# do not run the test on CRAN as they take too long
testthat::skip_on_cran()
# method: fit model and get predictions. Check these against others.
# load in ggplot
library(ggplot2)
# subset for the first TPC curve
data('chlorella_tpc')
d <- subset(chlorella_tpc, curve_id == 1)
modelname <- "gaussianmodified_2006"
# get start values and fit model
start_vals <- get_start_vals(d$temp, d$rate, model_name = modelname)
# fit model
mod <- nls.multstart::nls_multstart(rate~gaussianmodified_2006(temp = temp, rmax, topt, a, b),
data = d,
iter = c(4,4,4,4),
start_lower = start_vals - 10,
start_upper = start_vals + 10,
lower = get_lower_lims(d$temp, d$rate, model_name = modelname),
upper = get_upper_lims(d$temp, d$rate, model_name = modelname),
supp_errors = 'Y',
convergence_count = FALSE)
# get predictions
preds <- broom::augment(mod)
# dput(round(preds$.fitted, 1))
# plot
ggplot(preds) +
geom_point(aes(temp, rate)) +
geom_line(aes(temp, .fitted)) +
theme_bw()
# run test
testthat::test_that(paste(modelname, "function works"), {
testthat::expect_equal(
round(preds$.fitted, 1),
c(0.0, 0.0, 0.0, 0.5, 1.3, 1.3, 1.3, 1.3, 1.3, 1.3, 0.0, 0.0))
})
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