tests/testthat/test-modelturnover.R
In marcpaga/pulsedSilac: Analysis of pulsed-SILAC quantitative proteomics data
context("test-modelturnover")
test_that("modelturnover proteinExperiment works", {
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE@SilacProteinExperiment
testPE <- testPE[1:10,]
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
expect_silent(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = FALSE))
expect_is(ml, 'list')
expect_equal(names(ml), c('residuals', 'stderror', 'param_values',
'param_pval', 'param_tval', 'param_stderror'))
#expect_equal(unname(sapply(ml, class)), c(rep('matrix', 2), rep('list', 4)))
expect_equal(nrow(ml[[1]]), nrow(testPE))
expect_equal(nrow(ml[[2]]), nrow(testPE))
expect_equal(ncol(ml[[1]]), ncol(testPE))
expect_equal(ncol(ml[[2]]), 2)
expect_equal(ncol(ml[[3]][[1]]), 2)
expect_equal(ncol(ml[[4]][[1]]), 2)
expect_equal(ncol(ml[[5]][[1]]), 2)
expect_equal(ncol(ml[[6]][[1]]), 2)
expect_silent(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = TRUE,
verbose = FALSE,
returnModel = FALSE))
expect_is(ml, 'list')
expect_equal(names(ml), c('residuals', 'stderror', 'param_values',
'param_pval', 'param_tval', 'param_stderror',
'weights'))
#expect_equal(unname(sapply(ml, class)), c(rep('matrix', 2), rep('list', 4), 'matrix'))
expect_equal(nrow(ml[[1]]), nrow(testPE))
expect_equal(nrow(ml[[2]]), nrow(testPE))
expect_equal(ncol(ml[[1]]), ncol(testPE))
expect_equal(ncol(ml[[2]]), 2)
expect_equal(ncol(ml[[3]][[1]]), 2)
expect_equal(ncol(ml[[4]][[1]]), 2)
expect_equal(ncol(ml[[5]][[1]]), 2)
expect_equal(ncol(ml[[6]][[1]]), 2)
expect_equal(ncol(ml[[7]]), ncol(testPE))
expect_silent(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = TRUE))
expect_is(ml, 'list')
expect_equal(length(ml), 7)
expect_equal(length(ml[['models']][[1]]), nrow(testPE))
expect_equal(length(ml[['models']][[2]]), nrow(testPE))
expect_is(ml[['models']][[1]][[2]], 'nls')
expect_equal(names(attributes(ml)), c('names', 'loopCols', 'time', 'cond', 'assayName', 'mode'))
expect_silent(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = TRUE,
verbose = FALSE,
returnModel = TRUE))
expect_is(ml, 'list')
expect_equal(length(ml), 8)
expect_equal(length(ml[['models']][[1]]), nrow(testPE))
expect_equal(length(ml[['models']][[2]]), nrow(testPE))
expect_is(ml[['models']][[1]][[2]], 'nls')
expect_equal(names(attributes(ml)), c('names', 'loopCols', 'time', 'cond', 'assayName', 'mode'))
colnames(testPE) <- LETTERS[1:14]
rownames(testPE) <- LETTERS[1:10]
expect_silent(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = TRUE))
expect_equal(rownames(ml[[1]]), rownames(testPE))
expect_equal(colnames(ml[[1]]), colnames(testPE))
expect_equal(rownames(ml[[2]]), rownames(testPE))
expect_equal(colnames(ml[[2]]), c('OW40', 'OW450'))
})
test_that("modelturnover peptideExperiment works", {
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE[1:10,]
testPE <- testPE@SilacPeptideExperiment
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
## 1 model per peptide
expect_message(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = FALSE,
mode = 'peptide'))
expect_is(ml, 'list')
expect_equal(names(ml), c('residuals', 'stderror', 'param_values',
'param_pval', 'param_tval', 'param_stderror'))
#expect_equal(unname(sapply(ml, class)), c(rep('matrix', 2), rep('list', 4)))
expect_equal(nrow(ml[[1]]), nrow(testPE))
expect_equal(nrow(ml[[2]]), nrow(testPE))
expect_equal(ncol(ml[[1]]), ncol(testPE))
expect_equal(ncol(ml[[2]]), 2)
expect_equal(ncol(ml[[3]][[1]]), 2)
expect_equal(ncol(ml[[4]][[1]]), 2)
expect_equal(ncol(ml[[5]][[1]]), 2)
expect_equal(ncol(ml[[6]][[1]]), 2)
expect_warning(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = TRUE,
verbose = FALSE,
returnModel = FALSE,
mode = 'peptide'))
expect_is(ml, 'list')
expect_equal(names(ml), c('residuals', 'stderror', 'param_values',
'param_pval', 'param_tval', 'param_stderror',
'weights'))
#expect_equal(unname(sapply(ml, class)), c(rep('matrix', 2), rep('list', 4), 'matrix'))
expect_equal(nrow(ml[[1]]), nrow(testPE))
expect_equal(nrow(ml[[2]]), nrow(testPE))
expect_equal(ncol(ml[[1]]), ncol(testPE))
expect_equal(ncol(ml[[2]]), 2)
expect_equal(ncol(ml[[3]][[1]]), 2)
expect_equal(ncol(ml[[4]][[1]]), 2)
expect_equal(ncol(ml[[5]][[1]]), 2)
expect_equal(ncol(ml[[6]][[1]]), 2)
expect_equal(ncol(ml[[7]]), ncol(testPE))
expect_message(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = TRUE,
mode = 'peptide'))
expect_is(ml, 'list')
expect_equal(length(ml), 7)
expect_equal(length(ml[['models']][[1]]), nrow(testPE))
expect_equal(length(ml[['models']][[2]]), nrow(testPE))
expect_is(ml[['models']][[1]][[6]], 'nls')
expect_equal(names(attributes(ml)), c('names','loopCols', 'time', 'cond', 'assayName', 'mode'))
expect_warning(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = TRUE,
verbose = FALSE,
returnModel = TRUE,
mode = 'peptide'))
expect_is(ml, 'list')
expect_equal(length(ml), 8)
expect_equal(length(ml[['models']][[1]]), nrow(testPE))
expect_equal(length(ml[['models']][[2]]), nrow(testPE))
expect_is(ml[['models']][[1]][[6]], 'nls')
expect_equal(names(attributes(ml)), c('names','loopCols', 'time', 'cond', 'assayName', 'mode'))
colnames(testPE) <- LETTERS[1:14]
rownames(testPE) <- rowData(testPE)$Sequence
expect_message(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = TRUE,
mode = 'peptide'))
expect_equal(rownames(ml[[1]]), rownames(testPE))
expect_equal(colnames(ml[[1]]), colnames(testPE))
expect_equal(rownames(ml[[2]]), rownames(testPE))
expect_equal(colnames(ml[[2]]), c('OW40', 'OW450'))
## 1 model per protein
expect_message(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = FALSE,
mode = 'grouped'))
expect_is(ml, 'list')
expect_equal(names(ml), c('residuals', 'stderror', 'param_values',
'param_pval', 'param_tval', 'param_stderror'))
#expect_equal(unname(sapply(ml, class)), c(rep('matrix', 2), rep('list', 4)))
expect_equal(nrow(ml[[1]]), nrow(testPE))
expect_equal(nrow(ml[[2]]), 10)
expect_equal(nrow(ml[[3]][[1]]), 10)
expect_equal(nrow(ml[[4]][[1]]), 10)
expect_equal(nrow(ml[[5]][[1]]), 10)
expect_equal(nrow(ml[[6]][[1]]), 10)
expect_equal(ncol(ml[[1]]), ncol(testPE))
expect_equal(ncol(ml[[2]]), 2)
expect_equal(ncol(ml[[3]][[1]]), 2)
expect_equal(ncol(ml[[4]][[1]]), 2)
expect_equal(ncol(ml[[5]][[1]]), 2)
expect_equal(ncol(ml[[6]][[1]]), 2)
expect_warning(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = TRUE,
verbose = FALSE,
returnModel = FALSE,
mode = 'grouped'))
expect_is(ml, 'list')
expect_equal(names(ml), c('residuals', 'stderror', 'param_values',
'param_pval', 'param_tval', 'param_stderror',
'weights'))
#expect_equal(unname(sapply(ml, class)), c(rep('matrix', 2), rep('list', 4), 'matrix'))
expect_equal(nrow(ml[[1]]), nrow(testPE))
expect_equal(nrow(ml[[2]]), 10)
expect_equal(nrow(ml[[3]][[1]]), 10)
expect_equal(nrow(ml[[4]][[1]]), 10)
expect_equal(nrow(ml[[5]][[1]]), 10)
expect_equal(nrow(ml[[6]][[1]]), 10)
expect_equal(nrow(ml[[7]]), nrow(testPE))
expect_equal(ncol(ml[[1]]), ncol(testPE))
expect_equal(ncol(ml[[2]]), 2)
expect_equal(ncol(ml[[3]][[1]]), 2)
expect_equal(ncol(ml[[4]][[1]]), 2)
expect_equal(ncol(ml[[5]][[1]]), 2)
expect_equal(ncol(ml[[6]][[1]]), 2)
expect_equal(ncol(ml[[7]]), ncol(testPE))
expect_message(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = TRUE,
mode = 'grouped'))
expect_is(ml, 'list')
expect_equal(length(ml), 7)
expect_equal(length(ml[['models']][[1]]), 10)
expect_equal(length(ml[['models']][[2]]), 10)
expect_is(ml[['models']][[1]][[2]], 'nls')
expect_equal(names(attributes(ml)), c('names','loopCols', 'time', 'cond', 'prot', 'assayName', 'mode'))
expect_warning(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = TRUE,
verbose = FALSE,
returnModel = TRUE,
mode = 'grouped'))
expect_is(ml, 'list')
expect_equal(length(ml), 8)
expect_equal(length(ml[['models']][[1]]), 10)
expect_equal(length(ml[['models']][[2]]), 10)
expect_is(ml[['models']][[1]][[2]], 'nls')
expect_equal(names(attributes(ml)), c('names', 'loopCols', 'time', 'cond', 'prot', 'assayName', 'mode'))
expect_message(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = TRUE,
mode = 'grouped'))
expect_equal(rownames(ml[[1]]), rownames(testPE))
expect_equal(colnames(ml[[1]]), colnames(testPE))
expect_equal(rownames(ml[[2]]),
unique(rowData(testPE)[,metaoptions(testPE)[['proteinCol']]]))
expect_equal(colnames(ml[[2]]), c('OW40', 'OW450'))
})
test_that("modelturnover differet nls algorithms works", {
frac <- 1 - exp(-0.02 * c(4, 8, 16, 24, 36, 48)) + rnorm(6, mean = 0, sd = 0.02)
data <- data.frame(t = c(4, 8, 16, 24, 36, 48),
fraction =frac)
formula <- as.formula('fraction ~ 1 - exp(-k * t)')
start <- list(k = 0.025)
robust <- FALSE
modeldata <- .modelTurnover(data = data,
formula = formula,
start = start,
robust = robust,
returnModel = FALSE)
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror,length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
modeldata <- .modelTurnover(data = data,
formula = formula,
start = start,
robust = robust,
returnModel = FALSE,
algorithm = 'plinear')
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror, length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
modeldata <- .modelTurnover(data = data,
formula = formula,
start = start,
robust = robust,
returnModel = FALSE,
algorithm = 'port')
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror,length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
frac <- 1 - exp(-0.02 * c(4, 8, 16, 24, 36, 48)) + rnorm(6, mean = 0, sd = 0.02)
data <- data.frame(t = c(4, 8, 16, 24, 36, 48),
fraction =frac)
formula <- as.formula('fraction ~ 1 - exp(-k * t) + b')
start <- list(k = 0.025, b = 0)
robust <- FALSE
modeldata <- .modelTurnover(data = data,
formula = formula,
start = start,
robust = robust,
returnModel = FALSE)
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror,length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
modeldata <- .modelTurnover(data = data,
formula = formula,
start = start,
robust = robust,
returnModel = FALSE,
algorithm = 'plinear')
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror, length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
modeldata <- .modelTurnover(data = data,
formula = formula,
start = start,
robust = robust,
returnModel = FALSE,
algorithm = 'port')
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror,length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
})
test_that("modelturnover differet nlrob algorithms works", {
frac <- 1 - exp(-0.02 * c(4, 8, 16, 24, 36, 48)) + rnorm(6, mean = 0, sd = 0.02)
data <- data.frame(t = c(4, 8, 16, 24, 36, 48),
fraction =frac)
formula <- as.formula('fraction ~ 1 - exp(-k * t) + b')
start <- list(k = 0.025, b = 0)
lower <- c(k = 0, b = 0)
upper <- c(k = 0.5, b = 0.1)
robust <- TRUE
modeldata <- .modelTurnover(data = data,
formula = formula,
start = start,
robust = robust,
returnModel = FALSE,
method = 'M')
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror,length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
modeldata <- .modelTurnover(data = data,
formula = formula,
lower = lower,
upper = upper,
robust = robust,
returnModel = FALSE,
method = 'MM')
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror, length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
modeldata <- .modelTurnover(data = data,
formula = formula,
lower = lower,
upper = upper,
robust = robust,
returnModel = FALSE,
method = 'tau')
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror,length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
modeldata <- expect_error(.modelTurnover(data = data,
formula = formula,
lower = lower,
upper = upper,
robust = robust,
returnModel = FALSE,
method = 'CM'))
modeldata <- expect_error(.modelTurnover(data = data,
formula = formula,
lower = lower,
upper = upper,
robust = robust,
returnModel = FALSE,
method = 'mtl', silent = FALSE))
})
marcpaga/pulsedSilac documentation built on March 11, 2020, 8:49 p.m.
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context("test-modelturnover")
test_that("modelturnover proteinExperiment works", {
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE@SilacProteinExperiment
testPE <- testPE[1:10,]
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
expect_silent(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = FALSE))
expect_is(ml, 'list')
expect_equal(names(ml), c('residuals', 'stderror', 'param_values',
'param_pval', 'param_tval', 'param_stderror'))
#expect_equal(unname(sapply(ml, class)), c(rep('matrix', 2), rep('list', 4)))
expect_equal(nrow(ml[[1]]), nrow(testPE))
expect_equal(nrow(ml[[2]]), nrow(testPE))
expect_equal(ncol(ml[[1]]), ncol(testPE))
expect_equal(ncol(ml[[2]]), 2)
expect_equal(ncol(ml[[3]][[1]]), 2)
expect_equal(ncol(ml[[4]][[1]]), 2)
expect_equal(ncol(ml[[5]][[1]]), 2)
expect_equal(ncol(ml[[6]][[1]]), 2)
expect_silent(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = TRUE,
verbose = FALSE,
returnModel = FALSE))
expect_is(ml, 'list')
expect_equal(names(ml), c('residuals', 'stderror', 'param_values',
'param_pval', 'param_tval', 'param_stderror',
'weights'))
#expect_equal(unname(sapply(ml, class)), c(rep('matrix', 2), rep('list', 4), 'matrix'))
expect_equal(nrow(ml[[1]]), nrow(testPE))
expect_equal(nrow(ml[[2]]), nrow(testPE))
expect_equal(ncol(ml[[1]]), ncol(testPE))
expect_equal(ncol(ml[[2]]), 2)
expect_equal(ncol(ml[[3]][[1]]), 2)
expect_equal(ncol(ml[[4]][[1]]), 2)
expect_equal(ncol(ml[[5]][[1]]), 2)
expect_equal(ncol(ml[[6]][[1]]), 2)
expect_equal(ncol(ml[[7]]), ncol(testPE))
expect_silent(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = TRUE))
expect_is(ml, 'list')
expect_equal(length(ml), 7)
expect_equal(length(ml[['models']][[1]]), nrow(testPE))
expect_equal(length(ml[['models']][[2]]), nrow(testPE))
expect_is(ml[['models']][[1]][[2]], 'nls')
expect_equal(names(attributes(ml)), c('names', 'loopCols', 'time', 'cond', 'assayName', 'mode'))
expect_silent(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = TRUE,
verbose = FALSE,
returnModel = TRUE))
expect_is(ml, 'list')
expect_equal(length(ml), 8)
expect_equal(length(ml[['models']][[1]]), nrow(testPE))
expect_equal(length(ml[['models']][[2]]), nrow(testPE))
expect_is(ml[['models']][[1]][[2]], 'nls')
expect_equal(names(attributes(ml)), c('names', 'loopCols', 'time', 'cond', 'assayName', 'mode'))
colnames(testPE) <- LETTERS[1:14]
rownames(testPE) <- LETTERS[1:10]
expect_silent(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = TRUE))
expect_equal(rownames(ml[[1]]), rownames(testPE))
expect_equal(colnames(ml[[1]]), colnames(testPE))
expect_equal(rownames(ml[[2]]), rownames(testPE))
expect_equal(colnames(ml[[2]]), c('OW40', 'OW450'))
})
test_that("modelturnover peptideExperiment works", {
wormsPE <- calculateIsotopeFraction(wormsPE, ratioAssay = 'ratio')
testPE <- wormsPE[1:10,]
testPE <- testPE@SilacPeptideExperiment
metadata(testPE)[['proteinCol']] <- 'Leading.razor.protein'
## 1 model per peptide
expect_message(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = FALSE,
mode = 'peptide'))
expect_is(ml, 'list')
expect_equal(names(ml), c('residuals', 'stderror', 'param_values',
'param_pval', 'param_tval', 'param_stderror'))
#expect_equal(unname(sapply(ml, class)), c(rep('matrix', 2), rep('list', 4)))
expect_equal(nrow(ml[[1]]), nrow(testPE))
expect_equal(nrow(ml[[2]]), nrow(testPE))
expect_equal(ncol(ml[[1]]), ncol(testPE))
expect_equal(ncol(ml[[2]]), 2)
expect_equal(ncol(ml[[3]][[1]]), 2)
expect_equal(ncol(ml[[4]][[1]]), 2)
expect_equal(ncol(ml[[5]][[1]]), 2)
expect_equal(ncol(ml[[6]][[1]]), 2)
expect_warning(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = TRUE,
verbose = FALSE,
returnModel = FALSE,
mode = 'peptide'))
expect_is(ml, 'list')
expect_equal(names(ml), c('residuals', 'stderror', 'param_values',
'param_pval', 'param_tval', 'param_stderror',
'weights'))
#expect_equal(unname(sapply(ml, class)), c(rep('matrix', 2), rep('list', 4), 'matrix'))
expect_equal(nrow(ml[[1]]), nrow(testPE))
expect_equal(nrow(ml[[2]]), nrow(testPE))
expect_equal(ncol(ml[[1]]), ncol(testPE))
expect_equal(ncol(ml[[2]]), 2)
expect_equal(ncol(ml[[3]][[1]]), 2)
expect_equal(ncol(ml[[4]][[1]]), 2)
expect_equal(ncol(ml[[5]][[1]]), 2)
expect_equal(ncol(ml[[6]][[1]]), 2)
expect_equal(ncol(ml[[7]]), ncol(testPE))
expect_message(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = TRUE,
mode = 'peptide'))
expect_is(ml, 'list')
expect_equal(length(ml), 7)
expect_equal(length(ml[['models']][[1]]), nrow(testPE))
expect_equal(length(ml[['models']][[2]]), nrow(testPE))
expect_is(ml[['models']][[1]][[6]], 'nls')
expect_equal(names(attributes(ml)), c('names','loopCols', 'time', 'cond', 'assayName', 'mode'))
expect_warning(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = TRUE,
verbose = FALSE,
returnModel = TRUE,
mode = 'peptide'))
expect_is(ml, 'list')
expect_equal(length(ml), 8)
expect_equal(length(ml[['models']][[1]]), nrow(testPE))
expect_equal(length(ml[['models']][[2]]), nrow(testPE))
expect_is(ml[['models']][[1]][[6]], 'nls')
expect_equal(names(attributes(ml)), c('names','loopCols', 'time', 'cond', 'assayName', 'mode'))
colnames(testPE) <- LETTERS[1:14]
rownames(testPE) <- rowData(testPE)$Sequence
expect_message(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = TRUE,
mode = 'peptide'))
expect_equal(rownames(ml[[1]]), rownames(testPE))
expect_equal(colnames(ml[[1]]), colnames(testPE))
expect_equal(rownames(ml[[2]]), rownames(testPE))
expect_equal(colnames(ml[[2]]), c('OW40', 'OW450'))
## 1 model per protein
expect_message(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = FALSE,
mode = 'grouped'))
expect_is(ml, 'list')
expect_equal(names(ml), c('residuals', 'stderror', 'param_values',
'param_pval', 'param_tval', 'param_stderror'))
#expect_equal(unname(sapply(ml, class)), c(rep('matrix', 2), rep('list', 4)))
expect_equal(nrow(ml[[1]]), nrow(testPE))
expect_equal(nrow(ml[[2]]), 10)
expect_equal(nrow(ml[[3]][[1]]), 10)
expect_equal(nrow(ml[[4]][[1]]), 10)
expect_equal(nrow(ml[[5]][[1]]), 10)
expect_equal(nrow(ml[[6]][[1]]), 10)
expect_equal(ncol(ml[[1]]), ncol(testPE))
expect_equal(ncol(ml[[2]]), 2)
expect_equal(ncol(ml[[3]][[1]]), 2)
expect_equal(ncol(ml[[4]][[1]]), 2)
expect_equal(ncol(ml[[5]][[1]]), 2)
expect_equal(ncol(ml[[6]][[1]]), 2)
expect_warning(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = TRUE,
verbose = FALSE,
returnModel = FALSE,
mode = 'grouped'))
expect_is(ml, 'list')
expect_equal(names(ml), c('residuals', 'stderror', 'param_values',
'param_pval', 'param_tval', 'param_stderror',
'weights'))
#expect_equal(unname(sapply(ml, class)), c(rep('matrix', 2), rep('list', 4), 'matrix'))
expect_equal(nrow(ml[[1]]), nrow(testPE))
expect_equal(nrow(ml[[2]]), 10)
expect_equal(nrow(ml[[3]][[1]]), 10)
expect_equal(nrow(ml[[4]][[1]]), 10)
expect_equal(nrow(ml[[5]][[1]]), 10)
expect_equal(nrow(ml[[6]][[1]]), 10)
expect_equal(nrow(ml[[7]]), nrow(testPE))
expect_equal(ncol(ml[[1]]), ncol(testPE))
expect_equal(ncol(ml[[2]]), 2)
expect_equal(ncol(ml[[3]][[1]]), 2)
expect_equal(ncol(ml[[4]][[1]]), 2)
expect_equal(ncol(ml[[5]][[1]]), 2)
expect_equal(ncol(ml[[6]][[1]]), 2)
expect_equal(ncol(ml[[7]]), ncol(testPE))
expect_message(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = TRUE,
mode = 'grouped'))
expect_is(ml, 'list')
expect_equal(length(ml), 7)
expect_equal(length(ml[['models']][[1]]), 10)
expect_equal(length(ml[['models']][[2]]), 10)
expect_is(ml[['models']][[1]][[2]], 'nls')
expect_equal(names(attributes(ml)), c('names','loopCols', 'time', 'cond', 'prot', 'assayName', 'mode'))
expect_warning(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = TRUE,
verbose = FALSE,
returnModel = TRUE,
mode = 'grouped'))
expect_is(ml, 'list')
expect_equal(length(ml), 8)
expect_equal(length(ml[['models']][[1]]), 10)
expect_equal(length(ml[['models']][[2]]), 10)
expect_is(ml[['models']][[1]][[2]], 'nls')
expect_equal(names(attributes(ml)), c('names', 'loopCols', 'time', 'cond', 'prot', 'assayName', 'mode'))
expect_message(ml <- modelTurnover(x = testPE,
assayName = 'fraction',
formula = 'fraction ~ 1-exp(-k*t)',
start = list(k = 0.02),
robust = FALSE,
verbose = FALSE,
returnModel = TRUE,
mode = 'grouped'))
expect_equal(rownames(ml[[1]]), rownames(testPE))
expect_equal(colnames(ml[[1]]), colnames(testPE))
expect_equal(rownames(ml[[2]]),
unique(rowData(testPE)[,metaoptions(testPE)[['proteinCol']]]))
expect_equal(colnames(ml[[2]]), c('OW40', 'OW450'))
})
test_that("modelturnover differet nls algorithms works", {
frac <- 1 - exp(-0.02 * c(4, 8, 16, 24, 36, 48)) + rnorm(6, mean = 0, sd = 0.02)
data <- data.frame(t = c(4, 8, 16, 24, 36, 48),
fraction =frac)
formula <- as.formula('fraction ~ 1 - exp(-k * t)')
start <- list(k = 0.025)
robust <- FALSE
modeldata <- .modelTurnover(data = data,
formula = formula,
start = start,
robust = robust,
returnModel = FALSE)
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror,length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
modeldata <- .modelTurnover(data = data,
formula = formula,
start = start,
robust = robust,
returnModel = FALSE,
algorithm = 'plinear')
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror, length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
modeldata <- .modelTurnover(data = data,
formula = formula,
start = start,
robust = robust,
returnModel = FALSE,
algorithm = 'port')
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror,length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
frac <- 1 - exp(-0.02 * c(4, 8, 16, 24, 36, 48)) + rnorm(6, mean = 0, sd = 0.02)
data <- data.frame(t = c(4, 8, 16, 24, 36, 48),
fraction =frac)
formula <- as.formula('fraction ~ 1 - exp(-k * t) + b')
start <- list(k = 0.025, b = 0)
robust <- FALSE
modeldata <- .modelTurnover(data = data,
formula = formula,
start = start,
robust = robust,
returnModel = FALSE)
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror,length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
modeldata <- .modelTurnover(data = data,
formula = formula,
start = start,
robust = robust,
returnModel = FALSE,
algorithm = 'plinear')
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror, length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
modeldata <- .modelTurnover(data = data,
formula = formula,
start = start,
robust = robust,
returnModel = FALSE,
algorithm = 'port')
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror,length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
})
test_that("modelturnover differet nlrob algorithms works", {
frac <- 1 - exp(-0.02 * c(4, 8, 16, 24, 36, 48)) + rnorm(6, mean = 0, sd = 0.02)
data <- data.frame(t = c(4, 8, 16, 24, 36, 48),
fraction =frac)
formula <- as.formula('fraction ~ 1 - exp(-k * t) + b')
start <- list(k = 0.025, b = 0)
lower <- c(k = 0, b = 0)
upper <- c(k = 0.5, b = 0.1)
robust <- TRUE
modeldata <- .modelTurnover(data = data,
formula = formula,
start = start,
robust = robust,
returnModel = FALSE,
method = 'M')
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror,length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
modeldata <- .modelTurnover(data = data,
formula = formula,
lower = lower,
upper = upper,
robust = robust,
returnModel = FALSE,
method = 'MM')
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror, length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
modeldata <- .modelTurnover(data = data,
formula = formula,
lower = lower,
upper = upper,
robust = robust,
returnModel = FALSE,
method = 'tau')
expect_length(modeldata$residuals, nrow(data))
expect_length(modeldata$stderror, 1)
expect_length(modeldata$params.vals, length(start))
expect_length(modeldata$params.stderror,length(start))
expect_length(modeldata$params.tval, length(start))
expect_length(modeldata$params.pval, length(start))
modeldata <- expect_error(.modelTurnover(data = data,
formula = formula,
lower = lower,
upper = upper,
robust = robust,
returnModel = FALSE,
method = 'CM'))
modeldata <- expect_error(.modelTurnover(data = data,
formula = formula,
lower = lower,
upper = upper,
robust = robust,
returnModel = FALSE,
method = 'mtl', silent = FALSE))
})
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