tests/testthat/test-fun_evalPerformance.R
In RETURN-project/BenchmarkRecovery: Benchmark recovery indicators derived from remote sensing time series
context("Performance evaluation")
test_that("perfect fit", {
meas <- c(3,4,2,7,5,9)
val <-meas
MAPE <- mape(val, meas)
R2 <- rsq(val, meas)
RMSE <- rmse(val, meas)
expect_equal(MAPE, 0, tolerance = 1e-4)
expect_equal(RMSE, 0, tolerance = 1e-4)
expect_equal(R2, 1, tolerance = 1e-4)
})
test_that("systematic error of 1", {
meas <- c(3,4,2,7,5,9)
val <-meas + 1
MAPE <- mape(val, meas)
R2 <- rsq(val, meas)
RMSE <- rmse(val, meas)
expect_equal(MAPE, mean(1/val), tolerance = 1e-4)
expect_equal(RMSE, 1, tolerance = 1e-4)
expect_equal(R2, 1, tolerance = 1e-4)
})
test_that("random error",{
set.seed(197)
meas <- runif(10000, min=0, max=100)
set.seed(198)
val <- runif(10000, min=0, max=100)
R2 <- rsq(val, meas)
expect_equal(R2, 0, tolerance = 1e-3)
})
test_that("R2 - infinite value",{
set.seed(197)
meas <- runif(10000, min=0, max=100)
meas <- c(meas,Inf)
set.seed(198)
val <- runif(10000, min=0, max=100)
val <- c(val,Inf)
R2 <- rsq(val, meas)
expect_equal(R2, 0, tolerance = 1e-3)
})
test_that("R2 - too few observations",{
meas <- runif(2, min=0, max=100)
val <- runif(2, min=0, max=100)
R2 <- rsq(val, meas)
expect_equal(is.na(R2), T, tolerance = 1e-3)
})
test_that("plot sensitivity - basic test",{
# generate plot data
dat<- as.data.frame(list(
Metric = as.factor(c('RRI', 'RRI', 'R80p','R80p','YrYr','YrYr','RRI','RRI','R80p','R80p','YrYr','YrYr','RRI','RRI','R80p','R80p','YrYr','YrYr','RRI','RRI','R80p', 'R80p','YrYr','YrYr')),
Dense = as.factor(rep('anual',24)),
Smooth = as.factor(rep('raw',24)),
Period = as.factor(rep(c('Short','Long'),12)),
Seas = as.factor(rep(F,24)),
variable = as.factor(rep(c(0,0.08,0.02,0.03), each = 6)),
value = c(0.0652595473, 0.0228917011, 0.1122423965, 0.1037905358, 0.0438699878, 0.0071708630, 0.0004348493, 0.0011731170, 0.1652815743, 0.1187443186,
0.1445397388, 0.0341160032, 0.0447431895, 0.0063563672, 0.2544452752, 0.1292675690, 0.1124770530, 0.0019788734, 0.1212960214, 0.0437967248,
0.1994489563, 0.2174260878, 0.0622801004, 0.0041313903)
))
# plot
xlbl <- "Disturbance magnitude"
ylbl <- "R²"
scales <- 'free_y'
pl <- plotSens(dat, xlbl, ylbl, scales)
# test if plot settings are ok
expect_identical(pl$labels$x, xlbl)
expect_identical(pl$labels$y, ylbl)
expect_identical(pl$labels$colour, "interaction(Dense)")
expect_identical(pl$labels$group, "interaction(Smooth, Dense)")
})
test_that("plot parameter comparison - basic test",{
# generate data to plot
dat<- as.data.frame(list(
Metric = as.factor(rep(c('RRI', 'R80p', 'YrYr'),13)),
Dense = as.factor(rep('dense',39)),
Smooth = as.factor(rep('raw',39)),
Period = as.factor(rep(c('Long'),39)),
Seas = as.factor(rep(T,39)),
variable = as.factor(c(rep('no',3), rep(c(rep('low',3), rep('medium',3), rep('high',3)),4))),
value = c(0.2266946, 0.2046240, 0.4389999, 0.3989832, 0.3940083, 0.5494937, 0.3751496, 0.4020705, 0.5705492, 0.2348569,
0.2354719, 0.5004227, NA, NA, 0.4861462, 0.2752960, 0.3127486, 0.6335284, 0.1444104, 0.2464581,
0.2390760, 0.2127164, 0.2657129, 0.6347170, 0.3993802, 0.4135900, 0.6013086, 0.3351734, 0.3687597, 0.6041666,
0.1185523, 0.2066233, 0.5891457, 0.2608077, 0.3164531, 0.5620634, 0.4014608, 0.4023814, 0.5804983),
param = c(rep('Seasonal amplitude',12),rep(c('Recovery period', 'Disturbance timing','Disturbance magnitude'), each =9)),
paramType = c(rep('Environmental parameter',12),rep('Disturbance parameter',27))
))
# plot
xlbl <- 'Parameter value'
ylbl <- 'R²'
scales <- 'free_y'
pl <- plotEnv(dat, xlbl, ylbl, scales)
# test if plot settings are ok
expect_identical(pl$labels$x, xlbl)
expect_identical(pl$labels$y, ylbl)
expect_identical(pl$labels$colour, "Parameter")
expect_identical(pl$labels$group, "param")
})
test_that("plot metric comparison - basic test",{
# generate data to plot
dat<- as.data.frame(list(
Metric = as.factor(rep(c('RRI', 'R80p', 'YrYr'),13)),
value = c(0.2266946, 0.2046240, 0.4389999, 0.3989832, 0.3940083, 0.5494937, 0.3751496, 0.4020705, 0.5705492, 0.2348569,
0.2354719, 0.5004227, NA, NA, 0.4861462, 0.2752960, 0.3127486, 0.6335284, 0.1444104, 0.2464581,
0.2390760, 0.2127164, 0.2657129, 0.6347170, 0.3993802, 0.4135900, 0.6013086, 0.3351734, 0.3687597, 0.6041666,
0.1185523, 0.2066233, 0.5891457, 0.2608077, 0.3164531, 0.5620634, 0.4014608, 0.4023814, 0.5804983)
))
# plot
xlbl <- 'Metric'
ylbl <- 'R²'
scales <- 'free_y'
pl <- plotMet(dat, xlbl, ylbl)
# test if plot settings are ok
expect_identical(pl$labels$x, xlbl)
expect_identical(pl$labels$y, ylbl)
expect_identical(pl$labels$colour, "Metric")
})
test_that("plot sensitivity bar - basic test",{
# generate plot data
dat<- as.data.frame(list(
Metric = as.factor(c('RRI', 'RRI', 'R80p','R80p','YrYr','YrYr','RRI','RRI','R80p','R80p','YrYr','YrYr','RRI','RRI','R80p','R80p','YrYr','YrYr','RRI','RRI','R80p', 'R80p','YrYr','YrYr')),
Dense = as.factor(rep('annual',24)),
Smooth = as.factor(rep('raw',24)),
Period = as.factor(rep(c('Short','Long'),12)),
Seas = as.factor(rep(F,24)),
variable = as.factor(rep(c(0,0.08,0.02,0.03), each = 6)),
param = as.factor(rep('Disturbance magnitude', 24)),
paramType = as.factor(rep('Disturbance', 24)),
value = c(0.0652595473, 0.0228917011, 0.1122423965, 0.1037905358, 0.0438699878, 0.0071708630, 0.0004348493, 0.0011731170, 0.1652815743, 0.1187443186,
0.1445397388, 0.0341160032, 0.0447431895, 0.0063563672, 0.2544452752, 0.1292675690, 0.1124770530, 0.0019788734, 0.1212960214, 0.0437967248,
0.1994489563, 0.2174260878, 0.0622801004, 0.0041313903)
))
# plot
xlbl <- "Disturbance magnitude"
ylbl <- "R²"
scales <- 'free_y'
pl <- plotSensBar(dat, xlbl, ylbl, scales)
# test if plot settings are ok
expect_identical(pl$labels$x, xlbl)
expect_identical(pl$labels$y, ylbl)
expect_identical(pl$labels$colour, "param")
expect_identical(pl$labels$group, "interaction(variable, param)")
})
test_that("plot preprocessing bar - basic test",{
# generate plot data
dat<- as.data.frame(list(
Metric = as.factor(c('RRI', 'RRI', 'R80p','R80p','YrYr','YrYr','RRI','RRI','R80p','R80p','YrYr','YrYr','RRI','RRI','R80p','R80p','YrYr','YrYr','RRI','RRI','R80p', 'R80p','YrYr','YrYr')),
Dense = as.factor(rep('annual',24)),
Smooth = as.factor(rep('raw',24)),
Period = as.factor(rep(c('Short','Long'),12)),
Seas = as.factor(rep(F,24)),
variable = as.factor(rep(c(0,0.08,0.02,0.03), each = 6)),
param = as.factor(rep('Disturbance magnitude', 24)),
paramType = as.factor(rep('Disturbance', 24)),
value = c(0.0652595473, 0.0228917011, 0.1122423965, 0.1037905358, 0.0438699878, 0.0071708630, 0.0004348493, 0.0011731170, 0.1652815743, 0.1187443186,
0.1445397388, 0.0341160032, 0.0447431895, 0.0063563672, 0.2544452752, 0.1292675690, 0.1124770530, 0.0019788734, 0.1212960214, 0.0437967248,
0.1994489563, 0.2174260878, 0.0622801004, 0.0041313903)
))
# plot
xlbl <- "Disturbance magnitude"
ylbl <- "R²"
scales <- 'free_y'
pl <- pltPrepBox(dat, xlbl, ylbl, scales)
# test if plot settings are ok
expect_identical(pl$labels$x, xlbl)
expect_identical(pl$labels$y, ylbl)
expect_identical(pl$labels$colour, "interaction(Smooth)")
})
RETURN-project/BenchmarkRecovery documentation built on July 13, 2021, 5:47 p.m.
R Package Documentation
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We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
context("Performance evaluation")
test_that("perfect fit", {
meas <- c(3,4,2,7,5,9)
val <-meas
MAPE <- mape(val, meas)
R2 <- rsq(val, meas)
RMSE <- rmse(val, meas)
expect_equal(MAPE, 0, tolerance = 1e-4)
expect_equal(RMSE, 0, tolerance = 1e-4)
expect_equal(R2, 1, tolerance = 1e-4)
})
test_that("systematic error of 1", {
meas <- c(3,4,2,7,5,9)
val <-meas + 1
MAPE <- mape(val, meas)
R2 <- rsq(val, meas)
RMSE <- rmse(val, meas)
expect_equal(MAPE, mean(1/val), tolerance = 1e-4)
expect_equal(RMSE, 1, tolerance = 1e-4)
expect_equal(R2, 1, tolerance = 1e-4)
})
test_that("random error",{
set.seed(197)
meas <- runif(10000, min=0, max=100)
set.seed(198)
val <- runif(10000, min=0, max=100)
R2 <- rsq(val, meas)
expect_equal(R2, 0, tolerance = 1e-3)
})
test_that("R2 - infinite value",{
set.seed(197)
meas <- runif(10000, min=0, max=100)
meas <- c(meas,Inf)
set.seed(198)
val <- runif(10000, min=0, max=100)
val <- c(val,Inf)
R2 <- rsq(val, meas)
expect_equal(R2, 0, tolerance = 1e-3)
})
test_that("R2 - too few observations",{
meas <- runif(2, min=0, max=100)
val <- runif(2, min=0, max=100)
R2 <- rsq(val, meas)
expect_equal(is.na(R2), T, tolerance = 1e-3)
})
test_that("plot sensitivity - basic test",{
# generate plot data
dat<- as.data.frame(list(
Metric = as.factor(c('RRI', 'RRI', 'R80p','R80p','YrYr','YrYr','RRI','RRI','R80p','R80p','YrYr','YrYr','RRI','RRI','R80p','R80p','YrYr','YrYr','RRI','RRI','R80p', 'R80p','YrYr','YrYr')),
Dense = as.factor(rep('anual',24)),
Smooth = as.factor(rep('raw',24)),
Period = as.factor(rep(c('Short','Long'),12)),
Seas = as.factor(rep(F,24)),
variable = as.factor(rep(c(0,0.08,0.02,0.03), each = 6)),
value = c(0.0652595473, 0.0228917011, 0.1122423965, 0.1037905358, 0.0438699878, 0.0071708630, 0.0004348493, 0.0011731170, 0.1652815743, 0.1187443186,
0.1445397388, 0.0341160032, 0.0447431895, 0.0063563672, 0.2544452752, 0.1292675690, 0.1124770530, 0.0019788734, 0.1212960214, 0.0437967248,
0.1994489563, 0.2174260878, 0.0622801004, 0.0041313903)
))
# plot
xlbl <- "Disturbance magnitude"
ylbl <- "R²"
scales <- 'free_y'
pl <- plotSens(dat, xlbl, ylbl, scales)
# test if plot settings are ok
expect_identical(pl$labels$x, xlbl)
expect_identical(pl$labels$y, ylbl)
expect_identical(pl$labels$colour, "interaction(Dense)")
expect_identical(pl$labels$group, "interaction(Smooth, Dense)")
})
test_that("plot parameter comparison - basic test",{
# generate data to plot
dat<- as.data.frame(list(
Metric = as.factor(rep(c('RRI', 'R80p', 'YrYr'),13)),
Dense = as.factor(rep('dense',39)),
Smooth = as.factor(rep('raw',39)),
Period = as.factor(rep(c('Long'),39)),
Seas = as.factor(rep(T,39)),
variable = as.factor(c(rep('no',3), rep(c(rep('low',3), rep('medium',3), rep('high',3)),4))),
value = c(0.2266946, 0.2046240, 0.4389999, 0.3989832, 0.3940083, 0.5494937, 0.3751496, 0.4020705, 0.5705492, 0.2348569,
0.2354719, 0.5004227, NA, NA, 0.4861462, 0.2752960, 0.3127486, 0.6335284, 0.1444104, 0.2464581,
0.2390760, 0.2127164, 0.2657129, 0.6347170, 0.3993802, 0.4135900, 0.6013086, 0.3351734, 0.3687597, 0.6041666,
0.1185523, 0.2066233, 0.5891457, 0.2608077, 0.3164531, 0.5620634, 0.4014608, 0.4023814, 0.5804983),
param = c(rep('Seasonal amplitude',12),rep(c('Recovery period', 'Disturbance timing','Disturbance magnitude'), each =9)),
paramType = c(rep('Environmental parameter',12),rep('Disturbance parameter',27))
))
# plot
xlbl <- 'Parameter value'
ylbl <- 'R²'
scales <- 'free_y'
pl <- plotEnv(dat, xlbl, ylbl, scales)
# test if plot settings are ok
expect_identical(pl$labels$x, xlbl)
expect_identical(pl$labels$y, ylbl)
expect_identical(pl$labels$colour, "Parameter")
expect_identical(pl$labels$group, "param")
})
test_that("plot metric comparison - basic test",{
# generate data to plot
dat<- as.data.frame(list(
Metric = as.factor(rep(c('RRI', 'R80p', 'YrYr'),13)),
value = c(0.2266946, 0.2046240, 0.4389999, 0.3989832, 0.3940083, 0.5494937, 0.3751496, 0.4020705, 0.5705492, 0.2348569,
0.2354719, 0.5004227, NA, NA, 0.4861462, 0.2752960, 0.3127486, 0.6335284, 0.1444104, 0.2464581,
0.2390760, 0.2127164, 0.2657129, 0.6347170, 0.3993802, 0.4135900, 0.6013086, 0.3351734, 0.3687597, 0.6041666,
0.1185523, 0.2066233, 0.5891457, 0.2608077, 0.3164531, 0.5620634, 0.4014608, 0.4023814, 0.5804983)
))
# plot
xlbl <- 'Metric'
ylbl <- 'R²'
scales <- 'free_y'
pl <- plotMet(dat, xlbl, ylbl)
# test if plot settings are ok
expect_identical(pl$labels$x, xlbl)
expect_identical(pl$labels$y, ylbl)
expect_identical(pl$labels$colour, "Metric")
})
test_that("plot sensitivity bar - basic test",{
# generate plot data
dat<- as.data.frame(list(
Metric = as.factor(c('RRI', 'RRI', 'R80p','R80p','YrYr','YrYr','RRI','RRI','R80p','R80p','YrYr','YrYr','RRI','RRI','R80p','R80p','YrYr','YrYr','RRI','RRI','R80p', 'R80p','YrYr','YrYr')),
Dense = as.factor(rep('annual',24)),
Smooth = as.factor(rep('raw',24)),
Period = as.factor(rep(c('Short','Long'),12)),
Seas = as.factor(rep(F,24)),
variable = as.factor(rep(c(0,0.08,0.02,0.03), each = 6)),
param = as.factor(rep('Disturbance magnitude', 24)),
paramType = as.factor(rep('Disturbance', 24)),
value = c(0.0652595473, 0.0228917011, 0.1122423965, 0.1037905358, 0.0438699878, 0.0071708630, 0.0004348493, 0.0011731170, 0.1652815743, 0.1187443186,
0.1445397388, 0.0341160032, 0.0447431895, 0.0063563672, 0.2544452752, 0.1292675690, 0.1124770530, 0.0019788734, 0.1212960214, 0.0437967248,
0.1994489563, 0.2174260878, 0.0622801004, 0.0041313903)
))
# plot
xlbl <- "Disturbance magnitude"
ylbl <- "R²"
scales <- 'free_y'
pl <- plotSensBar(dat, xlbl, ylbl, scales)
# test if plot settings are ok
expect_identical(pl$labels$x, xlbl)
expect_identical(pl$labels$y, ylbl)
expect_identical(pl$labels$colour, "param")
expect_identical(pl$labels$group, "interaction(variable, param)")
})
test_that("plot preprocessing bar - basic test",{
# generate plot data
dat<- as.data.frame(list(
Metric = as.factor(c('RRI', 'RRI', 'R80p','R80p','YrYr','YrYr','RRI','RRI','R80p','R80p','YrYr','YrYr','RRI','RRI','R80p','R80p','YrYr','YrYr','RRI','RRI','R80p', 'R80p','YrYr','YrYr')),
Dense = as.factor(rep('annual',24)),
Smooth = as.factor(rep('raw',24)),
Period = as.factor(rep(c('Short','Long'),12)),
Seas = as.factor(rep(F,24)),
variable = as.factor(rep(c(0,0.08,0.02,0.03), each = 6)),
param = as.factor(rep('Disturbance magnitude', 24)),
paramType = as.factor(rep('Disturbance', 24)),
value = c(0.0652595473, 0.0228917011, 0.1122423965, 0.1037905358, 0.0438699878, 0.0071708630, 0.0004348493, 0.0011731170, 0.1652815743, 0.1187443186,
0.1445397388, 0.0341160032, 0.0447431895, 0.0063563672, 0.2544452752, 0.1292675690, 0.1124770530, 0.0019788734, 0.1212960214, 0.0437967248,
0.1994489563, 0.2174260878, 0.0622801004, 0.0041313903)
))
# plot
xlbl <- "Disturbance magnitude"
ylbl <- "R²"
scales <- 'free_y'
pl <- pltPrepBox(dat, xlbl, ylbl, scales)
# test if plot settings are ok
expect_identical(pl$labels$x, xlbl)
expect_identical(pl$labels$y, ylbl)
expect_identical(pl$labels$colour, "interaction(Smooth)")
})
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