Nothing
tests/testthat/test-opm.R
In OrthoPanels: Dynamic Panel Models with Orthogonal Reparameterization of Fixed Effects
context('opm')
## Generate test data without littering the environment with temporary
## variables
x <- NULL
y <- NULL
d <- NULL
o <- NULL
local({
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
N <- 3
T <- 2
dd <- generate_data(N=N, T=T)
x <<- dd$x
y <<- dd$y
d <<- data.frame(i = rep(seq_len(N), each=T+1),
t = rep(seq_len(T+1), N),
x = c(x),
y = c(y))
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
o <<- opm(y~x, d, n.samp = 10)
})
## Sanity check
test_that('data', {
expect_equal(x, array(c(-1.31047564655221, -0.679491608575424, -0.289083794010798,
-0.23017748948328, 0.129287735160946, -1.26506123460653,
2.30870831414912, 2.46506498688328, 0.0631471481064739),
dim = c(3, 1, 3)))
expect_equal(y, matrix(c(-2.10089979337606, 1.10899305269782, 2.51416798413193,
-1.98942425038169, 0.729823109874504, 2.93377535075353,
-0.352340885393167, 0.230231415863224, 0.531844092800363),
nrow = 3, ncol = 3, byrow=TRUE))
})
test_that('default', {
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expect_equal(opm(x, y, n = 10),
structure(list(samples = list(rho = c(0.728, 0.574,
-0.181, 0.763,
0.877, 0.965,
0.0559999999999999,
0.781, 0.102,
-0.086),
sig2 = 1 / c(3.50041867260549,
4.00944598251174,
1.74928182974987,
0.0471496595688863,
0.342753347371835,
0.0355468448260593,
0.578145844500398,
1.84521258726027,
1.89471612513104,
0.815747433844173),
beta = as.matrix(c(1.06781404188239,
1.57176166272772,
0.984064976368265,
2.10164684546106,
0.663384108167381,
-0.714948757610917,
1.49999093333423,
1.25150458784134,
1.11183526859818,
1.05848623306165))),
fitted.values = matrix(c(-0.249023320200172, 0.249023320200172,
0.883217759019736, -0.883217759019736, 1.17225244244488, -1.17225244244488), 2, 3),
residuals = matrix(c(-0.569518362294091, 0.569518362294091,
-0.633421912014096, 0.633421912014096, 0.0287131865317012, -0.0287131865317007), 2, 3),
df.residual = 2*3 - 2,
logLik = 0.488478652112829,
design = "balanced",
call = quote(opm(x = x, y = y, n.samp = 10)),
.Environment = environment(),
time.indicators = FALSE),
class = 'opm'))
})
test_that('formula', {
d <- data.frame(i = rep(1:3, each=3),
t = rep(1:3, 3),
x = c(x), y = c(y))
expected <- structure(list(samples = list(rho = c(0.728, 0.574,
-0.181, 0.763,
0.877, 0.965,
0.0559999999999999,
0.781, 0.102,
-0.086),
sig2 = 1 / c(3.50041867260549,
4.00944598251174,
1.74928182974987,
0.0471496595688863,
0.342753347371835,
0.0355468448260593,
0.578145844500398,
1.84521258726027,
1.89471612513104,
0.815747433844173),
beta = as.matrix(c(1.06781404188239,
1.57176166272772,
0.984064976368265,
2.10164684546106,
0.663384108167381,
-0.714948757610917,
1.49999093333423,
1.25150458784134,
1.11183526859818,
1.05848623306165))),
fitted.values = matrix(c(-0.249023320200172, 0.249023320200172,
0.883217759019736, -0.883217759019736, 1.17225244244488, -1.17225244244488), 2, 3),
residuals = matrix(c(-0.569518362294091, 0.569518362294091,
-0.633421912014096, 0.633421912014096, 0.0287131865317012, -0.0287131865317007), 2, 3, dimnames=list(t=2:3, i = 1:3)),
df.residual = 2*3 - 2,
logLik = 0.488478652112829,
design = "balanced",
call = quote(opm(x = y~x, data = d, n.samp = 10)),
.Environment = environment(),
time.indicators = FALSE,
index = c('i', 't'),
terms = structure(y~x,
variables = quote(list(y, x)),
factors = matrix(0:1, 2, 1,
dimnames=list(c('y', 'x'),
'x')),
term.labels = 'x',
order = 1L,
intercept = 1L,
response = 1L,
.Environment = parent.frame(),
predvars = quote(list(y, x)),
dataClasses = c(y='numeric', x='numeric'),
class=c('terms', 'formula'))),
class = 'opm')
## default index is the first two columns of the data
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expect_equal(opm(y~x, d,
n.samp = 10),
expected)
## values at T=1 in X aren't used and having NAs there shouldn't
## affect the result
x[x[1,,]==1,,] <- NA
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expect_equal(opm(y~x, d,
n.samp = 10),
expected)
## look for variables in the parent frame
x <- c(x)
y <- c(y)
i <- rep(1:3, each=3)
t <- rep(1:3, 3)
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expected$call <- quote(opm(x = y~x, index = c('i', 't'),
n.samp = 10))
expect_equal(opm(y~x, index = c('i', 't'),
n.samp = 10),
expected)
d <- d[,c('y', 'x', 't', 'i')]
## specify index column by position
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expected$call <- quote(opm(x = y~x, data = d,
index = 4:3, n.samp = 10))
expect_equal(opm(y~x, d, index=4:3,
n.samp = 10),
expected)
## specify index column by name
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expected$call <- quote(opm(x = y~x, data = d,
index = c('i', 't'), n.samp = 10))
expect_equal(opm(y~x, d, index=c('i', 't'), n.samp = 10),
expected)
## different order for i and t columns
d$i <- letters[d$i]
d$t <- d$t*1000
d <- d[sample(nrow(d)), ]
dimnames(expected$residuals) <- list(t = 2:3 * 1000,
i = letters[1:3])
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expect_equal(opm(y~x, d, index=c('i', 't'), n.samp = 10),
expected)
})
test_that('dummy vals', {
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expect_equal(opm(x, y, n = 10, add = TRUE),
structure(list(samples = list(rho = c(-0.647,
-0.647,
-0.647,
0.763,
-0.651,
-0.635,
-0.647,
-0.648,
-0.647,
-0.086),
sig2 = 1 / c(5781053178.63559,
382064945.310797,
297081166.091305,
0.00556021027046193,
202407.002118016,
79927.7878466404,
31958664.3506085,
4096230.88017378,
172152798.137179,
25.8707402214816),
beta = matrix(c(1.37506444649828,
1.3750727761096,
1.37511878023697,
3.60053789546568,
1.37414275681303,
1.37772284729159,
1.37501825167012,
1.37574557193104,
1.37512079428095,
1.51620694338847,
1.17236073947895,
1.17240636180504,
1.17250411197871,
-8.92051368795613,
1.1693793246162,
1.17516181067838,
1.17225716029063,
1.17220803859993,
1.17238653820271,
1.35629081137943),
10, 2,
dimnames = list(NULL, c('1', 'tind.2')))),
fitted.values = matrix(c(-0.818553236715906, 0.818553236715905,
0.249850133411945, -0.249850133411945, 1.2010185374178, -1.2010185374178), 2, 3),
residuals = matrix(c(1.15542216431175e-05,
-1.15542216424513e-05, -5.42864063056703e-05, 5.42864063057258e-05,
-5.29084412208558e-05, 5.29084412215219e-05),
2, 3),
df.residual = 2*3 - 3,
logLik = 46.3811794934746,
design = "balanced",
call = quote(opm(x = x, y = y, n.samp = 10, add.time.indicators = TRUE)),
.Environment = environment(),
time.indicators = TRUE),
class = 'opm'),
tolerance = if (.Machine$sizeof.pointer == 4) 1e-2 else .Machine$double.eps^0.2)
})
test_that('formula dummy vals', {
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expect_equal(opm(y ~ x, d, n = 10, add = TRUE),
structure(list(samples = list(rho = c(-0.647,
-0.647,
-0.647,
0.763,
-0.651,
-0.635,
-0.647,
-0.648,
-0.647,
-0.086),
sig2 = 1 / c(5781053178.63559,
382064945.310797,
297081166.091305,
0.00556021027046193,
202407.002118016,
79927.7878466404,
31958664.3506085,
4096230.88017378,
172152798.137179,
25.8707402214816),
beta = matrix(c(1.37506444649828,
1.3750727761096,
1.37511878023697,
3.60053789546568,
1.37414275681303,
1.37772284729159,
1.37501825167012,
1.37574557193104,
1.37512079428095,
1.51620694338847,
1.17236073947895,
1.17240636180504,
1.17250411197871,
-8.92051368795613,
1.1693793246162,
1.17516181067838,
1.17225716029063,
1.17220803859993,
1.17238653820271,
1.35629081137943),
10, 2,
dimnames = list(NULL, c('1', 'tind.2')))),
fitted.values = matrix(c(-0.818553236715906, 0.818553236715905,
0.249850133411945, -0.249850133411945, 1.2010185374178, -1.2010185374178), 2, 3),
residuals = matrix(c(1.15542216431175e-05,
-1.15542216424513e-05, -5.42864063056703e-05, 5.42864063057258e-05,
-5.29084412208558e-05, 5.29084412215219e-05),
2, 3,
dimnames=list(t=2:3, i=1:3)),
df.residual = 2*3 - 3,
logLik = 46.3811794934746,
design = "balanced",
call = quote(opm(x = y ~ x, data = d, n.samp = 10, add = TRUE)),
.Environment = environment(),
time.indicators = TRUE,
index = c('i', 't'),
terms = structure(y~x,
variables = quote(list(y, x)),
factors = matrix(0:1, 2, 1,
dimnames=list(c('y', 'x'),
'x')),
term.labels = 'x',
order = 1L,
intercept = 1L,
response = 1L,
.Environment = parent.frame(),
predvars = quote(list(y, x)),
dataClasses = c(y='numeric', x='numeric'),
class=c('terms', 'formula'))),
class = 'opm'),
tolerance = if (.Machine$sizeof.pointer == 4) 1e-2 else .Machine$double.eps^0.2)
})
test_that('early dropouts', {
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
xx <- array(NA, dim=dim(x) + c(0, 0, 1))
xx[,,seq_len(ncol(y))] <- x
yy <- matrix(NA, nrow(y), ncol(y)+1)
yy[,seq_len(ncol(y))] <- y
## one more case, but immediately drops out
expect_equal(dim(xx), dim(x)+c(0,0,1))
expect_equal(dim(yy), dim(y)+c(0,1))
expect_equal(opm(xx, yy, n = 10),
structure(list(samples = list(rho = c(0.728, 0.574,
-0.181, 0.763,
0.877, 0.965,
0.0559999999999999,
0.781, 0.102,
-0.086),
sig2 = 1 / c(3.50041867260549,
4.00944598251174,
1.74928182974987,
0.0471496595688863,
0.342753347371835,
0.0355468448260593,
0.578145844500398,
1.84521258726027,
1.89471612513104,
0.815747433844173),
beta = as.matrix(c(1.06781404188239,
1.57176166272772,
0.984064976368265,
2.10164684546106,
0.663384108167381,
-0.714948757610917,
1.49999093333423,
1.25150458784134,
1.11183526859818,
1.05848623306165))),
fitted.values = matrix(c(-0.249023320200172, 0.249023320200172,
0.883217759019736, -0.883217759019736, 1.17225244244488, -1.17225244244488,
0, 0), 2, 4),
residuals = matrix(c(-0.569518362294091,
0.569518362294091, -0.633421912014096, 0.633421912014096, 0.0287131865317012,
-0.0287131865317007, 0, 0), 2, 4),
df.residual = 2*3 - 2,
logLik = 0.435112589801499,
design = "unbalanced (with dropouts)",
call = quote(opm(x = xx, y = yy, n.samp = 10)),
.Environment = environment(),
time.indicators = FALSE),
class = 'opm'))
})
test_that('confint', {
expect_equal(confint(o),
matrix(c(-0.159625, 0.257571563455217, -0.4048238628108,
0.9452, 26.5742550057164, 1.98242267934606),
nrow = 3, ncol = 2,
dimnames = list(c("rho", "sig2", "beta"),
c("2.5%", "97.5%"))))
expect_equal(confint(o, 1:2, level = 0.68),
matrix(c(-0.0235200000000001, 0.39220562432823,
0.83476, 13.1607960528734),
nrow = 2, ncol = 2,
dimnames = list(c("rho", "sig2"), c("16%", "84%"))))
})
test_that('coef', {
expect_equal(coef(o),
c(rho = 0.651,
sig2 = 0.898766398134737,
beta = 1.08982465524029))
expect_error(coef(o, probs = .6), "Arguments 'probs' and 'names' are not allowed")
expect_error(coef(o, names = TRUE), "Arguments 'probs' and 'names' are not allowed")
})
test_that('print', {
expect_equal(capture.output(o),
c('\tPanel design: balanced',
'',
'Call:',
'opm(x = y ~ x, data = d, n.samp = 10)',
'',
'Coefficients:',
' mean (SD) med 95-CI',
'rho 0.4579 (0.44) 0.65100 (-0.16, 0.95)',
'sig2 5.7391 (10.14) 0.89877 (0.26, 26.57)',
'beta 1.0596 (0.74) 1.08982 (-0.4, 2.0)',
''))
})
test_that('summary', {
expect_equal(capture.output(summary(o)),
c('Call:',
'opm(x = y ~ x, data = d, n.samp = 10)',
'',
'Parameter estimates:',
' <--95CI <--68CI med 68CI--> 95CI-->',
'rho -0.15962 -0.02352 0.65100 0.83476 0.9452',
'sig2 0.25757 0.39221 0.89877 13.16080 26.5743',
'beta -0.40482 0.80448 1.08982 1.54018 1.9824'))
})
test_that('DIC', {
expect_equal(DIC(o),
16.807856425829)
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expect_equal(DIC(opm(x, y, n = 10, add = TRUE)),
-133.868546487072,
tolerance = if (.Machine$sizeof.pointer == 4) 1e-2 else .Machine$double.eps^0.2)
})
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context('opm')
## Generate test data without littering the environment with temporary
## variables
x <- NULL
y <- NULL
d <- NULL
o <- NULL
local({
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
N <- 3
T <- 2
dd <- generate_data(N=N, T=T)
x <<- dd$x
y <<- dd$y
d <<- data.frame(i = rep(seq_len(N), each=T+1),
t = rep(seq_len(T+1), N),
x = c(x),
y = c(y))
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
o <<- opm(y~x, d, n.samp = 10)
})
## Sanity check
test_that('data', {
expect_equal(x, array(c(-1.31047564655221, -0.679491608575424, -0.289083794010798,
-0.23017748948328, 0.129287735160946, -1.26506123460653,
2.30870831414912, 2.46506498688328, 0.0631471481064739),
dim = c(3, 1, 3)))
expect_equal(y, matrix(c(-2.10089979337606, 1.10899305269782, 2.51416798413193,
-1.98942425038169, 0.729823109874504, 2.93377535075353,
-0.352340885393167, 0.230231415863224, 0.531844092800363),
nrow = 3, ncol = 3, byrow=TRUE))
})
test_that('default', {
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expect_equal(opm(x, y, n = 10),
structure(list(samples = list(rho = c(0.728, 0.574,
-0.181, 0.763,
0.877, 0.965,
0.0559999999999999,
0.781, 0.102,
-0.086),
sig2 = 1 / c(3.50041867260549,
4.00944598251174,
1.74928182974987,
0.0471496595688863,
0.342753347371835,
0.0355468448260593,
0.578145844500398,
1.84521258726027,
1.89471612513104,
0.815747433844173),
beta = as.matrix(c(1.06781404188239,
1.57176166272772,
0.984064976368265,
2.10164684546106,
0.663384108167381,
-0.714948757610917,
1.49999093333423,
1.25150458784134,
1.11183526859818,
1.05848623306165))),
fitted.values = matrix(c(-0.249023320200172, 0.249023320200172,
0.883217759019736, -0.883217759019736, 1.17225244244488, -1.17225244244488), 2, 3),
residuals = matrix(c(-0.569518362294091, 0.569518362294091,
-0.633421912014096, 0.633421912014096, 0.0287131865317012, -0.0287131865317007), 2, 3),
df.residual = 2*3 - 2,
logLik = 0.488478652112829,
design = "balanced",
call = quote(opm(x = x, y = y, n.samp = 10)),
.Environment = environment(),
time.indicators = FALSE),
class = 'opm'))
})
test_that('formula', {
d <- data.frame(i = rep(1:3, each=3),
t = rep(1:3, 3),
x = c(x), y = c(y))
expected <- structure(list(samples = list(rho = c(0.728, 0.574,
-0.181, 0.763,
0.877, 0.965,
0.0559999999999999,
0.781, 0.102,
-0.086),
sig2 = 1 / c(3.50041867260549,
4.00944598251174,
1.74928182974987,
0.0471496595688863,
0.342753347371835,
0.0355468448260593,
0.578145844500398,
1.84521258726027,
1.89471612513104,
0.815747433844173),
beta = as.matrix(c(1.06781404188239,
1.57176166272772,
0.984064976368265,
2.10164684546106,
0.663384108167381,
-0.714948757610917,
1.49999093333423,
1.25150458784134,
1.11183526859818,
1.05848623306165))),
fitted.values = matrix(c(-0.249023320200172, 0.249023320200172,
0.883217759019736, -0.883217759019736, 1.17225244244488, -1.17225244244488), 2, 3),
residuals = matrix(c(-0.569518362294091, 0.569518362294091,
-0.633421912014096, 0.633421912014096, 0.0287131865317012, -0.0287131865317007), 2, 3, dimnames=list(t=2:3, i = 1:3)),
df.residual = 2*3 - 2,
logLik = 0.488478652112829,
design = "balanced",
call = quote(opm(x = y~x, data = d, n.samp = 10)),
.Environment = environment(),
time.indicators = FALSE,
index = c('i', 't'),
terms = structure(y~x,
variables = quote(list(y, x)),
factors = matrix(0:1, 2, 1,
dimnames=list(c('y', 'x'),
'x')),
term.labels = 'x',
order = 1L,
intercept = 1L,
response = 1L,
.Environment = parent.frame(),
predvars = quote(list(y, x)),
dataClasses = c(y='numeric', x='numeric'),
class=c('terms', 'formula'))),
class = 'opm')
## default index is the first two columns of the data
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expect_equal(opm(y~x, d,
n.samp = 10),
expected)
## values at T=1 in X aren't used and having NAs there shouldn't
## affect the result
x[x[1,,]==1,,] <- NA
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expect_equal(opm(y~x, d,
n.samp = 10),
expected)
## look for variables in the parent frame
x <- c(x)
y <- c(y)
i <- rep(1:3, each=3)
t <- rep(1:3, 3)
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expected$call <- quote(opm(x = y~x, index = c('i', 't'),
n.samp = 10))
expect_equal(opm(y~x, index = c('i', 't'),
n.samp = 10),
expected)
d <- d[,c('y', 'x', 't', 'i')]
## specify index column by position
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expected$call <- quote(opm(x = y~x, data = d,
index = 4:3, n.samp = 10))
expect_equal(opm(y~x, d, index=4:3,
n.samp = 10),
expected)
## specify index column by name
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expected$call <- quote(opm(x = y~x, data = d,
index = c('i', 't'), n.samp = 10))
expect_equal(opm(y~x, d, index=c('i', 't'), n.samp = 10),
expected)
## different order for i and t columns
d$i <- letters[d$i]
d$t <- d$t*1000
d <- d[sample(nrow(d)), ]
dimnames(expected$residuals) <- list(t = 2:3 * 1000,
i = letters[1:3])
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expect_equal(opm(y~x, d, index=c('i', 't'), n.samp = 10),
expected)
})
test_that('dummy vals', {
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expect_equal(opm(x, y, n = 10, add = TRUE),
structure(list(samples = list(rho = c(-0.647,
-0.647,
-0.647,
0.763,
-0.651,
-0.635,
-0.647,
-0.648,
-0.647,
-0.086),
sig2 = 1 / c(5781053178.63559,
382064945.310797,
297081166.091305,
0.00556021027046193,
202407.002118016,
79927.7878466404,
31958664.3506085,
4096230.88017378,
172152798.137179,
25.8707402214816),
beta = matrix(c(1.37506444649828,
1.3750727761096,
1.37511878023697,
3.60053789546568,
1.37414275681303,
1.37772284729159,
1.37501825167012,
1.37574557193104,
1.37512079428095,
1.51620694338847,
1.17236073947895,
1.17240636180504,
1.17250411197871,
-8.92051368795613,
1.1693793246162,
1.17516181067838,
1.17225716029063,
1.17220803859993,
1.17238653820271,
1.35629081137943),
10, 2,
dimnames = list(NULL, c('1', 'tind.2')))),
fitted.values = matrix(c(-0.818553236715906, 0.818553236715905,
0.249850133411945, -0.249850133411945, 1.2010185374178, -1.2010185374178), 2, 3),
residuals = matrix(c(1.15542216431175e-05,
-1.15542216424513e-05, -5.42864063056703e-05, 5.42864063057258e-05,
-5.29084412208558e-05, 5.29084412215219e-05),
2, 3),
df.residual = 2*3 - 3,
logLik = 46.3811794934746,
design = "balanced",
call = quote(opm(x = x, y = y, n.samp = 10, add.time.indicators = TRUE)),
.Environment = environment(),
time.indicators = TRUE),
class = 'opm'),
tolerance = if (.Machine$sizeof.pointer == 4) 1e-2 else .Machine$double.eps^0.2)
})
test_that('formula dummy vals', {
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expect_equal(opm(y ~ x, d, n = 10, add = TRUE),
structure(list(samples = list(rho = c(-0.647,
-0.647,
-0.647,
0.763,
-0.651,
-0.635,
-0.647,
-0.648,
-0.647,
-0.086),
sig2 = 1 / c(5781053178.63559,
382064945.310797,
297081166.091305,
0.00556021027046193,
202407.002118016,
79927.7878466404,
31958664.3506085,
4096230.88017378,
172152798.137179,
25.8707402214816),
beta = matrix(c(1.37506444649828,
1.3750727761096,
1.37511878023697,
3.60053789546568,
1.37414275681303,
1.37772284729159,
1.37501825167012,
1.37574557193104,
1.37512079428095,
1.51620694338847,
1.17236073947895,
1.17240636180504,
1.17250411197871,
-8.92051368795613,
1.1693793246162,
1.17516181067838,
1.17225716029063,
1.17220803859993,
1.17238653820271,
1.35629081137943),
10, 2,
dimnames = list(NULL, c('1', 'tind.2')))),
fitted.values = matrix(c(-0.818553236715906, 0.818553236715905,
0.249850133411945, -0.249850133411945, 1.2010185374178, -1.2010185374178), 2, 3),
residuals = matrix(c(1.15542216431175e-05,
-1.15542216424513e-05, -5.42864063056703e-05, 5.42864063057258e-05,
-5.29084412208558e-05, 5.29084412215219e-05),
2, 3,
dimnames=list(t=2:3, i=1:3)),
df.residual = 2*3 - 3,
logLik = 46.3811794934746,
design = "balanced",
call = quote(opm(x = y ~ x, data = d, n.samp = 10, add = TRUE)),
.Environment = environment(),
time.indicators = TRUE,
index = c('i', 't'),
terms = structure(y~x,
variables = quote(list(y, x)),
factors = matrix(0:1, 2, 1,
dimnames=list(c('y', 'x'),
'x')),
term.labels = 'x',
order = 1L,
intercept = 1L,
response = 1L,
.Environment = parent.frame(),
predvars = quote(list(y, x)),
dataClasses = c(y='numeric', x='numeric'),
class=c('terms', 'formula'))),
class = 'opm'),
tolerance = if (.Machine$sizeof.pointer == 4) 1e-2 else .Machine$double.eps^0.2)
})
test_that('early dropouts', {
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
xx <- array(NA, dim=dim(x) + c(0, 0, 1))
xx[,,seq_len(ncol(y))] <- x
yy <- matrix(NA, nrow(y), ncol(y)+1)
yy[,seq_len(ncol(y))] <- y
## one more case, but immediately drops out
expect_equal(dim(xx), dim(x)+c(0,0,1))
expect_equal(dim(yy), dim(y)+c(0,1))
expect_equal(opm(xx, yy, n = 10),
structure(list(samples = list(rho = c(0.728, 0.574,
-0.181, 0.763,
0.877, 0.965,
0.0559999999999999,
0.781, 0.102,
-0.086),
sig2 = 1 / c(3.50041867260549,
4.00944598251174,
1.74928182974987,
0.0471496595688863,
0.342753347371835,
0.0355468448260593,
0.578145844500398,
1.84521258726027,
1.89471612513104,
0.815747433844173),
beta = as.matrix(c(1.06781404188239,
1.57176166272772,
0.984064976368265,
2.10164684546106,
0.663384108167381,
-0.714948757610917,
1.49999093333423,
1.25150458784134,
1.11183526859818,
1.05848623306165))),
fitted.values = matrix(c(-0.249023320200172, 0.249023320200172,
0.883217759019736, -0.883217759019736, 1.17225244244488, -1.17225244244488,
0, 0), 2, 4),
residuals = matrix(c(-0.569518362294091,
0.569518362294091, -0.633421912014096, 0.633421912014096, 0.0287131865317012,
-0.0287131865317007, 0, 0), 2, 4),
df.residual = 2*3 - 2,
logLik = 0.435112589801499,
design = "unbalanced (with dropouts)",
call = quote(opm(x = xx, y = yy, n.samp = 10)),
.Environment = environment(),
time.indicators = FALSE),
class = 'opm'))
})
test_that('confint', {
expect_equal(confint(o),
matrix(c(-0.159625, 0.257571563455217, -0.4048238628108,
0.9452, 26.5742550057164, 1.98242267934606),
nrow = 3, ncol = 2,
dimnames = list(c("rho", "sig2", "beta"),
c("2.5%", "97.5%"))))
expect_equal(confint(o, 1:2, level = 0.68),
matrix(c(-0.0235200000000001, 0.39220562432823,
0.83476, 13.1607960528734),
nrow = 2, ncol = 2,
dimnames = list(c("rho", "sig2"), c("16%", "84%"))))
})
test_that('coef', {
expect_equal(coef(o),
c(rho = 0.651,
sig2 = 0.898766398134737,
beta = 1.08982465524029))
expect_error(coef(o, probs = .6), "Arguments 'probs' and 'names' are not allowed")
expect_error(coef(o, names = TRUE), "Arguments 'probs' and 'names' are not allowed")
})
test_that('print', {
expect_equal(capture.output(o),
c('\tPanel design: balanced',
'',
'Call:',
'opm(x = y ~ x, data = d, n.samp = 10)',
'',
'Coefficients:',
' mean (SD) med 95-CI',
'rho 0.4579 (0.44) 0.65100 (-0.16, 0.95)',
'sig2 5.7391 (10.14) 0.89877 (0.26, 26.57)',
'beta 1.0596 (0.74) 1.08982 (-0.4, 2.0)',
''))
})
test_that('summary', {
expect_equal(capture.output(summary(o)),
c('Call:',
'opm(x = y ~ x, data = d, n.samp = 10)',
'',
'Parameter estimates:',
' <--95CI <--68CI med 68CI--> 95CI-->',
'rho -0.15962 -0.02352 0.65100 0.83476 0.9452',
'sig2 0.25757 0.39221 0.89877 13.16080 26.5743',
'beta -0.40482 0.80448 1.08982 1.54018 1.9824'))
})
test_that('DIC', {
expect_equal(DIC(o),
16.807856425829)
suppressWarnings(RNGversion("3.5.0"))
set.seed(123, kind = "Mersenne-Twister", normal.kind = "Inversion")
expect_equal(DIC(opm(x, y, n = 10, add = TRUE)),
-133.868546487072,
tolerance = if (.Machine$sizeof.pointer == 4) 1e-2 else .Machine$double.eps^0.2)
})
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