tests/testthat/test-mergemodelslists.R
In marcpaga/pulsedSilac: Analysis of pulsed-SILAC quantitative proteomics data
context("test-mergemodelslists")
test_that("mergemodelslists works protein", {
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE[1:10,]
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml3 <- mergeModelsLists(ml1, ml2)
expect_identical(ml0, ml3)
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE)
ml3 <- mergeModelsLists(ml1, ml2)
expect_identical(ml0, ml3)
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
ml3 <- mergeModelsLists(ml1, ml2)
## cannot use identical here because the model objects have some differences
expect_equal(ml0, ml3)
expect_identical(attributes(ml0), attributes(ml3))
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml3 <- mergeModelsLists(ml1, ml2)
## cannot use identical here because the model objects have some differences
expect_equal(ml0, ml3)
expect_identical(attributes(ml0), attributes(ml3))
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE[1:10,]
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
colnames(testPE) <- LETTERS[1:14]
rownamesProt(testPE) <- LETTERS[1:10]
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
ml3 <- mergeModelsLists(ml1, ml2)
expect_equal(ml0, ml3)
})
test_that("mergemodelslists works grouped", {
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE[1:10,]
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml3 <- mergeModelsLists(ml1, ml2)
expect_identical(ml0, ml3)
expect_warning(ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE))
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE)
expect_warning(ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE))
ml3 <- mergeModelsLists(ml1, ml2)
expect_equal(ml0, ml3)
expect_warning(ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
expect_warning(ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
ml3 <- mergeModelsLists(ml1, ml2)
## cannot use identical here because the model objects have some differences
expect_equal(ml0, ml3)
expect_identical(attributes(ml0), attributes(ml3))
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml3 <- mergeModelsLists(ml1, ml2)
## cannot use identical here because the model objects have some differences
expect_equal(ml0, ml3)
expect_identical(attributes(ml0), attributes(ml3))
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE[1:10,]
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
colnames(testPE) <- LETTERS[1:14]
rownamesPept(testPE) <- rowDataPept(testPE)$Sequence
expect_warning(ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
expect_warning(ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
ml3 <- mergeModelsLists(ml1, ml2)
expect_equal(ml0, ml3)
})
test_that("mergemodelslists works peptide", {
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE[1:10,]
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml3 <- mergeModelsLists(ml1, ml2)
expect_identical(ml0, ml3)
expect_warning(ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE))
expect_warning(ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE))
expect_warning(ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE))
ml3 <- mergeModelsLists(ml1, ml2)
expect_identical(ml0, ml3)
expect_warning(ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
expect_warning(ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
expect_warning( ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
ml3 <- mergeModelsLists(ml1, ml2)
## cannot use identical here because the model objects have some differences
expect_equal(ml0, ml3)
expect_identical(attributes(ml0), attributes(ml3))
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml3 <- mergeModelsLists(ml1, ml2)
## cannot use identical here because the model objects have some differences
expect_equal(ml0, ml3)
expect_identical(attributes(ml0), attributes(ml3))
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE[1:10,]
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
colnames(testPE) <- LETTERS[1:14]
rownamesPept(testPE) <- rowDataPept(testPE)$Sequence
expect_warning(ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
expect_warning(ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
expect_warning(ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
ml3 <- mergeModelsLists(ml1, ml2)
expect_equal(ml0, ml3)
})
marcpaga/pulsedSilac documentation built on March 11, 2020, 8:49 p.m.
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context("test-mergemodelslists")
test_that("mergemodelslists works protein", {
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE[1:10,]
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml3 <- mergeModelsLists(ml1, ml2)
expect_identical(ml0, ml3)
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE)
ml3 <- mergeModelsLists(ml1, ml2)
expect_identical(ml0, ml3)
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
ml3 <- mergeModelsLists(ml1, ml2)
## cannot use identical here because the model objects have some differences
expect_equal(ml0, ml3)
expect_identical(attributes(ml0), attributes(ml3))
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml3 <- mergeModelsLists(ml1, ml2)
## cannot use identical here because the model objects have some differences
expect_equal(ml0, ml3)
expect_identical(attributes(ml0), attributes(ml3))
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE[1:10,]
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
colnames(testPE) <- LETTERS[1:14]
rownamesProt(testPE) <- LETTERS[1:10]
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'protein',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
ml3 <- mergeModelsLists(ml1, ml2)
expect_equal(ml0, ml3)
})
test_that("mergemodelslists works grouped", {
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE[1:10,]
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml3 <- mergeModelsLists(ml1, ml2)
expect_identical(ml0, ml3)
expect_warning(ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE))
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE)
expect_warning(ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE))
ml3 <- mergeModelsLists(ml1, ml2)
expect_equal(ml0, ml3)
expect_warning(ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
expect_warning(ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
ml3 <- mergeModelsLists(ml1, ml2)
## cannot use identical here because the model objects have some differences
expect_equal(ml0, ml3)
expect_identical(attributes(ml0), attributes(ml3))
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml3 <- mergeModelsLists(ml1, ml2)
## cannot use identical here because the model objects have some differences
expect_equal(ml0, ml3)
expect_identical(attributes(ml0), attributes(ml3))
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE[1:10,]
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
colnames(testPE) <- LETTERS[1:14]
rownamesPept(testPE) <- rowDataPept(testPE)$Sequence
expect_warning(ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE)
expect_warning(ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'grouped',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
ml3 <- mergeModelsLists(ml1, ml2)
expect_equal(ml0, ml3)
})
test_that("mergemodelslists works peptide", {
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE[1:10,]
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = FALSE,
returnModel = FALSE)
ml3 <- mergeModelsLists(ml1, ml2)
expect_identical(ml0, ml3)
expect_warning(ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE))
expect_warning(ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE))
expect_warning(ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = FALSE))
ml3 <- mergeModelsLists(ml1, ml2)
expect_identical(ml0, ml3)
expect_warning(ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
expect_warning(ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
expect_warning( ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
ml3 <- mergeModelsLists(ml1, ml2)
## cannot use identical here because the model objects have some differences
expect_equal(ml0, ml3)
expect_identical(attributes(ml0), attributes(ml3))
ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = FALSE,
returnModel = TRUE)
ml3 <- mergeModelsLists(ml1, ml2)
## cannot use identical here because the model objects have some differences
expect_equal(ml0, ml3)
expect_identical(attributes(ml0), attributes(ml3))
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE[1:10,]
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
colnames(testPE) <- LETTERS[1:14]
rownamesPept(testPE) <- rowDataPept(testPE)$Sequence
expect_warning(ml0 <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
expect_warning(ml1 <- modelTurnover(x = testPE[,1:7],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
expect_warning(ml2 <- modelTurnover(x = testPE[,8:14],
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
mode = 'peptide',
start = list(k = 0.02),
robust = TRUE,
returnModel = TRUE))
ml3 <- mergeModelsLists(ml1, ml2)
expect_equal(ml0, ml3)
})
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