Nothing
inst/unitTests/runit_objective.R
In likeLTD: Tools to Evaluate DNA Profile Evidence
## Test unit 'objectives'
library(svUnit)
###############################################################
# The new two functions are to set up the unit test environment
###############################################################
.setUp <-
function () {
## Specific actions for svUnit: prepare context
if ("package:svUnit" %in% search()) {
.Log <- Log() ## Make sure .Log is created
.Log$..Unit <- "inst/unitTests/runit_objective.R"
.Log$..File <- ""
.Log$..Obj <- ""
.Log$..Tag <- ""
.Log$..Msg <- ""
rm(..Test, envir = .Log)
}
# Sets threads to 2 or less. This is a CRAN requirement.
if(.Call(likeLTD::.cpp.nbthreads) > 2) {
nb_threads_in_test = .Call(likeLTD::.cpp.nbthreads)
.Call(likeLTD::.cpp.set_nbthreads, as.integer(2))
}
}
.tearDown <-
function () {
## Specific actions for svUnit: clean up context
if ("package:svUnit" %in% search()) {
.Log$..Unit <- ""
.Log$..File <- ""
.Log$..Obj <- ""
.Log$..Tag <- ""
.Log$..Msg <- ""
rm(..Test, envir = .Log)
}
# Reset number of threads to what it was.
if('nb_threads_in_test' %in% ls()) {
.Call(likeLTD::.cpp.set_nbthreads, as.integer(nb_threads_in_test))
rm(nb_threads_in_test)
}
}
test_regression1 <- svTest(function() {
# Case we are going to be looking at.
datapath = system.file(file.path('extdata', 'hammer'), package="likeLTD")
args = list(
databaseFile = NULL,
kit = "SGMplus",
cspFile = file.path(datapath, 'hammer-CSP.csv'),
refFile = file.path(datapath, 'hammer-reference.csv'),
nUnknowns = 0,
doDropin = TRUE,
ethnic = "EA1",
adj = 1.0,
fst = 0.02,
relationship = 0,
combineRare = FALSE
)
if(! "defence.hypothesis" %in% ls(.GlobalEnv))
defence.hypothesis <- getFromNamespace("defence.hypothesis", "likeLTD")
hypothesis = do.call(defence.hypothesis, args)
hypothesis$nUnknowns = 0
likelihood <- create.likelihood.vectors(hypothesis)
objectives = c(3.10250372325746e-04, 1.17224578453062e-02,
4.76863464507366e-05, 4.29822197384531e-06,
1.76350988087800e-03, 1.43444739873622e-08,
9.09495530595362e-06, 7.11137407679358e-08,
3.01744911691531e-04, 6.89041414610852e-03)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
penalties = c(0.00303192703176332, 0.00303192703176332, 0.00303192703176332,
0.00303192703176332, 0.00303192703176332, 0.00303192703176332,
0.00303192703176332, 0.00303192703176332, 0.00303192703176332,
0.00303192703176332)
check = list(objectives=objectives, penalties=penalties)
arguments = list(rcont=c(0.923913043478261, 0.565217391304348),
dropin = 1.0,
degradation=rep(3e-3, 2),
locusAdjustment=1,
power=-4.35, beta=-4.35,
dropout=c(0.175, 0.105) )
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, check$objectives)
})
test_regression.zerounknown <- svTest(function() {
# Case we are going to be looking at.
datapath = system.file(file.path('extdata', 'hammer'), package="likeLTD")
args = list(
databaseFile = NULL,
kit = "SGMplus",
cspFile = file.path(datapath, 'hammer-CSP.csv'),
refFile = file.path(datapath, 'hammer-reference.csv'),
nUnknowns = 0,
doDropin = TRUE,
ethnic = "EA1",
adj = 1.0,
fst = 0.02,
relatedness = c(0.0, 0),
combineRare = FALSE
)
defenceHypothesis = do.call(defence.hypothesis, args)
prosecutionHypothesis = do.call(prosecution.hypothesis, args)
likelihood <- create.likelihood.vectors(prosecutionHypothesis)
arguments = list(rcont=c(0.923913043478261, 0.565217391304348, 1.0),
dropin = 0.543478260869565,
degradation=c(3e-3, 3e-3, 3e-3),
locusAdjustment=1,
power=-4.35,
dropout=c(0.15, 0.01) )
objectives = c(2.64877312615097e-04, 8.30010229904578e-02,
6.57219727208952e-02, 3.55156330421480e-03,
5.58990000167620e-01, 2.58579653708631e-05,
6.88667211400942e-07, 5.73149656581839e-02,
2.70886382744855e-02, 3.64303077108431e-03)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, objectives)
checkEquals(prod(result$objectives * result$penalties), 5.82387768745086e-25)
likelihood <- create.likelihood.vectors(defenceHypothesis)
arguments = list(rcont=c(0.923913043478261, 0.565217391304348, 1.0),
dropin = 0.543478260869565,
degradation=c(3e-3, 3e-3, 3e-3),
locusAdjustment=1,
power=-4.35,
dropout=c(0.175, 0.105) )
objectives = c(2.01470581026018e-03, 1.13394042752114e-02, 4.69287546906059e-03,
2.47293709587760e-04, 5.90787709164540e-02, 9.97729081224379e-07,
6.25814358291258e-06, 1.04230422586896e-04, 1.46838789834590e-02,
1.43741838557565e-03)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, objectives)
checkEquals(prod(result$objectives * result$penalties), 6.97617046679607e-32)
})
test_regression.oneunknown <- svTest(function() {
# Case we are going to be looking at.
datapath = system.file(file.path('extdata', 'hammer'), package="likeLTD")
args = list(
databaseFile = NULL,
kit = "SGMplus",
cspFile = file.path(datapath, 'hammer-CSP.csv'),
refFile = file.path(datapath, 'hammer-reference.csv'),
nUnknowns = 1,
doDropin = TRUE,
ethnic = "EA1",
adj = 1.0,
fst = 0.02,
relatedness = c(0.0, 0),
combineRare = FALSE
)
defenceHypothesis = do.call(defence.hypothesis, args)
prosecutionHypothesis = do.call(prosecution.hypothesis, args)
likelihood <- create.likelihood.vectors(prosecutionHypothesis)
arguments = list(rcont=c(0.923913043478261, 0.565217391304348, 1.0,
0.543478260869565),
dropin = 0.108695652173913,
degradation=rep(3e-3, 4),
locusAdjustment=1,
power=-4.35,
dropout=c(0.15, 0.01) )
objectives = c(5.24609987450467e-05, 1.28369768074416e-01, 3.19566044368621e-02,
9.37696749226556e-04, 3.54990168257584e-01, 3.61714632221897e-05,
2.78024377866913e-06, 7.42370399489160e-02, 2.15249772203121e-02,
4.27467268573391e-04)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, objectives)
checkEquals(prod(result$objectives * result$penalties), 1.9762409429182e-25)
likelihood <- create.likelihood.vectors(defenceHypothesis)
arguments = list(rcont=c(0.923913043478261, 0.565217391304348, 1.0,
0.543478260869565),
dropin = 0.108695652173913,
degradation=rep(3e-3, 4),
locusAdjustment=1,
power=-4.35,
dropout=c(0.175, 0.105) )
objectives = c(5.40928563410066e-04, 1.24028883413425e-02, 4.32400562747164e-03,
3.71369198539134e-04, 7.20410130310739e-02, 1.08896493758788e-06,
2.81245481065190e-06, 1.83320557026132e-04, 1.94351600773495e-02,
5.75805705402794e-04)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, objectives)
checkEquals(prod(result$objectives * result$penalties), 3.63417836957934e-31)
})
test_regression.relatedness <- svTest(function() {
# Case we are going to be looking at.
datapath = system.file(file.path('extdata', 'hammer'), package="likeLTD")
args = list(
databaseFile = NULL,
kit = "SGMplus",
cspFile = file.path(datapath, 'hammer-CSP.csv'),
refFile = file.path(datapath, 'hammer-reference.csv'),
nUnknowns = 1,
doDropin = TRUE,
ethnic = "EA1",
adj = 1.0,
fst = 0.02,
relationship = 2,
combineRare = FALSE
)
defenceHypothesis = do.call(defence.hypothesis, args)
prosecutionHypothesis = do.call(prosecution.hypothesis, args)
likelihood <- create.likelihood.vectors(prosecutionHypothesis)
arguments = list(rcont=c(0.923913043478261, 0.565217391304348, 1.0,
0.543478260869565),
dropin = 0.108695652173913,
degradation=rep(3e-3, 4),
locusAdjustment=rep(1,times=10),
power=-4.35,
dropout=c(0.15, 0.01) )
objectives = c(5.2460998745046723702e-05, 1.2836976807441610737e-01, 3.1956604436862108554e-02,
9.3769674922655570342e-04, 3.5499016825758356042e-01, 3.6171463222189653881e-05,
2.7802437786691249654e-06, 7.4237039948916033749e-02, 2.1524977220312099119e-02,
4.2746726857339074597e-04)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
penalty = 2.6078646479705969163
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, objectives)
checkEquals(prod(result$objectives * result$penalties), prod(objectives * penalty), checkNames=FALSE)
# old result was 1.9762409429182e-25 (different penalties)
likelihood <- create.likelihood.vectors(defenceHypothesis)
arguments = list(rcont=c(0.923913043478261, 0.565217391304348, 1.0,
0.543478260869565),
dropin = 0.108695652173913,
degradation=rep(3e-3, 4),
locusAdjustment=rep(1,times=10),
power=-4.35,
dropout=c(0.175, 0.105) )
objectives = c(2.0324320110552429076e-03, 4.0692306250085193142e-02, 4.4262872552010612548e-02,
1.5005784145824823013e-03, 1.9188770082595565936e-01, 7.4396398032556112094e-06,
2.1466961591367787368e-06, 1.3734648258538964885e-02, 5.5349543045990240442e-02,
2.2848765693775441657e-03)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
penalty = 2.6078646479705969163
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, objectives)
checkEquals(prod(result$objectives * result$penalties), prod(objectives * penalty), checkNames=FALSE)
# old result was 5.75589320779353e-25 (different penalties)
defenceHypothesis$relationship = 2
likelihood <- create.likelihood.vectors(defenceHypothesis)
# This also tests that refIndiv works. The objective function should be
# inserting 1 at position 3 in rcont.
arguments = list(rcont=c(0.923913043478261, 0.565217391304348,
0.543478260869565),
dropin = 0.108695652173913,
degradation=rep(3e-3, 4),
locusAdjustment=rep(1,times=10),
power=-4.35,
dropout=c(0.175, 0.105) )
objectives = c(2.0324320110552429076e-03, 4.0692306250085193142e-02, 4.4262872552010612548e-02,
1.5005784145824823013e-03, 1.9188770082595565936e-01, 7.4396398032556112094e-06,
2.1466961591367787368e-06, 1.3734648258538964885e-02, 5.5349543045990240442e-02,
2.2848765693775441657e-03)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
penalty = 2.6078646479705969163
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, objectives)
checkEquals(prod(result$objectives * result$penalties), prod(objectives * penalty), checkNames=FALSE)
# old result was 7.07569814196917e-26 (different penalties)
})
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## Test unit 'objectives'
library(svUnit)
###############################################################
# The new two functions are to set up the unit test environment
###############################################################
.setUp <-
function () {
## Specific actions for svUnit: prepare context
if ("package:svUnit" %in% search()) {
.Log <- Log() ## Make sure .Log is created
.Log$..Unit <- "inst/unitTests/runit_objective.R"
.Log$..File <- ""
.Log$..Obj <- ""
.Log$..Tag <- ""
.Log$..Msg <- ""
rm(..Test, envir = .Log)
}
# Sets threads to 2 or less. This is a CRAN requirement.
if(.Call(likeLTD::.cpp.nbthreads) > 2) {
nb_threads_in_test = .Call(likeLTD::.cpp.nbthreads)
.Call(likeLTD::.cpp.set_nbthreads, as.integer(2))
}
}
.tearDown <-
function () {
## Specific actions for svUnit: clean up context
if ("package:svUnit" %in% search()) {
.Log$..Unit <- ""
.Log$..File <- ""
.Log$..Obj <- ""
.Log$..Tag <- ""
.Log$..Msg <- ""
rm(..Test, envir = .Log)
}
# Reset number of threads to what it was.
if('nb_threads_in_test' %in% ls()) {
.Call(likeLTD::.cpp.set_nbthreads, as.integer(nb_threads_in_test))
rm(nb_threads_in_test)
}
}
test_regression1 <- svTest(function() {
# Case we are going to be looking at.
datapath = system.file(file.path('extdata', 'hammer'), package="likeLTD")
args = list(
databaseFile = NULL,
kit = "SGMplus",
cspFile = file.path(datapath, 'hammer-CSP.csv'),
refFile = file.path(datapath, 'hammer-reference.csv'),
nUnknowns = 0,
doDropin = TRUE,
ethnic = "EA1",
adj = 1.0,
fst = 0.02,
relationship = 0,
combineRare = FALSE
)
if(! "defence.hypothesis" %in% ls(.GlobalEnv))
defence.hypothesis <- getFromNamespace("defence.hypothesis", "likeLTD")
hypothesis = do.call(defence.hypothesis, args)
hypothesis$nUnknowns = 0
likelihood <- create.likelihood.vectors(hypothesis)
objectives = c(3.10250372325746e-04, 1.17224578453062e-02,
4.76863464507366e-05, 4.29822197384531e-06,
1.76350988087800e-03, 1.43444739873622e-08,
9.09495530595362e-06, 7.11137407679358e-08,
3.01744911691531e-04, 6.89041414610852e-03)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
penalties = c(0.00303192703176332, 0.00303192703176332, 0.00303192703176332,
0.00303192703176332, 0.00303192703176332, 0.00303192703176332,
0.00303192703176332, 0.00303192703176332, 0.00303192703176332,
0.00303192703176332)
check = list(objectives=objectives, penalties=penalties)
arguments = list(rcont=c(0.923913043478261, 0.565217391304348),
dropin = 1.0,
degradation=rep(3e-3, 2),
locusAdjustment=1,
power=-4.35, beta=-4.35,
dropout=c(0.175, 0.105) )
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, check$objectives)
})
test_regression.zerounknown <- svTest(function() {
# Case we are going to be looking at.
datapath = system.file(file.path('extdata', 'hammer'), package="likeLTD")
args = list(
databaseFile = NULL,
kit = "SGMplus",
cspFile = file.path(datapath, 'hammer-CSP.csv'),
refFile = file.path(datapath, 'hammer-reference.csv'),
nUnknowns = 0,
doDropin = TRUE,
ethnic = "EA1",
adj = 1.0,
fst = 0.02,
relatedness = c(0.0, 0),
combineRare = FALSE
)
defenceHypothesis = do.call(defence.hypothesis, args)
prosecutionHypothesis = do.call(prosecution.hypothesis, args)
likelihood <- create.likelihood.vectors(prosecutionHypothesis)
arguments = list(rcont=c(0.923913043478261, 0.565217391304348, 1.0),
dropin = 0.543478260869565,
degradation=c(3e-3, 3e-3, 3e-3),
locusAdjustment=1,
power=-4.35,
dropout=c(0.15, 0.01) )
objectives = c(2.64877312615097e-04, 8.30010229904578e-02,
6.57219727208952e-02, 3.55156330421480e-03,
5.58990000167620e-01, 2.58579653708631e-05,
6.88667211400942e-07, 5.73149656581839e-02,
2.70886382744855e-02, 3.64303077108431e-03)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, objectives)
checkEquals(prod(result$objectives * result$penalties), 5.82387768745086e-25)
likelihood <- create.likelihood.vectors(defenceHypothesis)
arguments = list(rcont=c(0.923913043478261, 0.565217391304348, 1.0),
dropin = 0.543478260869565,
degradation=c(3e-3, 3e-3, 3e-3),
locusAdjustment=1,
power=-4.35,
dropout=c(0.175, 0.105) )
objectives = c(2.01470581026018e-03, 1.13394042752114e-02, 4.69287546906059e-03,
2.47293709587760e-04, 5.90787709164540e-02, 9.97729081224379e-07,
6.25814358291258e-06, 1.04230422586896e-04, 1.46838789834590e-02,
1.43741838557565e-03)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, objectives)
checkEquals(prod(result$objectives * result$penalties), 6.97617046679607e-32)
})
test_regression.oneunknown <- svTest(function() {
# Case we are going to be looking at.
datapath = system.file(file.path('extdata', 'hammer'), package="likeLTD")
args = list(
databaseFile = NULL,
kit = "SGMplus",
cspFile = file.path(datapath, 'hammer-CSP.csv'),
refFile = file.path(datapath, 'hammer-reference.csv'),
nUnknowns = 1,
doDropin = TRUE,
ethnic = "EA1",
adj = 1.0,
fst = 0.02,
relatedness = c(0.0, 0),
combineRare = FALSE
)
defenceHypothesis = do.call(defence.hypothesis, args)
prosecutionHypothesis = do.call(prosecution.hypothesis, args)
likelihood <- create.likelihood.vectors(prosecutionHypothesis)
arguments = list(rcont=c(0.923913043478261, 0.565217391304348, 1.0,
0.543478260869565),
dropin = 0.108695652173913,
degradation=rep(3e-3, 4),
locusAdjustment=1,
power=-4.35,
dropout=c(0.15, 0.01) )
objectives = c(5.24609987450467e-05, 1.28369768074416e-01, 3.19566044368621e-02,
9.37696749226556e-04, 3.54990168257584e-01, 3.61714632221897e-05,
2.78024377866913e-06, 7.42370399489160e-02, 2.15249772203121e-02,
4.27467268573391e-04)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, objectives)
checkEquals(prod(result$objectives * result$penalties), 1.9762409429182e-25)
likelihood <- create.likelihood.vectors(defenceHypothesis)
arguments = list(rcont=c(0.923913043478261, 0.565217391304348, 1.0,
0.543478260869565),
dropin = 0.108695652173913,
degradation=rep(3e-3, 4),
locusAdjustment=1,
power=-4.35,
dropout=c(0.175, 0.105) )
objectives = c(5.40928563410066e-04, 1.24028883413425e-02, 4.32400562747164e-03,
3.71369198539134e-04, 7.20410130310739e-02, 1.08896493758788e-06,
2.81245481065190e-06, 1.83320557026132e-04, 1.94351600773495e-02,
5.75805705402794e-04)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, objectives)
checkEquals(prod(result$objectives * result$penalties), 3.63417836957934e-31)
})
test_regression.relatedness <- svTest(function() {
# Case we are going to be looking at.
datapath = system.file(file.path('extdata', 'hammer'), package="likeLTD")
args = list(
databaseFile = NULL,
kit = "SGMplus",
cspFile = file.path(datapath, 'hammer-CSP.csv'),
refFile = file.path(datapath, 'hammer-reference.csv'),
nUnknowns = 1,
doDropin = TRUE,
ethnic = "EA1",
adj = 1.0,
fst = 0.02,
relationship = 2,
combineRare = FALSE
)
defenceHypothesis = do.call(defence.hypothesis, args)
prosecutionHypothesis = do.call(prosecution.hypothesis, args)
likelihood <- create.likelihood.vectors(prosecutionHypothesis)
arguments = list(rcont=c(0.923913043478261, 0.565217391304348, 1.0,
0.543478260869565),
dropin = 0.108695652173913,
degradation=rep(3e-3, 4),
locusAdjustment=rep(1,times=10),
power=-4.35,
dropout=c(0.15, 0.01) )
objectives = c(5.2460998745046723702e-05, 1.2836976807441610737e-01, 3.1956604436862108554e-02,
9.3769674922655570342e-04, 3.5499016825758356042e-01, 3.6171463222189653881e-05,
2.7802437786691249654e-06, 7.4237039948916033749e-02, 2.1524977220312099119e-02,
4.2746726857339074597e-04)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
penalty = 2.6078646479705969163
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, objectives)
checkEquals(prod(result$objectives * result$penalties), prod(objectives * penalty), checkNames=FALSE)
# old result was 1.9762409429182e-25 (different penalties)
likelihood <- create.likelihood.vectors(defenceHypothesis)
arguments = list(rcont=c(0.923913043478261, 0.565217391304348, 1.0,
0.543478260869565),
dropin = 0.108695652173913,
degradation=rep(3e-3, 4),
locusAdjustment=rep(1,times=10),
power=-4.35,
dropout=c(0.175, 0.105) )
objectives = c(2.0324320110552429076e-03, 4.0692306250085193142e-02, 4.4262872552010612548e-02,
1.5005784145824823013e-03, 1.9188770082595565936e-01, 7.4396398032556112094e-06,
2.1466961591367787368e-06, 1.3734648258538964885e-02, 5.5349543045990240442e-02,
2.2848765693775441657e-03)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
penalty = 2.6078646479705969163
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, objectives)
checkEquals(prod(result$objectives * result$penalties), prod(objectives * penalty), checkNames=FALSE)
# old result was 5.75589320779353e-25 (different penalties)
defenceHypothesis$relationship = 2
likelihood <- create.likelihood.vectors(defenceHypothesis)
# This also tests that refIndiv works. The objective function should be
# inserting 1 at position 3 in rcont.
arguments = list(rcont=c(0.923913043478261, 0.565217391304348,
0.543478260869565),
dropin = 0.108695652173913,
degradation=rep(3e-3, 4),
locusAdjustment=rep(1,times=10),
power=-4.35,
dropout=c(0.175, 0.105) )
objectives = c(2.0324320110552429076e-03, 4.0692306250085193142e-02, 4.4262872552010612548e-02,
1.5005784145824823013e-03, 1.9188770082595565936e-01, 7.4396398032556112094e-06,
2.1466961591367787368e-06, 1.3734648258538964885e-02, 5.5349543045990240442e-02,
2.2848765693775441657e-03)
names(objectives) = c("D3S1358", "vWA", "D16S539", "D2S1338", "D8S1179", "D21S11", "D18S51", "D19S433",
"TH01", "FGA")
penalty = 2.6078646479705969163
result <- do.call(likelihood, arguments)
checkEquals(result$objectives, objectives)
checkEquals(prod(result$objectives * result$penalties), prod(objectives * penalty), checkNames=FALSE)
# old result was 7.07569814196917e-26 (different penalties)
})
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