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-rw-r--r--R/nlme.mmkin.R30
-rw-r--r--man/nlme.mmkin.Rd9
-rw-r--r--test.log22
-rw-r--r--tests/testthat/test_nlme.R12
4 files changed, 33 insertions, 40 deletions
diff --git a/R/nlme.mmkin.R b/R/nlme.mmkin.R
index 0d6e6c37..c6f15c8e 100644
--- a/R/nlme.mmkin.R
+++ b/R/nlme.mmkin.R
@@ -130,9 +130,13 @@ get_deg_func <- function() {
#' anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs, f_nlme_dfop_sfo_tc)
#'
#' }
-nlme.mmkin <- function(model, data = sys.frame(sys.parent()),
- fixed, random = fixed,
- groups, start, correlation = NULL, weights = NULL,
+nlme.mmkin <- function(model, data = "auto",
+ fixed = lapply(as.list(names(mean_degparms(model))),
+ function(el) eval(parse(text = paste(el, 1, sep = "~")))),
+ random = pdDiag(fixed),
+ groups,
+ start = mean_degparms(model, random = TRUE),
+ correlation = NULL, weights = NULL,
subset, method = c("ML", "REML"),
na.action = na.fail, naPattern,
control = list(), verbose= FALSE)
@@ -143,8 +147,8 @@ nlme.mmkin <- function(model, data = sys.frame(sys.parent()),
# Warn in case arguments were used that are overriden
if (any(!is.na(match(names(thisCall),
- c("fixed", "data"))))) {
- warning("'nlme.mmkin' will redefine 'fixed' and 'data'")
+ c("data"))))) {
+ warning("'nlme.mmkin' will redefine 'data'")
}
deg_func <- nlme_function(model)
@@ -158,21 +162,13 @@ nlme.mmkin <- function(model, data = sys.frame(sys.parent()),
thisCall[["model"]] <- this_model
- mean_dp_start <- mean_degparms(model)
- dp_names <- names(mean_dp_start)
-
thisCall[["data"]] <- nlme_data(model)
- if (missing(start)) {
- thisCall[["start"]] <- mean_degparms(model, random = TRUE)
- }
+ thisCall[["start"]] <- start
- thisCall[["fixed"]] <- lapply(as.list(dp_names), function(el)
- eval(parse(text = paste(el, 1, sep = "~"))))
+ thisCall[["fixed"]] <- fixed
- if (missing(random)) {
- thisCall[["random"]] <- pdLogChol(thisCall[["fixed"]])
- }
+ thisCall[["random"]] <- random
error_model <- model[[1]]$err_mod
@@ -198,7 +194,7 @@ nlme.mmkin <- function(model, data = sys.frame(sys.parent()),
val$mkinmod <- model[[1]]$mkinmod
val$data <- thisCall[["data"]]
val$mmkin <- model
- val$mean_dp_start <- mean_dp_start
+ val$mean_dp_start <- start$fixed
val$transform_rates <- model[[1]]$transform_rates
val$transform_fractions <- model[[1]]$transform_fractions
val$solution_type <- model[[1]]$solution_type
diff --git a/man/nlme.mmkin.Rd b/man/nlme.mmkin.Rd
index 7a72a19a..f78256ac 100644
--- a/man/nlme.mmkin.Rd
+++ b/man/nlme.mmkin.Rd
@@ -8,11 +8,12 @@
\usage{
\method{nlme}{mmkin}(
model,
- data = sys.frame(sys.parent()),
- fixed,
- random = fixed,
+ data = "auto",
+ fixed = lapply(as.list(names(mean_degparms(model))), function(el) eval(parse(text =
+ paste(el, 1, sep = "~")))),
+ random = pdDiag(fixed),
groups,
- start,
+ start = mean_degparms(model, random = TRUE),
correlation = NULL,
weights = NULL,
subset,
diff --git a/test.log b/test.log
index 84a8748e..c977d6a0 100644
--- a/test.log
+++ b/test.log
@@ -6,17 +6,17 @@ Testing mkin
✔ | 2 | Export dataset for reading into CAKE
✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.9 s]
✔ | 4 | Calculation of FOCUS chi2 error levels [0.4 s]
-✔ | 7 | Fitting the SFORB model [3.4 s]
+✔ | 7 | Fitting the SFORB model [3.5 s]
✔ | 5 | Analytical solutions for coupled models [3.1 s]
✔ | 5 | Calculation of Akaike weights
✔ | 10 | Confidence intervals and p-values [1.0 s]
-✔ | 14 | Error model fitting [4.4 s]
+✔ | 14 | Error model fitting [4.7 s]
✔ | 4 | Test fitting the decline of metabolites from their maximum [0.3 s]
✔ | 1 | Fitting the logistic model [0.2 s]
✔ | 1 | Test dataset class mkinds used in gmkin
✔ | 1 | mkinfit features [0.3 s]
✔ | 12 | Special cases of mkinfit calls [0.7 s]
-✔ | 8 | mkinmod model generation and printing [0.2 s]
+✔ | 8 | mkinmod model generation and printing [0.3 s]
✔ | 3 | Model predictions with mkinpredict [0.4 s]
✔ | 14 2 | Evaluations according to 2015 NAFTA guidance [1.2 s]
────────────────────────────────────────────────────────────────────────────────
@@ -26,28 +26,24 @@ Reason: getRversion() < "4.1.0" is TRUE
Skip (test_nafta.R:55:5): Test data from Appendix D are correctly evaluated
Reason: getRversion() < "4.1.0" is TRUE
────────────────────────────────────────────────────────────────────────────────
-✖ | 7 1 | Nonlinear mixed-effects models [8.0 s]
-────────────────────────────────────────────────────────────────────────────────
-FAILURE (test_nlme.R:90:3): nlme_function works correctly
-`tmp <- update(m_nlme_mmkin)` did not produce any warnings.
-────────────────────────────────────────────────────────────────────────────────
+✔ | 9 | Nonlinear mixed-effects models [8.0 s]
✔ | 0 1 | Plotting [0.7 s]
────────────────────────────────────────────────────────────────────────────────
Skip (test_plot.R:25:3): Plotting mkinfit and mmkin objects is reproducible
Reason: getRversion() < "4.1.0" is TRUE
────────────────────────────────────────────────────────────────────────────────
✔ | 4 | Residuals extracted from mkinfit models
-✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.5 s]
-✔ | 4 | Summary [0.1 s]
+✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.7 s]
+✔ | 4 | Summary [0.2 s]
✔ | 1 | Summaries of old mkinfit objects
-✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.2 s]
+✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.3 s]
✔ | 9 | Hypothesis tests [7.2 s]
✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.4 s]
══ Results ═════════════════════════════════════════════════════════════════════
-Duration: 38.9 s
+Duration: 39.8 s
── Skipped tests ──────────────────────────────────────────────────────────────
● getRversion() < "4.1.0" is TRUE (3)
-[ FAIL 1 | WARN 0 | SKIP 3 | PASS 144 ]
+[ FAIL 0 | WARN 0 | SKIP 3 | PASS 146 ]
diff --git a/tests/testthat/test_nlme.R b/tests/testthat/test_nlme.R
index da994f49..989914da 100644
--- a/tests/testthat/test_nlme.R
+++ b/tests/testthat/test_nlme.R
@@ -56,18 +56,18 @@ test_that("nlme_function works correctly", {
m_nlme_raw_2 <- nlme(value ~ SSasymp(time, 0, parent_0, log_k_parent),
data = grouped_data,
fixed = parent_0 + log_k_parent ~ 1,
- random = pdLogChol(parent_0 + log_k_parent ~ 1),
+ random = pdDiag(parent_0 + log_k_parent ~ 1),
start = mean_degparms(f, random = TRUE),
control = list("msWarnNoConv" = FALSE))
expect_equal(m_nlme_raw_2$coefficients, m_nlme_mmkin$coefficients)
- anova_nlme <- anova(m_nlme_raw, m_nlme_mmkin)
+ anova_nlme <- anova(m_nlme_raw, m_nlme_mkin, m_nlme_raw_2, m_nlme_mmkin)
- # We get a slightly lower AIC with the improved starting values used within
- # nlme.mmkin, specifying also random effects
- expect_lt(anova_nlme["m_nlme_mmkin", "AIC"],
+ expect_equal(anova_nlme["m_nlme_mkin", "AIC"],
anova_nlme["m_nlme_raw", "AIC"])
+ expect_equal(anova_nlme["m_nlme_mmkin", "AIC"],
+ anova_nlme["m_nlme_raw_2", "AIC"])
m_nlme_raw_up_1 <- update(m_nlme_raw, random = log_k_parent_sink ~ 1)
# The following three calls give an error although they should
@@ -87,7 +87,7 @@ test_that("nlme_function works correctly", {
m_nlme_mkin_up_2 <- update(m_nlme_mkin, random = parent_0 ~ 1)
expect_equal(m_nlme_raw_up_2$coefficients, m_nlme_mkin_up_2$coefficients)
- expect_warning(tmp <- update(m_nlme_mmkin), "Iteration 1, LME step")
+ expect_silent(tmp <- update(m_nlme_mmkin))
geomean_dt50_mmkin <- exp(mean(log((sapply(f, function(x) endpoints(x)$distimes["parent", "DT50"])))))
expect_equal(round(endpoints(m_nlme_mmkin)$distimes["parent", "DT50"]), round(geomean_dt50_mmkin))

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