Nothing
# ======================================================================
# inequality constraints are evaluated two at a time
# below a check that this works correctly for even and uneven numbers
# ======================================================================
estimate <- c(0,0,0,0,0)
names(estimate)<-c("a", "b", "c","d", "e")
sampN<- 100
cov <- matrix(c(1,.0,.0,0,0,
.0,1,.0,0,0,
.0,.0,1,0,0,
0,0,0,1,0,
0,0,0,0,1),nrow=5,ncol=5)
set.seed(7)
y<-bain(estimate,"a>0 & b>0 & c>0 & d>0 & e>0",n=sampN,Sigma=cov,
group_parameters=0,joint_parameters = 5)
test_that("bain default", {expect_equal(y$fit$Fit_in[1], .0312, tolerance = .001)})
estimate <- c(0,0,0,0)
names(estimate)<-c("a", "b", "c","d")
sampN<- 100
cov <- matrix(c(1,.0,.0,0,
.0,1,.0,0,
.0,.0,1,0,
0,0,0,1),nrow=4,ncol=4)
set.seed(7)
y<-bain(estimate,"a>0 & b>0 & c>0 & d>0",n=sampN,Sigma=cov,
group_parameters=0,joint_parameters = 4)
test_that("bain default", {expect_equal(y$fit$Fit_in[1], .0625)})
estimate <- c(0,0,0)
names(estimate)<-c("a", "b", "c")
sampN<- 100
cov <- matrix(c(1,.0,0,
.0,1,0,
0,0,1),nrow=3,ncol=3)
set.seed(7)
y<-bain(estimate,"a>0 & b>0 & c>0",n=sampN,Sigma=cov,group_parameters=0,
joint_parameters = 3)
test_that("bain default", {expect_equal(y$fit$Fit_in[1], .125)})
estimate <- c(0,0)
names(estimate)<-c("a", "b")
sampN<- 100
cov <- matrix(c(1,.0,
.0,1),nrow=2,ncol=2)
set.seed(7)
y<-bain(estimate,"a>0 & b>0",n=sampN,Sigma=cov,group_parameters=0,
joint_parameters = 2)
test_that("bain default", {expect_equal(y$fit$Fit_in[1], .25)})
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