context("Plotting")
test_that("Plotting mkinfit, mmkin and mixed model objects is reproducible", {
skip_on_cran()
plot_default_FOCUS_C_SFO <- function() plot(fits[["SFO", "FOCUS_C"]])
plot_res_FOCUS_C_SFO <- function() plot(fits[["SFO", "FOCUS_C"]], show_residuals = TRUE)
plot_res_FOCUS_C_SFO_2 <- function() plot_res(fits[["SFO", "FOCUS_C"]])
plot_sep_FOCUS_C_SFO <- function() plot_sep(fits[["SFO", "FOCUS_C"]])
mkinparplot_FOCUS_C_SFO <- function() mkinparplot(fits[["SFO", "FOCUS_C"]])
mkinerrplot_FOCUS_C_SFO <- function() mkinerrplot(fits[["SFO", "FOCUS_C"]])
mmkin_FOCUS_C <- function() plot(fits[, "FOCUS_C"])
mmkin_SFO <- function() plot(fits["SFO", c("FOCUS_C", "FOCUS_D")])
fit_D_obs_eigen <- suppressWarnings(mkinfit(SFO_SFO, FOCUS_2006_D, error_model = "obs", quiet = TRUE))
fit_C_tc <- mkinfit("SFO", FOCUS_2006_C, error_model = "tc", quiet = TRUE)
plot_errmod_fit_C_tc <- function() plot_err(fit_C_tc)
vdiffr::expect_doppelganger("mkinfit plot for FOCUS C with defaults", plot_default_FOCUS_C_SFO)
vdiffr::expect_doppelganger("mkinfit plot for FOCUS C with residuals like in gmkin", plot_res_FOCUS_C_SFO)
vdiffr::expect_doppelganger("plot_res for FOCUS C", plot_res_FOCUS_C_SFO_2)
vdiffr::expect_doppelganger("mkinfit plot for FOCUS C with sep = TRUE", plot_sep_FOCUS_C_SFO)
vdiffr::expect_doppelganger("mkinparplot for FOCUS C SFO", mkinparplot_FOCUS_C_SFO)
vdiffr::expect_doppelganger("mkinerrplot for FOCUS C SFO", mkinerrplot_FOCUS_C_SFO)
vdiffr::expect_doppelganger("mmkin plot for FOCUS C", mmkin_FOCUS_C)
vdiffr::expect_doppelganger("mmkin plot for SFO (FOCUS C and D)", mmkin_SFO)
vdiffr::expect_doppelganger("plot_errmod with FOCUS C tc", plot_errmod_fit_C_tc)
plot_res_sfo_sfo <- function() plot_res(f_sfo_sfo_desolve)
vdiffr::expect_doppelganger("plot_res for FOCUS D", plot_res_sfo_sfo)
plot_err_sfo_sfo <- function() plot_err(f_sfo_sfo_desolve)
vdiffr::expect_doppelganger("plot_err for FOCUS D", plot_err_sfo_sfo)
# UBA datasets
ds_uba <- lapply(experimental_data_for_UBA_2019[6:10],
function(x) subset(x$data[c("name", "time", "value")]))
names(ds_uba) <- paste("Dataset", 6:10)
sfo_sfo_uba <- mkinmod(parent = mkinsub("SFO", "A1"),
A1 = mkinsub("SFO"), quiet = TRUE)
dfop_sfo_uba <- mkinmod(parent = mkinsub("DFOP", "A1"),
A1 = mkinsub("SFO"), quiet = TRUE)
f_uba_mmkin <- mmkin(list("DFOP-SFO" = dfop_sfo_uba),
ds_uba, quiet = TRUE, cores = n_cores)
f_uba_dfop_sfo_mixed <- mixed(f_uba_mmkin["DFOP-SFO", ])
f_uba_dfop_sfo_saem <- saem(f_uba_mmkin["DFOP-SFO", ], quiet = TRUE, transformations = "saemix")
plot_dfop_sfo_mmkin <- function() plot(f_uba_dfop_sfo_mixed, pop_curve = TRUE)
vdiffr::expect_doppelganger("mixed model fit for mmkin object", plot_dfop_sfo_mmkin)
plot_dfop_sfo_saem_s <- function() plot(f_uba_dfop_sfo_saem)
vdiffr::expect_doppelganger("mixed model fit for saem object with saemix transformations", plot_dfop_sfo_saem_s)
skip_on_travis()
plot_dfop_sfo_nlme <- function() plot(dfop_nlme_1)
vdiffr::expect_doppelganger("mixed model fit for nlme object", plot_dfop_sfo_nlme)
#plot_dfop_sfo_mmkin <- function() plot(mixed(mmkin_dfop_sfo))
# Biphasic fits with lots of data and fits have lots of potential for differences
plot_dfop_sfo_nlme <- function() plot(nlme_dfop_sfo)
#plot_dfop_sfo_saem_s <- function() plot(saem_dfop_sfo_s)
plot_dfop_sfo_saem_m <- function() plot(saem_dfop_sfo_m)
vdiffr::expect_doppelganger("mixed model fit for saem object with mkin transformations", plot_dfop_sfo_saem_m)
# different results when working with eigenvalues
plot_errmod_fit_D_obs_eigen <- function() plot_err(fit_D_obs_eigen, sep_obs = FALSE)
vdiffr::expect_doppelganger("plot_errmod with FOCUS D obs eigen", plot_errmod_fit_D_obs_eigen)
})
