Nothing

```
context("Tests for CEA")
test_that("Errors for creating initial design in CEA function", {
## Categorical attributes
# Misspecification of coding type
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "B")),
"coding argument is incorrect.")
# Less coding types than attributes
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D")),
"coding argument is incorrect.")
# One attribute only
expect_error(CEA(lvls = c(3), coding = c("D")), "lvls argument is incorrect.")
# Non-numeric number of levels of an attribute
expect_error(CEA(lvls = c(3, 3, "d"), coding = c("D", "D", "D")),
"lvls argument is incorrect.")
# Incorrect type of coding
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "f")),
"coding argument is incorrect.")
## Continuous attributes
# Continuous levels missing
expect_error(CEA(lvls = c(3, 3, 3), coding = c("C", "C", "C")),
"when 'coding' contains C, 'c.lvls' should be specified")
# Less number of continuous levels than attributes
expect_error(CEA(lvls = c(3, 3, 3), coding = c("C", "C", "C"),
c.lvls = list(c(4, 6, 8), c(2, 4,6))),
"length of 'c.lvls' does not match number of specified continuous attributes in 'coding")
# Misspecification in "c.lvls" according to the number of levels in "lvls"
expect_error(CEA(lvls = c(3, 3, 3), coding = c("C", "C", "C"),
c.lvls = list(c(4, 6), c(2, 4, 6), c(5, 6, 7))),
"the number of levels provided in 'c.lvls' does not match the expected based on 'lvls'")
## Design specifications
# More alternative constants than alternatives in a choice set
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.sets = 8,
n.alts = 2, par.draws = c(0, 0, 0, 0, 0, 0),
alt.cte = c(1, 0, 0)),
"'n.alts' does not match the 'alt.cte' vector")
# An alternative constant equals to 2
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.sets = 8,
n.alts = 2, par.draws = c(0, 0, 0, 0, 0, 0),
alt.cte = c(2, 0)),
"'alt.cte' should only contain zero or ones.")
# No boolean value in nochoice parameter
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.sets = 8,
n.alts = 2, alt.cte = c(1, 0),
par.draws = c(0, 0, 0, 0, 0, 0), no.choice = T),
"if 'no.choice' is TRUE, the last alternative constant should equal 1.")
# 1 alternative constant and not a list in the draws
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.sets = 8,
n.alts = 2, alt.cte = c(1, 0),
par.draws = c(0, 0, 0, 0, 0, 0), no.choice = F),
"par.draws should be a list")
# 1 alternative constant and draws in a list, but a single component
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.sets = 8,
n.alts = 2, alt.cte = c(1, 0),
par.draws = list(c(0, 0, 0, 0, 0, 0, 0)), no.choice = F),
"'par.draws' should contain two components")
# 1 alternative constant and draws for the attributes are not in a matrix
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.sets = 8,
n.alts = 2, alt.cte = c(1, 0),
par.draws = list(0, c(0, 0, 0, 0, 0, 0)), no.choice = F),
"'par.draws' should contain two matrices")
# Different number of draws for the alternative constant and betas
# Note: There should be the same number of draws for both components
# All these values are random
mu <- c(0.5, 0.8, 0.2, -0.3, -1.2, 1.6, 2.2)
v <- diag(length(mu)) # Prior variance.
set.seed(123)
pd <- MASS::mvrnorm(n = 2, mu = mu, Sigma = v) # 10 draws.
p.d <- list(matrix(pd[,1], ncol = 2), pd[,2:7])
p.d[[1]] <- p.d[[1]][,-2] # Remove a draw for the constant
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.sets = 8,
n.alts = 2, alt.cte = c(1, 0),
par.draws = p.d, no.choice = F),
"the number of rows in the components of 'par.draws' should be equal")
# 2 alternative constants and not a list in the draws
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.sets = 8,
n.alts = 3, alt.cte = c(1, 1, 0),
par.draws = c(0, 0, 0, 0, 0, 0, 0, 0), no.choice = F),
"par.draws should be a list")
# 2 alternative constants and draws in a list, but a single component
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.sets = 8,
n.alts = 3, alt.cte = c(1, 1, 0),
par.draws = list(c(0, 0, 0, 0, 0, 0, 0, 0)), no.choice = F),
"'par.draws' should contain two components")
# 2 alternative constants and draws for the attributes are not in a matrix
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.sets = 8,
n.alts = 3, alt.cte = c(1, 1, 0),
par.draws = list(c(0, 0), c(0, 0, 0, 0, 0, 0)),
no.choice = F),
"'par.draws' should contain two matrices")
# 2 alternative constants and draws for only one of them
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.sets = 8,
n.alts = 3, alt.cte = c(1, 1, 0),
par.draws = list(as.matrix(c(0)),
as.matrix(c(0, 0, 0, 0, 0, 0))),
no.choice = F),
"the first component of 'par.draws' should contain the same number of columns as there are non zero elements in 'alt.cte'")
# Different number of draws for the alternative constants and betas
# Note: There should be the same number of draws for both components
# All these values are random
mu <- c(0.5, 0.5, 0.8, 0.2, -0.3, -1.2, 1.6, 2.2)
v <- diag(length(mu)) # Prior variance.
set.seed(123)
pd <- MASS::mvrnorm(n = 3, mu = mu, Sigma = v) # 10 draws.
p.d <- list(matrix(pd[,1:2], ncol = 2), pd[,3:8])
p.d[[1]] <- p.d[[1]][-2,] # Remove a draw for the constant
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.sets = 8,
n.alts = 3, alt.cte = c(1, 1, 0),
par.draws = p.d, no.choice = F),
"the number of rows in the components of 'par.draws' should be equal")
# Number of choice sets is smaller than parameters to estimate
mu <- c(0.5, 0.8, 0.2, -0.3, -1.2, 1.6, 2.2)
v <- diag(length(mu)) # Prior variance.
set.seed(123)
pd <- MASS::mvrnorm(n = 3, mu = mu, Sigma = v) # 10 draws.
p.d <- list(matrix(pd[,1], ncol = 1), pd[,2:7])
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.sets = 6,
n.alts = 2, alt.cte = c(1, 0),
par.draws = p.d, no.choice = F),
"Model is unidentified. Increase the number of choice sets or decrease parameters to estimate.")
# Number of columns of par.draws has to be the same as number of parameters
# in the model
mu <- c(0.5, 0.8, 0.2, -0.3, -1.2, 1.6, 2.2)
v <- diag(length(mu)) # Prior variance.
set.seed(123)
pd <- MASS::mvrnorm(n = 3, mu = mu, Sigma = v) # 10 draws.
p.d <- list(matrix(pd[,1], ncol = 1), pd[,2:6])
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"), n.sets = 6,
n.alts = 2, alt.cte = c(1, 0),
par.draws = p.d, no.choice = F),
"The sum of the number of columns in the components of 'par.draws' should equal the number of columns of design matrix \\(including alternative specific constants\\)")
# When initial designs are given, should be in a list
mu <- c(1.2, 1, 0.8, 0.2, -0.3, -1.2, 1.6, 2.2) # Prior parameter vector
v <- diag(length(mu)) # Prior variance.
set.seed(123)
pd <- MASS::mvrnorm(n = 3, mu = mu, Sigma = v) # 10 draws.
p.d <- list(matrix(pd[,1:2], ncol = 2), pd[,3:8])
initial <- matrix(c(1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0,
0, 0, 0, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0,
0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0,
0, 0, 0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,
1, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0,
0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 1, 0, 0, 0,
0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0,
1, 0, 0, 1, 1, 0, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0,
0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1,
0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0,
0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 0, 0, 0, 0,
1, 0, 0, 0, 0, 1, 0, 1, 0, 0, 1, 0, 0, 0, 1, 0,
0, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1,
0, 0, 0, 1, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0),
36,8,byrow = T)
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"),
par.draws = p.d, n.alts = 3, n.sets = 12, parallel = F,
alt.cte = c(1, 0, 1), no.choice = T,
start.des = initial), "'start.des' should be a list")
# All initial designs should be matrices
initial_v <- list(initial, as.vector(initial))
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"),
par.draws = p.d, n.alts = 3, n.sets = 12, parallel = F,
alt.cte = c(1, 0, 1), no.choice = T,start.des = initial_v),
"'start.des' should contain matrices as components")
# All initial designs should have the same dimensions
initial_d <- list(initial, initial[-(1:3),])
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"),
par.draws = p.d, n.alts = 3, n.sets = 12, parallel = F,
alt.cte = c(1, 0, 1), no.choice = T, start.des = initial_d),
"start designs have different dimensions")
# The number of rows of the initial design should be the same as the number
# of alternatives per (times) choice set (n.alts*n.sets)
initial_r <- list(initial[-(1:3),])
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"),
par.draws = p.d, n.alts = 3, n.sets = 12, parallel = F,
alt.cte = c(1, 0, 1), no.choice = T, start.des = initial_r),
"number of rows of start design\\(s\\) does not match with 'n.alts' \\* 'n.sets'")
# The number of columns of the initial design should be the same as the number
# of parameter in the model
initial_c <- list(initial[,-1])
expect_error(CEA(lvls = c(3, 3, 3), coding = c("D", "D", "D"),
par.draws = p.d, n.alts = 3, n.sets = 12, parallel = F,
alt.cte = c(1, 0, 1), no.choice = T, start.des = initial_c),
"number of columns of start design\\(s\\) does not match with the number of columns in the design matrix")
})
```

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