# ANCOVA VIA LM OBJECT
data(sesamesim)
sesamesim$site <- as.factor(sesamesim$site)
ancov <- lm(postnumb ~ site + prenumb + peabody -1, data = sesamesim)


set.seed(100)
y<-bain(ancov, "site1=site2=site3=site4=site5;site2 > site5 > site3 > site1 >site4;")

# ANCOVA VIA BAIN_DEFAULT

sesamesim$prenumb <- sesamesim$prenumb-mean(sesamesim$prenumb)
sesamesim$peabody <- sesamesim$peabody-mean(sesamesim$peabody)

ancov <- lm(postnumb ~ site + prenumb + peabody -1, data = sesamesim)
est <- coef(ancov)
samp <- table(sesamesim$site)
prep.var <- (summary(ancov)$sigma)**2

cat1 <- subset(cbind(sesamesim$site,sesamesim$prenumb,sesamesim$peabody), sesamesim$site == 1)
cat1[,1] <- 1
cat1 <- as.matrix(cat1)
cov1 <- prep.var * solve(t(cat1) %*% cat1)

cat2 <- subset(cbind(sesamesim$site,sesamesim$prenumb,sesamesim$peabody), sesamesim$site == 2)
cat2[,1] <- 1
cat2 <- as.matrix(cat2)
cov2 <- prep.var * solve(t(cat2) %*% cat2)

cat3 <- subset(cbind(sesamesim$site,sesamesim$prenumb,sesamesim$peabody), sesamesim$site == 3)
cat3[,1] <- 1
cat3 <- as.matrix(cat3)
cov3 <- prep.var * solve(t(cat3) %*% cat3)

cat4 <- subset(cbind(sesamesim$site,sesamesim$prenumb,sesamesim$peabody), sesamesim$site == 4)
cat4[,1] <- 1
cat4 <- as.matrix(cat4)
cov4 <- prep.var * solve(t(cat4) %*% cat4)

cat5 <- subset(cbind(sesamesim$site,sesamesim$prenumb,sesamesim$peabody), sesamesim$site == 5)
cat5[,1] <- 1
cat5 <- as.matrix(cat5)
cov5 <- prep.var * solve(t(cat5) %*% cat5)

covariances <- list(cov1, cov2, cov3, cov4,cov5)

names(est)<- c("v.1", "v.2", "v.3", "v.4","v.5", "pre", "pea")
set.seed(100)
z<-bain(est,"v.1=v.2=v.3=v.4=v.5;v.2 > v.5 > v.3 > v.1 >v.4;",n=samp,Sigma=covariances,group_parameters=1,joint_parameters = 2)

# TESTING BAIN LM AND DEFAULT VERSUS EACH OTHER

test_that("Bain mutual", {expect_equal(y$fit$Fit , z$fit$Fit)})
test_that("Bain mutual", {expect_equal(y$fit$Com , z$fit$Com)})
test_that("Bain mutual", {expect_equal(y$independent_restrictions, z$independent_restrictions)})
test_that("Bain mutual", {expect_equal(y$b, z$b)})
test_that("Bain mutual", {expect_equal(as.vector(y$posterior), as.vector(z$posterior))})
test_that("Bain mutual", {expect_equal(as.vector(y$prior), as.vector(z$prior))})
test_that("Bain mutual", {expect_equal(y$fit$BF,z$fit$BF)})
test_that("Bain mutual", {expect_equal(y$fit$PMPb , z$fit$PMPb)})
test_that("Bain mutual", {expect_equal(as.vector(t(y$BFmatrix)), as.vector(t(z$BFmatrix)))})



# TESTING ANCOVA WITH RESTRICTIONS ON THE COVARIATES



sesamesim$sex <- as.factor(sesamesim$sex)
ancov <- lm(postnumb ~ sex + prenumb + peabody -1, data = sesamesim)
set.seed(100)
z<-bain(ancov, " sex1 = sex2 & pre > 0 &  pea > 0")

sesamesim$prenumb <- sesamesim$prenumb-mean(sesamesim$prenumb)
sesamesim$peabody <- sesamesim$peabody-mean(sesamesim$peabody)

est <- coef(ancov)
names(est) <- c("a", "b","pre", "pea")
samp <- table(sesamesim$sex)
prep.var <- (summary(ancov)$sigma)**2

cat1 <- subset(cbind(sesamesim$sex,sesamesim$prenumb,sesamesim$peabody), sesamesim$sex == 1)
cat1[,1] <- 1
cat1 <- as.matrix(cat1)
cov1 <- prep.var * solve(t(cat1) %*% cat1)

cat2 <- subset(cbind(sesamesim$sex,sesamesim$prenumb,sesamesim$peabody), sesamesim$sex == 2)
cat2[,1] <- 1
cat2 <- as.matrix(cat2)
cov2 <- prep.var * solve(t(cat2) %*% cat2)

covariances <- list(cov1, cov2)

set.seed(100)
y<-bain(est,"a = b & pre>0 & pea>0",n=samp,Sigma=covariances,group_parameters=1,joint_parameters = 2)

# TESTING BAIN LM AND DEFAULT VERSUS EACH OTHER

test_that("Bain mutual", {expect_equal(y$fit$Fit , z$fit$Fit, tolerance = .001)})
test_that("Bain mutual", {expect_equal(y$fit$Com , z$fit$Com, tolerance = .001)})
test_that("Bain mutual", {expect_equal(y$independent_restrictions, z$independent_restrictions)})
test_that("Bain mutual", {expect_equal(y$b, z$b)})
test_that("Bain mutual", {expect_equal(as.vector(y$posterior), as.vector(z$posterior))})
test_that("Bain mutual", {expect_equal(as.vector(y$prior), as.vector(z$prior))})
test_that("Bain mutual", {expect_equal(y$fit$PMPb , z$fit$PMPb, tolerance =.001)})
test_that("Bain mutual", {expect_equal(as.vector(t(y$BFmatrix)), as.vector(t(z$BFmatrix)))})

# TESTING ANCOVA WITH LM OBJECT WITH INTERCEPTS UNEQUAL TO ZERO AND TWO COVARIATES


df <- sesamesim
df$site <- as.factor(df$site)
model <- lm(postnumb~site+prenumb+peabody-1, df)
set.seed(100)
y <- bain(model, "site1 = 19.35 & site2 = 29.33;site1>19.35&site2>29.33")
test_that("Bain mutual", {expect_equal(y$fit$Com[1], 0.0007, tolerance = .0005)})


# TESTING ANCOVA WITH BAIN DEFAULT WITH INTERCEPTS UNEQUAL TO ZERO AND TWO COVARIATES
sesamesim$prenumb <- sesamesim$prenumb-mean(sesamesim$prenumb)
sesamesim$peabody <- sesamesim$peabody-mean(sesamesim$peabody)

ancov <- lm(postnumb ~ site + prenumb + peabody -1, data = sesamesim)
est <- coef(ancov)
samp <- table(sesamesim$site)
prep.var <- (summary(ancov)$sigma)**2

cat1 <- subset(cbind(sesamesim$site,sesamesim$prenumb,sesamesim$peabody), sesamesim$site == 1)
cat1[,1] <- 1
cat1 <- as.matrix(cat1)
cov1 <- prep.var * solve(t(cat1) %*% cat1)

cat2 <- subset(cbind(sesamesim$site,sesamesim$prenumb,sesamesim$peabody), sesamesim$site == 2)
cat2[,1] <- 1
cat2 <- as.matrix(cat2)
cov2 <- prep.var * solve(t(cat2) %*% cat2)

cat3 <- subset(cbind(sesamesim$site,sesamesim$prenumb,sesamesim$peabody), sesamesim$site == 3)
cat3[,1] <- 1
cat3 <- as.matrix(cat3)
cov3 <- prep.var * solve(t(cat3) %*% cat3)

cat4 <- subset(cbind(sesamesim$site,sesamesim$prenumb,sesamesim$peabody), sesamesim$site == 4)
cat4[,1] <- 1
cat4 <- as.matrix(cat4)
cov4 <- prep.var * solve(t(cat4) %*% cat4)

cat5 <- subset(cbind(sesamesim$site,sesamesim$prenumb,sesamesim$peabody), sesamesim$site == 5)
cat5[,1] <- 1
cat5 <- as.matrix(cat5)
cov5 <- prep.var * solve(t(cat5) %*% cat5)

covariances <- list(cov1, cov2, cov3, cov4,cov5)
names(est)<- c("v.1", "v.2", "v.3", "v.4","v.5", "pre", "pea")
hyp<-"v.1=19.35 & v.2 = 29.33; v.1>19.35 & v.2 > 29.33;"

set.seed(100)
z<-bain(est,hyp,n=samp,Sigma=covariances,group_parameters=1,joint_parameters = 2)

# TESTING BAIN LM AND DEFAULT VERSUS EACH OTHER

test_that("Bain mutual", {expect_equal(y$fit$Fit , z$fit$Fit, tolerance = .001)})
test_that("Bain mutual", {expect_equal(y$fit$Com , z$fit$Com, tolerance = .001)})
test_that("Bain mutual", {expect_equal(y$independent_restrictions, z$independent_restrictions)})
test_that("Bain mutual", {expect_equal(y$b, z$b)})
test_that("Bain mutual", {expect_equal(as.vector(y$posterior), as.vector(z$posterior))})
test_that("Bain mutual", {expect_equal(as.vector(y$prior), as.vector(z$prior))})
test_that("Bain mutual", {expect_equal(y$fit$BF,z$fit$BF)})
test_that("Bain mutual", {expect_equal(y$fit$PMPb , z$fit$PMPb)})
test_that("Bain mutual", {expect_equal(as.vector(t(y$BFmatrix)), as.vector(t(z$BFmatrix)))})

# ===============================================================================================


# TESTING ANCOVA WITH LM OBJECT WITH INTERCEPTS UNEQUAL TO ZERO AND ONE COVARIATE



sesamesim$site <- as.factor(sesamesim$site)
ancov <- lm(postnumb ~ site + prenumb -1, data = sesamesim)
#hyp<-"v.1=19.35 & v.2 = 29.33; v.1>19.35 & v.2 > 29.33;"
set.seed(100)

y <- bain(ancov, "site1=19.35 & site2 = 29.33; site1>19.35 & site2 > 29.33")

sesamesim$prenumb <- sesamesim$prenumb-mean(sesamesim$prenumb)

ancov <- lm(postnumb ~ site + prenumb  -1, data = sesamesim)
est <- coef(ancov)
samp <- table(sesamesim$site)
prep.var <- (summary(ancov)$sigma)**2

cat1 <- subset(cbind(sesamesim$site,sesamesim$prenumb), sesamesim$site == 1)
cat1[,1] <- 1
cat1 <- as.matrix(cat1)
cov1 <- prep.var * solve(t(cat1) %*% cat1)

cat2 <- subset(cbind(sesamesim$site,sesamesim$prenumb), sesamesim$site == 2)
cat2[,1] <- 1
cat2 <- as.matrix(cat2)
cov2 <- prep.var * solve(t(cat2) %*% cat2)

cat3 <- subset(cbind(sesamesim$site,sesamesim$prenumb), sesamesim$site == 3)
cat3[,1] <- 1
cat3 <- as.matrix(cat3)
cov3 <- prep.var * solve(t(cat3) %*% cat3)

cat4 <- subset(cbind(sesamesim$site,sesamesim$prenumb), sesamesim$site == 4)
cat4[,1] <- 1
cat4 <- as.matrix(cat4)
cov4 <- prep.var * solve(t(cat4) %*% cat4)

cat5 <- subset(cbind(sesamesim$site,sesamesim$prenumb), sesamesim$site == 5)
cat5[,1] <- 1
cat5 <- as.matrix(cat5)
cov5 <- prep.var * solve(t(cat5) %*% cat5)

covariances <- list(cov1, cov2, cov3, cov4,cov5)
names(est)<- c("v.1", "v.2", "v.3", "v.4","v.5", "pre")
hyp<-"v.1=19.35 & v.2 = 29.33; v.1>19.35 & v.2 > 29.33;"

set.seed(100)
z<-bain(est,hyp,n=samp,Sigma=covariances,group_parameters=1,joint_parameters = 1)

# TESTING BAIN LM AND DEFAULT VERSUS EACH OTHER

test_that("Bain mutual", {expect_equal(y$fit$Fit , z$fit$Fit)})
test_that("Bain mutual", {expect_equal(y$fit$Com , z$fit$Com)})
test_that("Bain mutual", {expect_equal(y$independent_restrictions, z$independent_restrictions)})
test_that("Bain mutual", {expect_equal(y$b, z$b)})
test_that("Bain mutual", {expect_equal(as.vector(y$posterior), as.vector(z$posterior))})
test_that("Bain mutual", {expect_equal(as.vector(y$prior), as.vector(z$prior))})
test_that("Bain mutual", {expect_equal(y$fit$BF,z$fit$BF)})
test_that("Bain mutual", {expect_equal(y$fit$PMPb , z$fit$PMPb)})
test_that("Bain mutual", {expect_equal(as.vector(t(y$BFmatrix)), as.vector(t(z$BFmatrix)))})


# testing that the order of input of group and covariates does not matter


sesamesim$site <- as.factor(sesamesim$site)
ancov <- lm(postnumb ~ site + prenumb + peabody -1, data = sesamesim)
set.seed(100)
y<-bain(ancov, "site1=site2=site3=site4=site5;site2 > site5 > site3 > site1 >site4;")

ancov2 <- lm(postnumb ~ prenumb + peabody +site -1, data = sesamesim)
set.seed(100)
y2<-bain(ancov2, "site1=site2=site3=site4=site5;site2 > site5 > site3 > site1 >site4;")

ancov3 <- lm(postnumb ~ prenumb + site + peabody -1, data = sesamesim)
set.seed(100)
y3<-bain(ancov3, "site1=site2=site3=site4=site5;site2 > site5 > site3 > site1 >site4;")
test_that("Bain mutual", {expect_equal(y$estimates , y2$estimates)})
test_that("Bain mutual", {expect_equal(y$estimates , y3$estimates)})
test_that("Bain mutual", {expect_equal(y$fit$PMPb , y2$fit$PMPb)})
test_that("Bain mutual", {expect_equal(y$fit$PMPb , y3$fit$PMPb)})