Nothing

```
## library(growfunctions, quietly = TRUE)
context("test gpdpgrow() returns correct objects")
##
## Load cps dataset
##
data(cps)
## take a portion of the data matrix for compute speed
y_short <- cps$y[,(cps$yr_label %in% c(2012:2013))]
T <- ncol(y_short)
N <- nrow(y_short)
##
## estimation model
##
mod <- function(y, gp_cov, sn_order=NULL, niter, nburn, nthin){
gpdpgrow(y = y, jitter = 0.001, gp_cov = gp_cov,
sn_order = sn_order, n.iter = niter, n.burn = nburn,
n.thin = nthin, progress = FALSE, n.tune = 0)
}
test_that("gpdpgrow() with no missing data and one covariance term returns correct objects", {
P <- 3 ## number of covariance parameters associated with a rational quadratic
niter <- 5
nburn <- 1
nthin <- 1
GPDP <- mod(y = y_short, gp_cov = "rq",niter = niter,nburn = nburn, nthin =nthin)
## perform plots of posterior mean function values vs. data and functions grouped to cluster
plots_gp <- cluster_plot( object = GPDP, units_name = "state", units_label = cps$st,
single_unit = FALSE, credible = TRUE )
## evaluating class
expect_that(GPDP,is_a("gpdpgrow"))
## evaluating MCMC sample results
expect_that(ncol(GPDP$bb), equals(N*T))
## expect_that(GPDP$f, is_a("list"))
expect_that(nrow(GPDP$f), equals(1))
expect_that(ncol(GPDP$Theta), equals(N*P))
## check cluster_plot()
expect_that(plots_gp$p.cluster, is_a("ggplot"))
expect_that(plots_gp$p.fit, is_a("ggplot"))
expect_that(nrow(plots_gp$map), equals(N))
})
test_that("gpdpgrow() with some missing data and one covariance term returns correct objects", {
## insert missing values in observed data matrix, y
# randomly assign missing positions in y.
# assume every unit has equal number of missing positions
# randomly select number of missing observations for each unit
m_factor = .1
M = floor(m_factor*N*T)
m_vec = rep(floor(M/N),N)
if( sum(m_vec) < M )
{
m_left <- M - sum(m_vec)
pos_i <- sample(1:N, m_left, replace = FALSE)
m_vec[pos_i] <- m_vec[pos_i] + 1
} # end conditional statement on whether all missing cells allocated
## randomly select missing positions for each unit
pos <- matrix(0,N,T)
for( i in 1:N )
{
sel_ij <- sample(3:(T-3), m_vec[i], replace = FALSE) ## avoid edge effects
pos[i,sel_ij] <- 1
}
## blank cells in response corresponding to missing positions
y_obs <- y_short
y_obs[pos == 1] <- NA
## set number of iterations
P <- 3 ## number of covariance parameters associated with a rational quadratic
niter <- 5
nburn <- 1
nthin <- 1
GPDP_m <- mod(y = y_obs,gp_cov = "rq",niter = niter,nburn = nburn,nthin = nthin)
## evaluating class
expect_that(GPDP_m,is_a("gpdpgrow"))
## evaluating MCMC sample results
expect_that(ncol(GPDP_m$bb), equals(N*T))
expect_that(ncol(GPDP_m$Theta), equals(N*P))
})
test_that("gpdpgrow() with no missing data and two covariance term returns correct objects", {
P <- 6 ## number of covariance parameters associated with a rational quadratic
niter <- 5
nburn <- 1
nthin <- 1
gp_cov_2 <- c("rq","sn")
GPDP_2 <- mod(y_short,gp_cov_2,3,niter,nburn,nthin)
## evaluating class
expect_that(GPDP_2,is_a("gpdpgrow"))
## evaluating MCMC sample results
expect_that(ncol(GPDP_2$bb), equals(N*T))
expect_that(nrow(GPDP_2$f), equals(length(gp_cov_2)))
expect_that(ncol(GPDP_2$Theta), equals(N*P))
})
```

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