tests/testthat/test-blooddata.R
In mathesong/kinfitr: Kinetic Modelling of PET Time Activity Curves
context("test-blooddata")
data(pbr28)
data(oldbids_json)
suppressMessages(
blooddata_old <- create_blooddata_bids(oldbids_json) )
blooddata <- pbr28$blooddata[[1]]
blooddata <- bd_blood_dispcor(blooddata)
test_that("plotting blooddata works", {
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("creating blooddata from vectors works", {
blooddata2 <- create_blooddata_components(
Blood.Discrete.Values.time =
blooddata$Data$Blood$Discrete$Values$time,
Blood.Discrete.Values.activity =
blooddata$Data$Blood$Discrete$Values$activity,
Plasma.Values.time =
blooddata$Data$Plasma$Values$time,
Plasma.Values.activity =
blooddata$Data$Plasma$Values$activity,
Metabolite.Values.time =
blooddata$Data$Metabolite$Values$time,
Metabolite.Values.parentFraction =
blooddata$Data$Metabolite$Values$parentFraction,
Blood.Continuous.Values.time =
blooddata$Data$Blood$Continuous$Values$time,
Blood.Continuous.Values.activity =
blooddata$Data$Blood$Continuous$Values$activity,
Blood.Continuous.DispersionConstant =
blooddata$Data$Blood$Continuous$DispersionConstant,
Blood.Continuous.DispersionCorrected = FALSE,
TimeShift = 0)
expect_true(class(blooddata2) == "blooddata")
bdplot <- plot(blooddata2)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("blooddata from vectors with no continuous works", {
blood_discrete <- tibble::tibble(
time = c(blooddata$Data$Blood$Discrete$Values$time, # d & c as discrete
blooddata$Data$Blood$Continuous$Values$time),
activity = c(blooddata$Data$Blood$Discrete$Values$activity, # d & c as discrete
blooddata$Data$Blood$Continuous$Values$activity)
) %>%
dplyr::distinct(time, .keep_all = TRUE)
blooddata2 <- create_blooddata_components(
Blood.Discrete.Values.time =
blood_discrete$time,
Blood.Discrete.Values.activity =
blood_discrete$activity,
Plasma.Values.time =
blooddata$Data$Plasma$Values$time,
Plasma.Values.activity =
blooddata$Data$Plasma$Values$activity,
Metabolite.Values.time =
blooddata$Data$Metabolite$Values$time,
Metabolite.Values.parentFraction =
blooddata$Data$Metabolite$Values$parentFraction,
#Blood.Continuous.Values.time =
# blooddata$Data$Blood$Continuous$Values$time,
#Blood.Continuous.Values.activity =
# blooddata$Data$Blood$Continuous$Values$activity,
#Blood.Continuous.DispersionConstant =
# blooddata$Data$Blood$Continuous$DispersionConstant,
#Blood.Continuous.DispersionCorrected = FALSE,
TimeShift = 0)
expect_true(class(blooddata2) == "blooddata")
bdplot <- plot(blooddata2)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("blooddata with missing plasma works", {
blooddata2 <- create_blooddata_components(
Blood.Discrete.Values.time =
blooddata$Data$Blood$Discrete$Values$time,
Blood.Discrete.Values.activity =
blooddata$Data$Blood$Discrete$Values$activity,
#Plasma.Values.time =
# blooddata$Data$Plasma$Values$time,
#Plasma.Values.activity =
# blooddata$Data$Plasma$Values$activity,
Metabolite.Values.time =
blooddata$Data$Metabolite$Values$time,
Metabolite.Values.parentFraction =
blooddata$Data$Metabolite$Values$parentFraction,
Blood.Continuous.Values.time =
blooddata$Data$Blood$Continuous$Values$time,
Blood.Continuous.Values.activity =
blooddata$Data$Blood$Continuous$Values$activity,
Blood.Continuous.DispersionConstant =
blooddata$Data$Blood$Continuous$DispersionConstant,
Blood.Continuous.DispersionCorrected = FALSE,
TimeShift = 0)
expect_true(class(blooddata2) == "blooddata")
bdplot <- plot(blooddata2)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("blooddata with missing metabolite works", {
blooddata2 <- create_blooddata_components(
Blood.Discrete.Values.time =
blooddata$Data$Blood$Discrete$Values$time,
Blood.Discrete.Values.activity =
blooddata$Data$Blood$Discrete$Values$activity,
Plasma.Values.time =
blooddata$Data$Plasma$Values$time,
Plasma.Values.activity =
blooddata$Data$Plasma$Values$activity,
# Metabolite.Values.time =
# blooddata$Data$Metabolite$Values$time,
# Metabolite.Values.parentFraction =
# blooddata$Data$Metabolite$Values$parentFraction,
Blood.Continuous.Values.time =
blooddata$Data$Blood$Continuous$Values$time,
Blood.Continuous.Values.activity =
blooddata$Data$Blood$Continuous$Values$activity,
Blood.Continuous.DispersionConstant =
blooddata$Data$Blood$Continuous$DispersionConstant,
Blood.Continuous.DispersionCorrected = FALSE,
TimeShift = 0)
expect_true(class(blooddata2) == "blooddata")
bdplot <- plot(blooddata2)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("blooddata with missing WB works", {
blooddata2 <- create_blooddata_components(
#Blood.Discrete.Values.time =
# blooddata$Data$Blood$Discrete$Values$time,
#Blood.Discrete.Values.activity =
# blooddata$Data$Blood$Discrete$Values$activity,
Plasma.Values.time =
blooddata$Data$Plasma$Values$time,
Plasma.Values.activity =
blooddata$Data$Plasma$Values$activity,
Metabolite.Values.time =
blooddata$Data$Metabolite$Values$time,
Metabolite.Values.parentFraction =
blooddata$Data$Metabolite$Values$parentFraction,
# Blood.Continuous.Values.time =
# blooddata$Data$Blood$Continuous$Values$time,
# Blood.Continuous.Values.activity =
# blooddata$Data$Blood$Continuous$Values$activity,
# Blood.Continuous.DispersionConstant =
# blooddata$Data$Blood$Continuous$DispersionConstant,
# Blood.Continuous.DispersionCorrected = FALSE,
TimeShift = 0)
expect_true(class(blooddata2) == "blooddata")
bdplot <- plot(blooddata2)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("blooddata with missing WB and metabolite works", {
blooddata2 <- create_blooddata_components(
#Blood.Discrete.Values.time =
# blooddata$Data$Blood$Discrete$Values$time,
#Blood.Discrete.Values.activity =
# blooddata$Data$Blood$Discrete$Values$activity,
Plasma.Values.time =
blooddata$Data$Plasma$Values$time,
Plasma.Values.activity =
blooddata$Data$Plasma$Values$activity,
# Metabolite.Values.time =
# blooddata$Data$Metabolite$Values$time,
# Metabolite.Values.parentFraction =
# blooddata$Data$Metabolite$Values$parentFraction,
# Blood.Continuous.Values.time =
# blooddata$Data$Blood$Continuous$Values$time,
# Blood.Continuous.Values.activity =
# blooddata$Data$Blood$Continuous$Values$activity,
# Blood.Continuous.DispersionConstant =
# blooddata$Data$Blood$Continuous$DispersionConstant,
# Blood.Continuous.DispersionCorrected = FALSE,
TimeShift = 0)
expect_true(class(blooddata2) == "blooddata")
bdplot <- plot(blooddata2)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("updating blooddata works", {
blooddata2 <- update_blooddata(blooddata_old)
expect_true(class(blooddata2) == "blooddata")
bdplot <- plot(blooddata2)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("getting data from blooddata works", {
blood <- bd_extract(blooddata, output = "Blood")
expect_true(any(class(blood) == "tbl"))
bpr <- bd_extract(blooddata, output = "BPR")
expect_true(any(class(bpr) == "tbl"))
pf <- bd_extract(blooddata, output = "parentFraction")
expect_true(any(class(pf) == "tbl"))
aif <- bd_extract(blooddata, output = "AIF")
expect_true(any(class(aif) == "tbl"))
})
test_that("getting input data from blooddata works", {
input <- bd_create_input(blooddata)
expect_true(any(class(input) == "interpblood"))
})
test_that("addfit works", {
pf <- bd_extract(blooddata, output = "parentFraction")
pf_fit <- metab_sigmoid(pf$time, pf$parentFraction)
blooddata <- bd_addfit(blooddata, fit = pf_fit, modeltype = "parentFraction")
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("addfitted works", {
pf <- bd_extract(blooddata, output = "parentFraction")
pf_fit <- metab_sigmoid(pf$time, pf$parentFraction)
fitted <- tibble::tibble(
time = seq(min(pf$time), max(pf$time), length.out = 100)
)
fitted$pred <- predict(pf_fit, newdata = list(time = fitted$time))
blooddata <- bd_addfitted(blooddata,
time = fitted$time,
predicted = fitted$pred,
modeltype = "parentFraction"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("addfitpars works", {
pf <- bd_extract(blooddata, output = "parentFraction")
pf_fit <- metab_sigmoid(pf$time, pf$parentFraction)
fitpars <- as.list(coef(pf_fit))
blooddata <- bd_addfitpars(blooddata,
modelname = "metab_sigmoid_model", fitpars = fitpars,
modeltype = "parentFraction"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
# Blood/AIF Model Tests ----------------------------------------------
test_that("bloodsplines works", {
blood <- bd_extract(blooddata, output = "Blood")
blood_fit <- blmod_splines(blood$time,
blood$activity,
Method = blood$Method
)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "Blood"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("starting parameters for expontial when AIF contains zeros works", {
aif <- bd_extract(blooddata, output = "AIF")
aif$aif[100] <- 0
aif$aif[500] <- 0
aif$aif[length(aif$aif)] <- -1
aif$aif[length(aif$aif)-1] <- 0
start <- blmod_exp_startpars(aif$time,
aif$aif,
fit_exp3 = T,
expdecay_props = c(1/60, 0.1))
expect_true(any(class(start) == "list"))
})
test_that("exponential works", {
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_exp(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("exponential 2exp works", {
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_exp(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1, fit_exp3 = F)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("exponential peakfitting works", {
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_exp(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1,
fit_peaktime = T, fit_peakval = T)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("exponential without method works", {
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_exp(aif$time,
aif$aif,
multstart_iter = 1)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("exponential with start parameters works", {
aif <- bd_extract(blooddata, output = "AIF")
startpars <- blmod_exp_startpars(aif$time,
aif$aif)
blood_fit <- blmod_exp(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1,
start = startpars)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("Feng works", {
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_feng(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("Feng works with startpars", {
aif <- bd_extract(blooddata, output = "AIF")
startpars <- blmod_feng_startpars(aif$time,
aif$aif)
blood_fit <- blmod_feng(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1,
start = startpars)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("Fengconv works", {
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_fengconv(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("Fengconv works with startpars", {
aif <- bd_extract(blooddata, output = "AIF")
startpars <- blmod_feng_startpars(aif$time,
aif$aif)
blood_fit <- blmod_fengconv(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1,
start = startpars)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("Fengconvplus works", {
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_fengconvplus(aif$time,
aif$aif,
Method = aif$Method,
inftime = 20,
multstart_iter = 1)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("Fengconv works with startpars", {
aif <- bd_extract(blooddata, output = "AIF")
startpars <- blmod_feng_startpars(aif$time,
aif$aif)
blood_fit <- blmod_fengconvplus(aif$time,
aif$aif,
Method = aif$Method,
inftime = 20,
multstart_iter = 1,
start = startpars)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
# Parent Fraction Model Tests ----------------------------------------------
pf <- bd_extract(blooddata, "parentFraction")
set.seed(12345)
test_that("hill function works", {
fit <- metab_hill(pf$time, pf$parentFraction)
expect_true(any(class(fit) == "nls"))
})
test_that("exponential function works", {
fit <- metab_exponential(pf$time, pf$parentFraction)
expect_true(any(class(fit) == "nls"))
})
test_that("power function works", {
fit <- metab_power(pf$time, pf$parentFraction)
expect_true(any(class(fit) == "nls"))
})
test_that("sigmoid function works", {
fit <- metab_sigmoid(pf$time, pf$parentFraction)
expect_true(any(class(fit) == "nls"))
})
test_that("gamma function works", {
fit <- metab_gamma(pf$time, pf$parentFraction)
expect_true(any(class(fit) == "nls"))
})
test_that("invgamma function works", {
fit <- metab_invgamma(pf$time, pf$parentFraction)
expect_true(any(class(fit) == "nls"))
})
# test_that("exp_sep works", {
#
# aif <- bd_extract(blooddata, output = "AIF")
#
# blood_fit <- blmod_exp_sep(aif$time,
# aif$aif,
# Method = aif$Method,
# multstart_iter = 1)
#
# blooddata <- bd_addfit(blooddata,
# fit = blood_fit,
# modeltype = "AIF"
# )
#
# bdplot <- plot(blooddata)
#
# expect_true(any(class(bdplot) == "ggplot"))
# })
# test_that("exp_sep without method works", {
#
# aif <- bd_extract(blooddata, output = "AIF")
#
# blood_fit <- blmod_exp_sep(aif$time,
# aif$aif,
# multstart_iter = 1)
#
# blooddata <- bd_addfit(blooddata,
# fit = blood_fit,
# modeltype = "AIF"
# )
#
# bdplot <- plot(blooddata)
#
# expect_true(any(class(bdplot) == "ggplot"))
# })
# test_that("exp_sep with start parameters works", {
#
# aif <- bd_extract(blooddata, output = "AIF")
#
# startpars <- blmod_exp_startpars(aif$time,
# aif$aif)
#
# blood_fit <- blmod_exp_sep(aif$time,
# aif$aif,
# Method = aif$Method,
# multstart_iter = 1,
# start = startpars)
#
# blooddata <- bd_addfit(blooddata,
# fit = blood_fit,
# modeltype = "AIF"
# )
#
# bdplot <- plot(blooddata)
#
# expect_true(any(class(bdplot) == "ggplot"))
# })
test_that("dispcor with different intervals works", {
time <- 1:20
activity <- rnorm(20)
tau <- 2.5
time <- time[-c(15, 17, 19)]
activity <- activity[-c(15, 17, 19)]
out <- blood_dispcor(time, activity, tau, keep_interpolated = T)
expect_true(nrow(out)==20)
})
test_that("dispcor with different intervals works with orig times", {
time <- 1:20
activity <- rnorm(20)
tau <- 2.5
time <- time[-c(15, 17, 19)]
activity <- activity[-c(15, 17, 19)]
out <- blood_dispcor(time, activity, tau, keep_interpolated = F)
expect_true(nrow(out)==17)
})
# Missing blood measurements and BPR--------------------------------------------
blooddata_pl <- blooddata
blooddata_pl$Data$Blood$Discrete$Values <- blooddata_pl$Data$Blood$Discrete$Values[sample(1:18, 5),]
test_that("plotting blooddata with missing blood samples works", {
bdplot <- plot(blooddata_pl)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("addfit bpr with missing blood samples works", {
bpr <- bd_extract(blooddata_pl, output = "BPR")
bpr_fit <- lm(bpr ~ time, data=bpr)
blooddata_pl <- bd_addfit(blooddata_pl, fit = bpr_fit, modeltype = "BPR")
bdplot <- plot(blooddata_pl)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("addfitted bpr with missing blood samples works", {
bpr <- bd_extract(blooddata_pl, output = "BPR")
bpr_fit <- lm(bpr ~ time, data=bpr)
fitted <- tibble::tibble(
time = seq(min(bpr$time), max(bpr$time), length.out = 100)
)
fitted$pred <- predict(bpr_fit, newdata = list(time = fitted$time))
blooddata_pl <- bd_addfitted(blooddata_pl,
time = fitted$time,
predicted = fitted$pred,
modeltype = "BPR"
)
bdplot <- plot(blooddata_pl)
expect_true(any(class(bdplot) == "ggplot"))
})
mathesong/kinfitr documentation built on Nov. 27, 2024, 1:38 a.m.
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context("test-blooddata")
data(pbr28)
data(oldbids_json)
suppressMessages(
blooddata_old <- create_blooddata_bids(oldbids_json) )
blooddata <- pbr28$blooddata[[1]]
blooddata <- bd_blood_dispcor(blooddata)
test_that("plotting blooddata works", {
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("creating blooddata from vectors works", {
blooddata2 <- create_blooddata_components(
Blood.Discrete.Values.time =
blooddata$Data$Blood$Discrete$Values$time,
Blood.Discrete.Values.activity =
blooddata$Data$Blood$Discrete$Values$activity,
Plasma.Values.time =
blooddata$Data$Plasma$Values$time,
Plasma.Values.activity =
blooddata$Data$Plasma$Values$activity,
Metabolite.Values.time =
blooddata$Data$Metabolite$Values$time,
Metabolite.Values.parentFraction =
blooddata$Data$Metabolite$Values$parentFraction,
Blood.Continuous.Values.time =
blooddata$Data$Blood$Continuous$Values$time,
Blood.Continuous.Values.activity =
blooddata$Data$Blood$Continuous$Values$activity,
Blood.Continuous.DispersionConstant =
blooddata$Data$Blood$Continuous$DispersionConstant,
Blood.Continuous.DispersionCorrected = FALSE,
TimeShift = 0)
expect_true(class(blooddata2) == "blooddata")
bdplot <- plot(blooddata2)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("blooddata from vectors with no continuous works", {
blood_discrete <- tibble::tibble(
time = c(blooddata$Data$Blood$Discrete$Values$time, # d & c as discrete
blooddata$Data$Blood$Continuous$Values$time),
activity = c(blooddata$Data$Blood$Discrete$Values$activity, # d & c as discrete
blooddata$Data$Blood$Continuous$Values$activity)
) %>%
dplyr::distinct(time, .keep_all = TRUE)
blooddata2 <- create_blooddata_components(
Blood.Discrete.Values.time =
blood_discrete$time,
Blood.Discrete.Values.activity =
blood_discrete$activity,
Plasma.Values.time =
blooddata$Data$Plasma$Values$time,
Plasma.Values.activity =
blooddata$Data$Plasma$Values$activity,
Metabolite.Values.time =
blooddata$Data$Metabolite$Values$time,
Metabolite.Values.parentFraction =
blooddata$Data$Metabolite$Values$parentFraction,
#Blood.Continuous.Values.time =
# blooddata$Data$Blood$Continuous$Values$time,
#Blood.Continuous.Values.activity =
# blooddata$Data$Blood$Continuous$Values$activity,
#Blood.Continuous.DispersionConstant =
# blooddata$Data$Blood$Continuous$DispersionConstant,
#Blood.Continuous.DispersionCorrected = FALSE,
TimeShift = 0)
expect_true(class(blooddata2) == "blooddata")
bdplot <- plot(blooddata2)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("blooddata with missing plasma works", {
blooddata2 <- create_blooddata_components(
Blood.Discrete.Values.time =
blooddata$Data$Blood$Discrete$Values$time,
Blood.Discrete.Values.activity =
blooddata$Data$Blood$Discrete$Values$activity,
#Plasma.Values.time =
# blooddata$Data$Plasma$Values$time,
#Plasma.Values.activity =
# blooddata$Data$Plasma$Values$activity,
Metabolite.Values.time =
blooddata$Data$Metabolite$Values$time,
Metabolite.Values.parentFraction =
blooddata$Data$Metabolite$Values$parentFraction,
Blood.Continuous.Values.time =
blooddata$Data$Blood$Continuous$Values$time,
Blood.Continuous.Values.activity =
blooddata$Data$Blood$Continuous$Values$activity,
Blood.Continuous.DispersionConstant =
blooddata$Data$Blood$Continuous$DispersionConstant,
Blood.Continuous.DispersionCorrected = FALSE,
TimeShift = 0)
expect_true(class(blooddata2) == "blooddata")
bdplot <- plot(blooddata2)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("blooddata with missing metabolite works", {
blooddata2 <- create_blooddata_components(
Blood.Discrete.Values.time =
blooddata$Data$Blood$Discrete$Values$time,
Blood.Discrete.Values.activity =
blooddata$Data$Blood$Discrete$Values$activity,
Plasma.Values.time =
blooddata$Data$Plasma$Values$time,
Plasma.Values.activity =
blooddata$Data$Plasma$Values$activity,
# Metabolite.Values.time =
# blooddata$Data$Metabolite$Values$time,
# Metabolite.Values.parentFraction =
# blooddata$Data$Metabolite$Values$parentFraction,
Blood.Continuous.Values.time =
blooddata$Data$Blood$Continuous$Values$time,
Blood.Continuous.Values.activity =
blooddata$Data$Blood$Continuous$Values$activity,
Blood.Continuous.DispersionConstant =
blooddata$Data$Blood$Continuous$DispersionConstant,
Blood.Continuous.DispersionCorrected = FALSE,
TimeShift = 0)
expect_true(class(blooddata2) == "blooddata")
bdplot <- plot(blooddata2)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("blooddata with missing WB works", {
blooddata2 <- create_blooddata_components(
#Blood.Discrete.Values.time =
# blooddata$Data$Blood$Discrete$Values$time,
#Blood.Discrete.Values.activity =
# blooddata$Data$Blood$Discrete$Values$activity,
Plasma.Values.time =
blooddata$Data$Plasma$Values$time,
Plasma.Values.activity =
blooddata$Data$Plasma$Values$activity,
Metabolite.Values.time =
blooddata$Data$Metabolite$Values$time,
Metabolite.Values.parentFraction =
blooddata$Data$Metabolite$Values$parentFraction,
# Blood.Continuous.Values.time =
# blooddata$Data$Blood$Continuous$Values$time,
# Blood.Continuous.Values.activity =
# blooddata$Data$Blood$Continuous$Values$activity,
# Blood.Continuous.DispersionConstant =
# blooddata$Data$Blood$Continuous$DispersionConstant,
# Blood.Continuous.DispersionCorrected = FALSE,
TimeShift = 0)
expect_true(class(blooddata2) == "blooddata")
bdplot <- plot(blooddata2)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("blooddata with missing WB and metabolite works", {
blooddata2 <- create_blooddata_components(
#Blood.Discrete.Values.time =
# blooddata$Data$Blood$Discrete$Values$time,
#Blood.Discrete.Values.activity =
# blooddata$Data$Blood$Discrete$Values$activity,
Plasma.Values.time =
blooddata$Data$Plasma$Values$time,
Plasma.Values.activity =
blooddata$Data$Plasma$Values$activity,
# Metabolite.Values.time =
# blooddata$Data$Metabolite$Values$time,
# Metabolite.Values.parentFraction =
# blooddata$Data$Metabolite$Values$parentFraction,
# Blood.Continuous.Values.time =
# blooddata$Data$Blood$Continuous$Values$time,
# Blood.Continuous.Values.activity =
# blooddata$Data$Blood$Continuous$Values$activity,
# Blood.Continuous.DispersionConstant =
# blooddata$Data$Blood$Continuous$DispersionConstant,
# Blood.Continuous.DispersionCorrected = FALSE,
TimeShift = 0)
expect_true(class(blooddata2) == "blooddata")
bdplot <- plot(blooddata2)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("updating blooddata works", {
blooddata2 <- update_blooddata(blooddata_old)
expect_true(class(blooddata2) == "blooddata")
bdplot <- plot(blooddata2)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("getting data from blooddata works", {
blood <- bd_extract(blooddata, output = "Blood")
expect_true(any(class(blood) == "tbl"))
bpr <- bd_extract(blooddata, output = "BPR")
expect_true(any(class(bpr) == "tbl"))
pf <- bd_extract(blooddata, output = "parentFraction")
expect_true(any(class(pf) == "tbl"))
aif <- bd_extract(blooddata, output = "AIF")
expect_true(any(class(aif) == "tbl"))
})
test_that("getting input data from blooddata works", {
input <- bd_create_input(blooddata)
expect_true(any(class(input) == "interpblood"))
})
test_that("addfit works", {
pf <- bd_extract(blooddata, output = "parentFraction")
pf_fit <- metab_sigmoid(pf$time, pf$parentFraction)
blooddata <- bd_addfit(blooddata, fit = pf_fit, modeltype = "parentFraction")
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("addfitted works", {
pf <- bd_extract(blooddata, output = "parentFraction")
pf_fit <- metab_sigmoid(pf$time, pf$parentFraction)
fitted <- tibble::tibble(
time = seq(min(pf$time), max(pf$time), length.out = 100)
)
fitted$pred <- predict(pf_fit, newdata = list(time = fitted$time))
blooddata <- bd_addfitted(blooddata,
time = fitted$time,
predicted = fitted$pred,
modeltype = "parentFraction"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("addfitpars works", {
pf <- bd_extract(blooddata, output = "parentFraction")
pf_fit <- metab_sigmoid(pf$time, pf$parentFraction)
fitpars <- as.list(coef(pf_fit))
blooddata <- bd_addfitpars(blooddata,
modelname = "metab_sigmoid_model", fitpars = fitpars,
modeltype = "parentFraction"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
# Blood/AIF Model Tests ----------------------------------------------
test_that("bloodsplines works", {
blood <- bd_extract(blooddata, output = "Blood")
blood_fit <- blmod_splines(blood$time,
blood$activity,
Method = blood$Method
)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "Blood"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("starting parameters for expontial when AIF contains zeros works", {
aif <- bd_extract(blooddata, output = "AIF")
aif$aif[100] <- 0
aif$aif[500] <- 0
aif$aif[length(aif$aif)] <- -1
aif$aif[length(aif$aif)-1] <- 0
start <- blmod_exp_startpars(aif$time,
aif$aif,
fit_exp3 = T,
expdecay_props = c(1/60, 0.1))
expect_true(any(class(start) == "list"))
})
test_that("exponential works", {
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_exp(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("exponential 2exp works", {
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_exp(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1, fit_exp3 = F)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("exponential peakfitting works", {
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_exp(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1,
fit_peaktime = T, fit_peakval = T)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("exponential without method works", {
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_exp(aif$time,
aif$aif,
multstart_iter = 1)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("exponential with start parameters works", {
aif <- bd_extract(blooddata, output = "AIF")
startpars <- blmod_exp_startpars(aif$time,
aif$aif)
blood_fit <- blmod_exp(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1,
start = startpars)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("Feng works", {
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_feng(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("Feng works with startpars", {
aif <- bd_extract(blooddata, output = "AIF")
startpars <- blmod_feng_startpars(aif$time,
aif$aif)
blood_fit <- blmod_feng(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1,
start = startpars)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("Fengconv works", {
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_fengconv(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("Fengconv works with startpars", {
aif <- bd_extract(blooddata, output = "AIF")
startpars <- blmod_feng_startpars(aif$time,
aif$aif)
blood_fit <- blmod_fengconv(aif$time,
aif$aif,
Method = aif$Method,
multstart_iter = 1,
start = startpars)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("Fengconvplus works", {
aif <- bd_extract(blooddata, output = "AIF")
blood_fit <- blmod_fengconvplus(aif$time,
aif$aif,
Method = aif$Method,
inftime = 20,
multstart_iter = 1)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("Fengconv works with startpars", {
aif <- bd_extract(blooddata, output = "AIF")
startpars <- blmod_feng_startpars(aif$time,
aif$aif)
blood_fit <- blmod_fengconvplus(aif$time,
aif$aif,
Method = aif$Method,
inftime = 20,
multstart_iter = 1,
start = startpars)
blooddata <- bd_addfit(blooddata,
fit = blood_fit,
modeltype = "AIF"
)
bdplot <- plot(blooddata)
expect_true(any(class(bdplot) == "ggplot"))
})
# Parent Fraction Model Tests ----------------------------------------------
pf <- bd_extract(blooddata, "parentFraction")
set.seed(12345)
test_that("hill function works", {
fit <- metab_hill(pf$time, pf$parentFraction)
expect_true(any(class(fit) == "nls"))
})
test_that("exponential function works", {
fit <- metab_exponential(pf$time, pf$parentFraction)
expect_true(any(class(fit) == "nls"))
})
test_that("power function works", {
fit <- metab_power(pf$time, pf$parentFraction)
expect_true(any(class(fit) == "nls"))
})
test_that("sigmoid function works", {
fit <- metab_sigmoid(pf$time, pf$parentFraction)
expect_true(any(class(fit) == "nls"))
})
test_that("gamma function works", {
fit <- metab_gamma(pf$time, pf$parentFraction)
expect_true(any(class(fit) == "nls"))
})
test_that("invgamma function works", {
fit <- metab_invgamma(pf$time, pf$parentFraction)
expect_true(any(class(fit) == "nls"))
})
# test_that("exp_sep works", {
#
# aif <- bd_extract(blooddata, output = "AIF")
#
# blood_fit <- blmod_exp_sep(aif$time,
# aif$aif,
# Method = aif$Method,
# multstart_iter = 1)
#
# blooddata <- bd_addfit(blooddata,
# fit = blood_fit,
# modeltype = "AIF"
# )
#
# bdplot <- plot(blooddata)
#
# expect_true(any(class(bdplot) == "ggplot"))
# })
# test_that("exp_sep without method works", {
#
# aif <- bd_extract(blooddata, output = "AIF")
#
# blood_fit <- blmod_exp_sep(aif$time,
# aif$aif,
# multstart_iter = 1)
#
# blooddata <- bd_addfit(blooddata,
# fit = blood_fit,
# modeltype = "AIF"
# )
#
# bdplot <- plot(blooddata)
#
# expect_true(any(class(bdplot) == "ggplot"))
# })
# test_that("exp_sep with start parameters works", {
#
# aif <- bd_extract(blooddata, output = "AIF")
#
# startpars <- blmod_exp_startpars(aif$time,
# aif$aif)
#
# blood_fit <- blmod_exp_sep(aif$time,
# aif$aif,
# Method = aif$Method,
# multstart_iter = 1,
# start = startpars)
#
# blooddata <- bd_addfit(blooddata,
# fit = blood_fit,
# modeltype = "AIF"
# )
#
# bdplot <- plot(blooddata)
#
# expect_true(any(class(bdplot) == "ggplot"))
# })
test_that("dispcor with different intervals works", {
time <- 1:20
activity <- rnorm(20)
tau <- 2.5
time <- time[-c(15, 17, 19)]
activity <- activity[-c(15, 17, 19)]
out <- blood_dispcor(time, activity, tau, keep_interpolated = T)
expect_true(nrow(out)==20)
})
test_that("dispcor with different intervals works with orig times", {
time <- 1:20
activity <- rnorm(20)
tau <- 2.5
time <- time[-c(15, 17, 19)]
activity <- activity[-c(15, 17, 19)]
out <- blood_dispcor(time, activity, tau, keep_interpolated = F)
expect_true(nrow(out)==17)
})
# Missing blood measurements and BPR--------------------------------------------
blooddata_pl <- blooddata
blooddata_pl$Data$Blood$Discrete$Values <- blooddata_pl$Data$Blood$Discrete$Values[sample(1:18, 5),]
test_that("plotting blooddata with missing blood samples works", {
bdplot <- plot(blooddata_pl)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("addfit bpr with missing blood samples works", {
bpr <- bd_extract(blooddata_pl, output = "BPR")
bpr_fit <- lm(bpr ~ time, data=bpr)
blooddata_pl <- bd_addfit(blooddata_pl, fit = bpr_fit, modeltype = "BPR")
bdplot <- plot(blooddata_pl)
expect_true(any(class(bdplot) == "ggplot"))
})
test_that("addfitted bpr with missing blood samples works", {
bpr <- bd_extract(blooddata_pl, output = "BPR")
bpr_fit <- lm(bpr ~ time, data=bpr)
fitted <- tibble::tibble(
time = seq(min(bpr$time), max(bpr$time), length.out = 100)
)
fitted$pred <- predict(bpr_fit, newdata = list(time = fitted$time))
blooddata_pl <- bd_addfitted(blooddata_pl,
time = fitted$time,
predicted = fitted$pred,
modeltype = "BPR"
)
bdplot <- plot(blooddata_pl)
expect_true(any(class(bdplot) == "ggplot"))
})
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