equal(sum(score(out)$delta), 0, tolerance = 1e-04)
})
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