Nothing
suppressPackageStartupMessages(library(dplyr))
data("usz_13c")
data = usz_13c %>%
dplyr::filter( patient_id %in%
c("norm_001", "norm_002", "norm_004", "norm_007", "pat_004", "pat_012", "pat_023")) %>%
cleanup_data()
fit = nls_fit(data)
cm = comment(fit$data)
test_that("Result with default parameters is tbl_df with required columns",{
cf = coef_by_group(fit)
expect_s3_class(cf, "tbl_df")
expect_s3_class(cf, "coef_by_group")
expect_identical(ncol(cf), 7L)
expect_equal(names(cf), c("parameter", "method", "group", "estimate", "conf.low",
"conf.high", "diff_group"))
expect_identical(nrow(cf), 36L)
# The following test fails for multcomp 1.4-22
#expect_identical(unique(cf$diff_group), c("a", "c", "b", "bc", "ab"))
expect_equal(unique(cf$group),
c("liquid_normal", "liquid_patient", "solid_normal", "solid_patient"))
expect_equal(comment(cf), cm)
})
digs = function(x){
nchar(stringr::str_replace_all(paste(abs(x)), "[0\\.]",""))
}
test_that("Options digits is served",{
options(digits = 4)
cf = coef_by_group(fit)
expect_s3_class(cf, "tbl_df")
expect_lte(digs(cf[[1,"estimate"]]) ,4L)
})
test_that("Fit must be of class breathtestfit",{
expect_error(coef_by_group(NULL))
})
test_that("nlme_fit can be used to compute coefficients for multiple records in one group",{
data = cleanup_data(simulate_breathtest_data(4))
fit = nlme_fit(data)
cf = coef_by_group(fit)
expect_s3_class(cf, "tbl_df")
expect_identical(ncol(cf), 7L)
expect_equal(unique(cf$group), "A")
expect_equal(unique(cf$diff_group), "a")
expect_equal(names(cf), c("parameter", "method", "group", "estimate", "conf.low",
"conf.high", "diff_group"))
})
test_that("nlme_fit can be used to compute coefficients for multiple groups",{
skip_on_cran() # Does no converge on some CRAN settings
fit = nlme_fit(data)
cf = coef_by_group(fit)
expect_s3_class(cf, "tbl_df")
expect_identical(ncol(cf), 7L)
expect_equal(names(cf), c("parameter", "method", "group", "estimate", "conf.low",
"conf.high", "diff_group"))
})
test_that("Fit of a single curve returns valid data", {
data = usz_13c %>%
dplyr::filter( patient_id == "pat_001") %>%
cleanup_data()
comment(data) = "comment"
options(digits = 4)
fit = nls_fit(data)
cf = coef_by_group(fit)
expect_identical(ncol(cf), 7L)
expect_identical(nrow(cf), 9L)
expect_equal(cf$conf.low, rep(NA, 9L))
expect_equal(cf$conf.high, rep(NA, 9L))
expect_equal(comment(cf), "comment")
expect_lte(digs(cf[[1,"estimate"]]) ,4L)
})
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