tests/testthat/test_helpers.R
In JetiLab/blotIt: Software set for alignment of biological replicate data
# getSymbols() -----------------------------------------------------------
test_that("getSymbols()", {
expect_equal(
getSymbols("left1 ~ right11 + right12"),
c("left1", "right11", "right12")
)
expect_equal(
getSymbols(
"left2 ~ (right21 * right22 / (right23 * right24)) - right25"
),
c("left2", "right21", "right22", "right23", "right24", "right25")
)
})
# splitForScaling() -----------------------------------------------------
test_that("splitForScaling()", {
simDataWideFile <- system.file(
"extdata", "simDataWide.csv",
package = "blotIt"
)
sim_data_long <- readWide(simDataWideFile, description = seq_len(3))
effectsValues <- list(
biologicaValues = c("name", "time", "condition"),
scalingValues = c("name", "ID"),
errorValues = "name"
)
effectsValues_1 <- list(
biologicaValues = c("name", "time", "condition"),
scalingValues = c("name"),
errorValues = "name"
)
effectsValues_2 <- list(
biologicaValues = c("name", "time", "condition"),
scalingValues = c("ID"),
errorValues = "name"
)
expect_equal(
length(
splitForScaling(
data = sim_data_long,
effectsValues = effectsValues,
normalizeInput = TRUE
)
),
14
)
expect_equal(
length(
splitForScaling(
data = sim_data_long,
effectsValues = effectsValues_1,
normalizeInput = TRUE
)
),
nrow(unique(sim_data_long["name"]))
)
expect_equal(
length(
splitForScaling(
data = sim_data_long,
effectsValues = effectsValues_2,
normalizeInput = TRUE
)
),
1
)
})
# replaceSymbols() -------------------------------------------------------
test_that("replaceSymbols()", {
expect_equal(
replaceSymbols(
what = "before",
by = "after",
x = "this ~ before"
),
"this~after"
)
expect_equal(
replaceSymbols(
what = c("before1", "before2", "before3"),
by = c("after1", "after2", "after3"),
x = "this ~ before1*before2/before3"
),
"this~after1*after2/after3"
)
})
# analyzeBlocks() --------------------------------------------------------
test_that("analyzeBlocks()", {
test_block_matrix <- matrix(
c(
1, 0, 0, 0,
1, 0, 0, 0,
0, 1, 0, 0,
0, 1, 1, 0,
0, 0, 1, 0,
0, 0, 0, 1,
0, 0, 0, 1
),
nrow = 7,
byrow = TRUE
)
expect_equal(
analyzeBlocks(
test_block_matrix
),
list(
c(1, 2),
c(3, 4, 5),
c(6, 7)
)
)
expect_equal(
length(
analyzeBlocks(
test_block_matrix
)
),
3
)
})
# inputCheck() -----------------------------------------------------------
test_that("inputCheck()", {
simDataWideFile <- system.file(
"extdata", "simDataWide.csv",
package = "blotIt"
)
sim_data_long <- readWide(simDataWideFile, description = seq_len(3))
expect_equal(
inputCheck(
data = sim_data_long,
model = "yi / sj",
errorModel = "value * sigmaR",
biological = yi ~ name + time + condition,
scaling = sj ~ name + ID,
error = sigmaR ~ name + 1,
fitLogscale = TRUE
),
"All input checks passed."
)
expect_error(
inputCheck(
data = sim_data_long,
model = "yi / sj",
errorModel = "value * sigmaR",
biological = yi ~ name + time + condition + wrong,
scaling = sj ~ name + ID,
error = sigmaR ~ name + 1,
fitLogscale = TRUE
),
paste0(
"Not all column names set in 'biological', 'scaling' and 'error' ",
"are present in 'data'."
)
)
expect_error(
inputCheck(
data = sim_data_long,
model = "yi / sj",
errorModel = "value * sigmaR",
# biological = "yi ~ name + time + condition",
scaling = sj ~ name + ID,
error = sigmaR ~ name + 1,
fitLogscale = TRUE
),
"All of model, errorModel, biological, scaling, error must be set."
)
expect_error(
inputCheck(
data = sim_data_long,
# model = "yi / sj",
errorModel = "value * sigmaR",
# biological = "yi ~ name + time + condition",
scaling = sj ~ name + ID,
# error = sigmaR ~ name + 1,
fitLogscale = TRUE
),
"All of model, errorModel, biological, scaling, error must be set."
)
expect_error(
inputCheck(
data = sim_data_long,
model = "yi / sj",
errorModel = "value * sigmaR",
biological = "yi ~ name + time + condition",
scaling = sj ~ name + ID,
error = sigmaR ~ name + 1,
fitLogscale = "wrong"
),
"'fitLogscale' must be logical"
)
})
# genInitPars() -------------------------------------------------
test_that("genInitPars()", {
parameters <- c(
"biological" = "yi",
"scaling" = "sj",
"error" = "sigmaR"
)
levelsList <- list(
biological = c(
"biological_1",
"biological_2",
"biological_3",
"biological_4",
"biological_5",
"biological_6",
"biological_7"
),
scaling = c(
"scaling_1",
"scaling_2"
),
error = "error1"
)
expect_equal(
genInitPars(
parameters = parameters,
fitLogscale = FALSE,
levelsList
),
c(
"yi_1" = 1,
"yi_2" = 1,
"yi_3" = 1,
"yi_4" = 1,
"yi_5" = 1,
"yi_6" = 1,
"yi_7" = 1,
"sj_8" = 1,
"sj_9" = 1,
"sigmaR_10" = 1
)
)
expect_equal(
genInitPars(
parameters = parameters,
fitLogscale = TRUE,
levelsList
),
c(
"yi_1" = 0,
"yi_2" = 0,
"yi_3" = 0,
"yi_4" = 0,
"yi_5" = 0,
"yi_6" = 0,
"yi_7" = 0,
"sj_8" = 0,
"sj_9" = 0,
"sigmaR_10" = 0
)
)
expect_equal(
genInitPars(
parameters = parameters,
fitLogscale = TRUE,
levelsList = list(
biological = c(
"pAKT_0_0Uml Epo",
"pAKT_5_0Uml Epo",
"pAKT_10_0Uml Epo"
),
scaling = c(
"scaling_1",
"scaling_2"
),
error = "error1"
)
),
c(
"yi_1" = 0,
"yi_2" = 0,
"yi_3" = 0,
"sj_4" = 0,
"sj_5" = 0,
"sigmaR_6" = 0
)
)
expect_equal(
genInitPars(
parameters = parameters,
fitLogscale = FALSE,
levelsList = list(
biological = c(
"biological_a",
"biological_b",
"biological_c"
),
scaling = c(
"scaling_a",
"scaling_b",
"scaling_c"
),
error = c(
"error_a",
"error_b",
"error_c"
)
)
),
c(
"yi_1" = 1,
"yi_2" = 1,
"yi_3" = 1,
"sj_4" = 1,
"sj_5" = 1,
"sj_6" = 1,
"sigmaR_7" = 1,
"sigmaR_8" = 1,
"sigmaR_9" = 1
)
)
})
# objFunction() ----------------------------------------------------
test_that("objFunction()", {
test_initial_parameters <- c(
yi = 1,
yi = 1,
yi = 1,
yi = 1,
yi = 1,
yi = 1,
yi = 1,
sj = 1,
sj = 1,
sigmaR = 1
)
test_data_fit <- data.frame(
name = c(
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT"
),
time = c(
0, 5, 10, 20, 30, 60, 240, 0
),
value = c(
1.0210929,
1.2130989,
0.8659901,
0.9323670,
1.0052727,
0.9780511,
1.1585450,
0.8255822
),
sigma = c(
NaN,
NaN,
NaN,
NaN,
NaN,
NaN,
NaN,
NaN
),
biological = c(
"pAKT_0_0Uml Epo",
"pAKT_5_0Uml Epo",
"pAKT_10_0Uml Epo",
"pAKT_20_0Uml Epo",
"pAKT_30_0Uml Epo",
"pAKT_60_0Uml Epo",
"pAKT_240_0Uml Epo",
"pAKT_0_0Uml Epo"
),
scaling = c(
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_2"
),
error = c(
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1"
)
)
row.names(test_data_fit) <- c(
"pAKT1",
"pAKT2",
"pAKT3",
"pAKT4",
"pAKT5",
"pAKT6",
"pAKT7",
"pAKT8"
)
test_parameters <- c(
"biological" = "yi",
"scaling" = "sj",
"error" = "sigmaR"
)
test_levels_list <- list(
biological = c(
"pAKT_0_0Uml Epo",
"pAKT_5_0Uml Epo",
"pAKT_10_0Uml Epo",
"pAKT_20_0Uml Epo",
"pAKT_30_0Uml Epo",
"pAKT_60_0Uml Epo",
"pAKT_240_0Uml Epo"
),
scaling = c(
"pAKT_test", "pAKT_2"
),
error = c(
"pAKT_1"
)
)
test_effectsPars <- list(
biologicalPars = "yi",
scalingPars = "sj",
errorPars = "sigmaR"
)
test_c_strenth <- 1000
test_all_levels <- c(
"pAKT_0_0Uml Epo",
"pAKT_5_0Uml Epo",
"pAKT_10_0Uml Epo",
"pAKT_20_0Uml Epo",
"pAKT_30_0Uml Epo",
"pAKT_60_0Uml Epo",
"pAKT_240_0Uml Epo",
"pAKT_test",
"pAKT_2",
"pAKT_1"
)
test_mask <- genMask(
test_initial_parameters,
test_parameters,
test_all_levels,
test_data_fit
)
test_modelExpr <- parse(text = "yi[biological]/sj[scaling]")
test_errorModel <- parse(
text = "(yi[biological]/sj[scaling])* sigmaR[error]"
)
test_modelJacobianExpr <- list(
biological = parse(text = "1/sj[scaling]"),
scaling = parse(text = "-(yi[biological]/sj[scaling]^2)"),
error = parse(text = "0")
)
test_errorModelJacobianExpr <- list(
biological = parse(text = "1/sj[scaling]*sigmaR[error]"),
scaling = parse(
text = "-(yi[biological]/sj[scaling]^2*sigmaR[error])"
),
error = parse(text = "(yi[biological]/sj[scaling])")
)
test_constrExpr <- parse(text = "1e3 * (mean( yi ) - 1)")
test_passParList <- list(
parameters = test_parameters,
fitLogscale = TRUE,
constStrength = test_c_strenth,
modelExpr = test_modelExpr,
errorModelExpr = test_errorModel,
modelJacobianExpr = test_modelJacobianExpr,
errorModelJacobianExpr = test_errorModelJacobianExpr,
constrExpr = test_constrExpr
)
test_passParList2 <- list(
dataFit = test_data_fit,
levelsList = test_levels_list,
mask = test_mask,
effectsPars = test_effectsPars
)
# * actual tests ----------------------------------------------------------
expect_equal(
objFunction(
currentPars = test_initial_parameters,
passParList = test_passParList,
passParList2 = test_passParList2,
calcDeriv = TRUE
)$value,
0.12445657
)
expect_equal(
round(
objFunction(
currentPars = test_initial_parameters,
passParList = test_passParList,
passParList2 = test_passParList2,
calcDeriv = TRUE
)$gradient,
digits = 8
),
c(
4.244916840,
1.482979920,
2.232102490,
2.126117550,
1.989399000,
2.042934290,
1.632636970,
-13.463094600,
-2.287992460,
15.751086860
)
)
})
# splitForScaling -------------------------------------------------------
test_that("splitForScaling()", {
simDataWideFile <- system.file(
"extdata", "simDataWide.csv",
package = "blotIt"
)
sim_data_long <- readWide(simDataWideFile, description = seq_len(3))
test_effect_values <- list(
biologicaValues = c("name", "time", "condition"),
scalingValues = c("name", "ID"),
errorValues = c("name")
)
expected_values <- c(
116.83827140,
138.80850037,
99.09067869,
106.68583790,
115.02805006,
111.91323007,
132.56618485,
94.46701749,
122.44084967,
51.58390606
)
expect_equal(
splitForScaling(
data = sim_data_long,
effectsValues = test_effect_values,
normalizeInput = FALSE
)[[1]]$value,
expected = expected_values
)
expect_equal(
splitForScaling(
data = sim_data_long,
effectsValues = test_effect_values,
normalizeInput = TRUE
)[[1]]$value,
expected_values / mean(expected_values)
)
expect_equal(
splitForScaling(
data = sim_data_long,
effectsValues = test_effect_values,
normalizeInput = FALSE
)[[1]]$value,
expected_values
)
})
# resolveFunction() -----------------------------------------------------
test_that("resolveFunction()", {
test_initial_parameters <- c(
yi = 1,
yi = 1,
yi = 1,
yi = 1,
yi = 1,
yi = 1,
yi = 1,
sj = 1,
sj = 1,
sigmaR = 1
)
test_data_fit <- data.frame(
name = c(
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT"
),
time = c(
0, 5, 10, 20, 30, 60, 240, 0
),
value = c(
1.0210929,
1.2130989,
0.8659901,
0.9323670,
1.0052727,
0.9780511,
1.1585450,
0.8255822
),
sigma = c(
NaN,
NaN,
NaN,
NaN,
NaN,
NaN,
NaN,
NaN
),
biological = c(
"pAKT_0_0Uml Epo",
"pAKT_5_0Uml Epo",
"pAKT_10_0Uml Epo",
"pAKT_20_0Uml Epo",
"pAKT_30_0Uml Epo",
"pAKT_60_0Uml Epo",
"pAKT_240_0Uml Epo",
"pAKT_0_0Uml Epo"
),
scaling = c(
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_2"
),
error = c(
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1"
)
)
row.names(test_data_fit) <- c(
"pAKT1",
"pAKT2",
"pAKT3",
"pAKT4",
"pAKT5",
"pAKT6",
"pAKT7",
"pAKT8"
)
test_parameters <- c(
"biological" = "yi",
"scaling" = "sj",
"error" = "sigmaR"
)
test_levels_list <- list(
biological = c(
"pAKT_0_0Uml Epo",
"pAKT_5_0Uml Epo",
"pAKT_10_0Uml Epo",
"pAKT_20_0Uml Epo",
"pAKT_30_0Uml Epo",
"pAKT_60_0Uml Epo",
"pAKT_240_0Uml Epo"
),
scaling = c(
"pAKT_test", "pAKT_2"
),
error = c(
"pAKT_1"
)
)
test_effectsPars <- list(
biologicalPars = "yi",
scalingPars = "sj",
errorPars = "sigmaR"
)
test_c_strenth <- 1000
test_all_levels <- c(
"pAKT_0_0Uml Epo",
"pAKT_5_0Uml Epo",
"pAKT_10_0Uml Epo",
"pAKT_20_0Uml Epo",
"pAKT_30_0Uml Epo",
"pAKT_60_0Uml Epo",
"pAKT_240_0Uml Epo",
"pAKT_test",
"pAKT_2",
"pAKT_1"
)
test_mask <- genMask(
test_initial_parameters,
test_parameters,
test_all_levels,
test_data_fit
)
test_modelExpr <- parse(text = "yi[biological]/sj[scaling]")
test_errorModel <- parse(
text = "(yi[biological]/sj[scaling])* sigmaR[error]"
)
test_modelJacobianExpr <- list(
biological = parse(text = "1/sj[scaling]"),
scaling = parse(text = "-(yi[biological]/sj[scaling]^2)"),
error = parse(text = "0")
)
test_errorModelJacobianExpr <- list(
biological = parse(text = "1/sj[scaling]*sigmaR[error]"),
scaling = parse(
text = "-(yi[biological]/sj[scaling]^2*sigmaR[error])"
),
error = parse(text = "(yi[biological]/sj[scaling])")
)
test_constrExpr <- parse(text = "1e3 * (mean( yi ) - 1)")
test_passParList <- list(
parameters = test_parameters,
fitLogscale = TRUE,
constStrength = test_c_strenth,
modelExpr = test_modelExpr,
errorModelExpr = test_errorModel,
modelJacobianExpr = test_modelJacobianExpr,
errorModelJacobianExpr = test_errorModelJacobianExpr,
constrExpr = test_constrExpr
)
test_passParList2 <- list(
dataFit = test_data_fit,
levelsList = test_levels_list,
mask = test_mask,
effectsPars = test_effectsPars
)
# * actual tests ----------------------------------------------------------
expect_equal(
resolveFunction(
currentPars = test_initial_parameters,
passParList = test_passParList,
passParList2 = test_passParList2,
calcDeriv = FALSE
)$residuals,
c(
-0.0210929,
-0.2130989,
0.1340099,
0.0676330,
-0.0052727,
0.0219489,
-0.1585450,
0.1744178,
1.0000000,
1.0000000,
1.0000000,
1.0000000,
1.0000000,
1.0000000,
1.0000000,
1.0000000,
0.0000000
)
)
})
JetiLab/blotIt documentation built on Aug. 23, 2023, 7:38 p.m.
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# getSymbols() -----------------------------------------------------------
test_that("getSymbols()", {
expect_equal(
getSymbols("left1 ~ right11 + right12"),
c("left1", "right11", "right12")
)
expect_equal(
getSymbols(
"left2 ~ (right21 * right22 / (right23 * right24)) - right25"
),
c("left2", "right21", "right22", "right23", "right24", "right25")
)
})
# splitForScaling() -----------------------------------------------------
test_that("splitForScaling()", {
simDataWideFile <- system.file(
"extdata", "simDataWide.csv",
package = "blotIt"
)
sim_data_long <- readWide(simDataWideFile, description = seq_len(3))
effectsValues <- list(
biologicaValues = c("name", "time", "condition"),
scalingValues = c("name", "ID"),
errorValues = "name"
)
effectsValues_1 <- list(
biologicaValues = c("name", "time", "condition"),
scalingValues = c("name"),
errorValues = "name"
)
effectsValues_2 <- list(
biologicaValues = c("name", "time", "condition"),
scalingValues = c("ID"),
errorValues = "name"
)
expect_equal(
length(
splitForScaling(
data = sim_data_long,
effectsValues = effectsValues,
normalizeInput = TRUE
)
),
14
)
expect_equal(
length(
splitForScaling(
data = sim_data_long,
effectsValues = effectsValues_1,
normalizeInput = TRUE
)
),
nrow(unique(sim_data_long["name"]))
)
expect_equal(
length(
splitForScaling(
data = sim_data_long,
effectsValues = effectsValues_2,
normalizeInput = TRUE
)
),
1
)
})
# replaceSymbols() -------------------------------------------------------
test_that("replaceSymbols()", {
expect_equal(
replaceSymbols(
what = "before",
by = "after",
x = "this ~ before"
),
"this~after"
)
expect_equal(
replaceSymbols(
what = c("before1", "before2", "before3"),
by = c("after1", "after2", "after3"),
x = "this ~ before1*before2/before3"
),
"this~after1*after2/after3"
)
})
# analyzeBlocks() --------------------------------------------------------
test_that("analyzeBlocks()", {
test_block_matrix <- matrix(
c(
1, 0, 0, 0,
1, 0, 0, 0,
0, 1, 0, 0,
0, 1, 1, 0,
0, 0, 1, 0,
0, 0, 0, 1,
0, 0, 0, 1
),
nrow = 7,
byrow = TRUE
)
expect_equal(
analyzeBlocks(
test_block_matrix
),
list(
c(1, 2),
c(3, 4, 5),
c(6, 7)
)
)
expect_equal(
length(
analyzeBlocks(
test_block_matrix
)
),
3
)
})
# inputCheck() -----------------------------------------------------------
test_that("inputCheck()", {
simDataWideFile <- system.file(
"extdata", "simDataWide.csv",
package = "blotIt"
)
sim_data_long <- readWide(simDataWideFile, description = seq_len(3))
expect_equal(
inputCheck(
data = sim_data_long,
model = "yi / sj",
errorModel = "value * sigmaR",
biological = yi ~ name + time + condition,
scaling = sj ~ name + ID,
error = sigmaR ~ name + 1,
fitLogscale = TRUE
),
"All input checks passed."
)
expect_error(
inputCheck(
data = sim_data_long,
model = "yi / sj",
errorModel = "value * sigmaR",
biological = yi ~ name + time + condition + wrong,
scaling = sj ~ name + ID,
error = sigmaR ~ name + 1,
fitLogscale = TRUE
),
paste0(
"Not all column names set in 'biological', 'scaling' and 'error' ",
"are present in 'data'."
)
)
expect_error(
inputCheck(
data = sim_data_long,
model = "yi / sj",
errorModel = "value * sigmaR",
# biological = "yi ~ name + time + condition",
scaling = sj ~ name + ID,
error = sigmaR ~ name + 1,
fitLogscale = TRUE
),
"All of model, errorModel, biological, scaling, error must be set."
)
expect_error(
inputCheck(
data = sim_data_long,
# model = "yi / sj",
errorModel = "value * sigmaR",
# biological = "yi ~ name + time + condition",
scaling = sj ~ name + ID,
# error = sigmaR ~ name + 1,
fitLogscale = TRUE
),
"All of model, errorModel, biological, scaling, error must be set."
)
expect_error(
inputCheck(
data = sim_data_long,
model = "yi / sj",
errorModel = "value * sigmaR",
biological = "yi ~ name + time + condition",
scaling = sj ~ name + ID,
error = sigmaR ~ name + 1,
fitLogscale = "wrong"
),
"'fitLogscale' must be logical"
)
})
# genInitPars() -------------------------------------------------
test_that("genInitPars()", {
parameters <- c(
"biological" = "yi",
"scaling" = "sj",
"error" = "sigmaR"
)
levelsList <- list(
biological = c(
"biological_1",
"biological_2",
"biological_3",
"biological_4",
"biological_5",
"biological_6",
"biological_7"
),
scaling = c(
"scaling_1",
"scaling_2"
),
error = "error1"
)
expect_equal(
genInitPars(
parameters = parameters,
fitLogscale = FALSE,
levelsList
),
c(
"yi_1" = 1,
"yi_2" = 1,
"yi_3" = 1,
"yi_4" = 1,
"yi_5" = 1,
"yi_6" = 1,
"yi_7" = 1,
"sj_8" = 1,
"sj_9" = 1,
"sigmaR_10" = 1
)
)
expect_equal(
genInitPars(
parameters = parameters,
fitLogscale = TRUE,
levelsList
),
c(
"yi_1" = 0,
"yi_2" = 0,
"yi_3" = 0,
"yi_4" = 0,
"yi_5" = 0,
"yi_6" = 0,
"yi_7" = 0,
"sj_8" = 0,
"sj_9" = 0,
"sigmaR_10" = 0
)
)
expect_equal(
genInitPars(
parameters = parameters,
fitLogscale = TRUE,
levelsList = list(
biological = c(
"pAKT_0_0Uml Epo",
"pAKT_5_0Uml Epo",
"pAKT_10_0Uml Epo"
),
scaling = c(
"scaling_1",
"scaling_2"
),
error = "error1"
)
),
c(
"yi_1" = 0,
"yi_2" = 0,
"yi_3" = 0,
"sj_4" = 0,
"sj_5" = 0,
"sigmaR_6" = 0
)
)
expect_equal(
genInitPars(
parameters = parameters,
fitLogscale = FALSE,
levelsList = list(
biological = c(
"biological_a",
"biological_b",
"biological_c"
),
scaling = c(
"scaling_a",
"scaling_b",
"scaling_c"
),
error = c(
"error_a",
"error_b",
"error_c"
)
)
),
c(
"yi_1" = 1,
"yi_2" = 1,
"yi_3" = 1,
"sj_4" = 1,
"sj_5" = 1,
"sj_6" = 1,
"sigmaR_7" = 1,
"sigmaR_8" = 1,
"sigmaR_9" = 1
)
)
})
# objFunction() ----------------------------------------------------
test_that("objFunction()", {
test_initial_parameters <- c(
yi = 1,
yi = 1,
yi = 1,
yi = 1,
yi = 1,
yi = 1,
yi = 1,
sj = 1,
sj = 1,
sigmaR = 1
)
test_data_fit <- data.frame(
name = c(
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT"
),
time = c(
0, 5, 10, 20, 30, 60, 240, 0
),
value = c(
1.0210929,
1.2130989,
0.8659901,
0.9323670,
1.0052727,
0.9780511,
1.1585450,
0.8255822
),
sigma = c(
NaN,
NaN,
NaN,
NaN,
NaN,
NaN,
NaN,
NaN
),
biological = c(
"pAKT_0_0Uml Epo",
"pAKT_5_0Uml Epo",
"pAKT_10_0Uml Epo",
"pAKT_20_0Uml Epo",
"pAKT_30_0Uml Epo",
"pAKT_60_0Uml Epo",
"pAKT_240_0Uml Epo",
"pAKT_0_0Uml Epo"
),
scaling = c(
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_2"
),
error = c(
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1"
)
)
row.names(test_data_fit) <- c(
"pAKT1",
"pAKT2",
"pAKT3",
"pAKT4",
"pAKT5",
"pAKT6",
"pAKT7",
"pAKT8"
)
test_parameters <- c(
"biological" = "yi",
"scaling" = "sj",
"error" = "sigmaR"
)
test_levels_list <- list(
biological = c(
"pAKT_0_0Uml Epo",
"pAKT_5_0Uml Epo",
"pAKT_10_0Uml Epo",
"pAKT_20_0Uml Epo",
"pAKT_30_0Uml Epo",
"pAKT_60_0Uml Epo",
"pAKT_240_0Uml Epo"
),
scaling = c(
"pAKT_test", "pAKT_2"
),
error = c(
"pAKT_1"
)
)
test_effectsPars <- list(
biologicalPars = "yi",
scalingPars = "sj",
errorPars = "sigmaR"
)
test_c_strenth <- 1000
test_all_levels <- c(
"pAKT_0_0Uml Epo",
"pAKT_5_0Uml Epo",
"pAKT_10_0Uml Epo",
"pAKT_20_0Uml Epo",
"pAKT_30_0Uml Epo",
"pAKT_60_0Uml Epo",
"pAKT_240_0Uml Epo",
"pAKT_test",
"pAKT_2",
"pAKT_1"
)
test_mask <- genMask(
test_initial_parameters,
test_parameters,
test_all_levels,
test_data_fit
)
test_modelExpr <- parse(text = "yi[biological]/sj[scaling]")
test_errorModel <- parse(
text = "(yi[biological]/sj[scaling])* sigmaR[error]"
)
test_modelJacobianExpr <- list(
biological = parse(text = "1/sj[scaling]"),
scaling = parse(text = "-(yi[biological]/sj[scaling]^2)"),
error = parse(text = "0")
)
test_errorModelJacobianExpr <- list(
biological = parse(text = "1/sj[scaling]*sigmaR[error]"),
scaling = parse(
text = "-(yi[biological]/sj[scaling]^2*sigmaR[error])"
),
error = parse(text = "(yi[biological]/sj[scaling])")
)
test_constrExpr <- parse(text = "1e3 * (mean( yi ) - 1)")
test_passParList <- list(
parameters = test_parameters,
fitLogscale = TRUE,
constStrength = test_c_strenth,
modelExpr = test_modelExpr,
errorModelExpr = test_errorModel,
modelJacobianExpr = test_modelJacobianExpr,
errorModelJacobianExpr = test_errorModelJacobianExpr,
constrExpr = test_constrExpr
)
test_passParList2 <- list(
dataFit = test_data_fit,
levelsList = test_levels_list,
mask = test_mask,
effectsPars = test_effectsPars
)
# * actual tests ----------------------------------------------------------
expect_equal(
objFunction(
currentPars = test_initial_parameters,
passParList = test_passParList,
passParList2 = test_passParList2,
calcDeriv = TRUE
)$value,
0.12445657
)
expect_equal(
round(
objFunction(
currentPars = test_initial_parameters,
passParList = test_passParList,
passParList2 = test_passParList2,
calcDeriv = TRUE
)$gradient,
digits = 8
),
c(
4.244916840,
1.482979920,
2.232102490,
2.126117550,
1.989399000,
2.042934290,
1.632636970,
-13.463094600,
-2.287992460,
15.751086860
)
)
})
# splitForScaling -------------------------------------------------------
test_that("splitForScaling()", {
simDataWideFile <- system.file(
"extdata", "simDataWide.csv",
package = "blotIt"
)
sim_data_long <- readWide(simDataWideFile, description = seq_len(3))
test_effect_values <- list(
biologicaValues = c("name", "time", "condition"),
scalingValues = c("name", "ID"),
errorValues = c("name")
)
expected_values <- c(
116.83827140,
138.80850037,
99.09067869,
106.68583790,
115.02805006,
111.91323007,
132.56618485,
94.46701749,
122.44084967,
51.58390606
)
expect_equal(
splitForScaling(
data = sim_data_long,
effectsValues = test_effect_values,
normalizeInput = FALSE
)[[1]]$value,
expected = expected_values
)
expect_equal(
splitForScaling(
data = sim_data_long,
effectsValues = test_effect_values,
normalizeInput = TRUE
)[[1]]$value,
expected_values / mean(expected_values)
)
expect_equal(
splitForScaling(
data = sim_data_long,
effectsValues = test_effect_values,
normalizeInput = FALSE
)[[1]]$value,
expected_values
)
})
# resolveFunction() -----------------------------------------------------
test_that("resolveFunction()", {
test_initial_parameters <- c(
yi = 1,
yi = 1,
yi = 1,
yi = 1,
yi = 1,
yi = 1,
yi = 1,
sj = 1,
sj = 1,
sigmaR = 1
)
test_data_fit <- data.frame(
name = c(
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT",
"pAKT"
),
time = c(
0, 5, 10, 20, 30, 60, 240, 0
),
value = c(
1.0210929,
1.2130989,
0.8659901,
0.9323670,
1.0052727,
0.9780511,
1.1585450,
0.8255822
),
sigma = c(
NaN,
NaN,
NaN,
NaN,
NaN,
NaN,
NaN,
NaN
),
biological = c(
"pAKT_0_0Uml Epo",
"pAKT_5_0Uml Epo",
"pAKT_10_0Uml Epo",
"pAKT_20_0Uml Epo",
"pAKT_30_0Uml Epo",
"pAKT_60_0Uml Epo",
"pAKT_240_0Uml Epo",
"pAKT_0_0Uml Epo"
),
scaling = c(
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_test",
"pAKT_2"
),
error = c(
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1",
"pAKT_1"
)
)
row.names(test_data_fit) <- c(
"pAKT1",
"pAKT2",
"pAKT3",
"pAKT4",
"pAKT5",
"pAKT6",
"pAKT7",
"pAKT8"
)
test_parameters <- c(
"biological" = "yi",
"scaling" = "sj",
"error" = "sigmaR"
)
test_levels_list <- list(
biological = c(
"pAKT_0_0Uml Epo",
"pAKT_5_0Uml Epo",
"pAKT_10_0Uml Epo",
"pAKT_20_0Uml Epo",
"pAKT_30_0Uml Epo",
"pAKT_60_0Uml Epo",
"pAKT_240_0Uml Epo"
),
scaling = c(
"pAKT_test", "pAKT_2"
),
error = c(
"pAKT_1"
)
)
test_effectsPars <- list(
biologicalPars = "yi",
scalingPars = "sj",
errorPars = "sigmaR"
)
test_c_strenth <- 1000
test_all_levels <- c(
"pAKT_0_0Uml Epo",
"pAKT_5_0Uml Epo",
"pAKT_10_0Uml Epo",
"pAKT_20_0Uml Epo",
"pAKT_30_0Uml Epo",
"pAKT_60_0Uml Epo",
"pAKT_240_0Uml Epo",
"pAKT_test",
"pAKT_2",
"pAKT_1"
)
test_mask <- genMask(
test_initial_parameters,
test_parameters,
test_all_levels,
test_data_fit
)
test_modelExpr <- parse(text = "yi[biological]/sj[scaling]")
test_errorModel <- parse(
text = "(yi[biological]/sj[scaling])* sigmaR[error]"
)
test_modelJacobianExpr <- list(
biological = parse(text = "1/sj[scaling]"),
scaling = parse(text = "-(yi[biological]/sj[scaling]^2)"),
error = parse(text = "0")
)
test_errorModelJacobianExpr <- list(
biological = parse(text = "1/sj[scaling]*sigmaR[error]"),
scaling = parse(
text = "-(yi[biological]/sj[scaling]^2*sigmaR[error])"
),
error = parse(text = "(yi[biological]/sj[scaling])")
)
test_constrExpr <- parse(text = "1e3 * (mean( yi ) - 1)")
test_passParList <- list(
parameters = test_parameters,
fitLogscale = TRUE,
constStrength = test_c_strenth,
modelExpr = test_modelExpr,
errorModelExpr = test_errorModel,
modelJacobianExpr = test_modelJacobianExpr,
errorModelJacobianExpr = test_errorModelJacobianExpr,
constrExpr = test_constrExpr
)
test_passParList2 <- list(
dataFit = test_data_fit,
levelsList = test_levels_list,
mask = test_mask,
effectsPars = test_effectsPars
)
# * actual tests ----------------------------------------------------------
expect_equal(
resolveFunction(
currentPars = test_initial_parameters,
passParList = test_passParList,
passParList2 = test_passParList2,
calcDeriv = FALSE
)$residuals,
c(
-0.0210929,
-0.2130989,
0.1340099,
0.0676330,
-0.0052727,
0.0219489,
-0.1585450,
0.1744178,
1.0000000,
1.0000000,
1.0000000,
1.0000000,
1.0000000,
1.0000000,
1.0000000,
1.0000000,
0.0000000
)
)
})
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