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-rw-r--r--NAMESPACE4
-rw-r--r--NEWS.md2
-rw-r--r--R/memkin.R170
-rw-r--r--R/mkinsub.R11
-rw-r--r--R/nlme.R213
-rw-r--r--check.log71
-rw-r--r--man/memkin.Rd84
-rw-r--r--man/mkinsub.Rd11
-rw-r--r--man/nlme.Rd134
-rw-r--r--vignettes/web_only/FOCUS_Z.R115
-rw-r--r--vignettes/web_only/compiled_models.R61
11 files changed, 368 insertions, 508 deletions
diff --git a/NAMESPACE b/NAMESPACE
index b16541ca..1b53f101 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -45,7 +45,7 @@ export(max_twa_fomc)
export(max_twa_hs)
export(max_twa_parent)
export(max_twa_sfo)
-export(memkin)
+export(mean_degparms)
export(mkin_long_to_wide)
export(mkin_wide_to_long)
export(mkinds)
@@ -60,6 +60,8 @@ export(mkinresplot)
export(mkinsub)
export(mmkin)
export(nafta)
+export(nlme_data)
+export(nlme_function)
export(parms)
export(plot_err)
export(plot_res)
diff --git a/NEWS.md b/NEWS.md
index a2258e77..46d2d711 100644
--- a/NEWS.md
+++ b/NEWS.md
@@ -1,5 +1,7 @@
# mkin 0.9.49.10 (unreleased)
+- 'mean_degparms, nlme_data, nlme_function': Three new functions to facilitate building nlme models from mmkin row objects
+
- 'endpoints': Don't return the SFORB list component if it's empty. This reduces distraction and complies with the documentation
- Article in compiled models: Add some platform specific code and suppress warnings about zero values being removed from the FOCUS D dataset
diff --git a/R/memkin.R b/R/memkin.R
deleted file mode 100644
index 8a71484e..00000000
--- a/R/memkin.R
+++ /dev/null
@@ -1,170 +0,0 @@
-#' Estimation of parameter distributions from mmkin row objects
-#'
-#' This function sets up and attempts to fit a mixed effects model to
-#' an mmkin row object which is essentially a list of mkinfit objects
-#' that have been obtained by fitting the same model to a list of
-#' datasets.
-#'
-#' @param object An mmkin row object containing several fits of the same model to different datasets
-#' @param random_spec Either "auto" or a specification of random effects for \code{\link{nlme}}
-#' given as a character vector
-#' @param ... Additional arguments passed to \code{\link{nlme}}
-#' @import nlme
-#' @importFrom purrr map_dfr
-#' @return An nlme object
-#' @examples
-#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-#' m_SFO <- mkinmod(parent = mkinsub("SFO"))
-#' d_SFO_1 <- mkinpredict(m_SFO,
-#' c(k_parent_sink = 0.1),
-#' c(parent = 98), sampling_times)
-#' d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time")
-#' d_SFO_2 <- mkinpredict(m_SFO,
-#' c(k_parent_sink = 0.05),
-#' c(parent = 102), sampling_times)
-#' d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time")
-#' d_SFO_3 <- mkinpredict(m_SFO,
-#' c(k_parent_sink = 0.02),
-#' c(parent = 103), sampling_times)
-#' d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")
-#'
-#' d1 <- add_err(d_SFO_1, function(value) 3, n = 1)
-#' d2 <- add_err(d_SFO_2, function(value) 2, n = 1)
-#' d3 <- add_err(d_SFO_3, function(value) 4, n = 1)
-#' ds <- c(d1 = d1, d2 = d2, d3 = d3)
-#'
-#' f <- mmkin("SFO", ds)
-#' x <- memkin(f)
-#' summary(x)
-#'
-#' ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
-#' function(x) x$data[c("name", "time", "value")])
-#' m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
-#' A1 = mkinsub("SFO"), use_of_ff = "min")
-#' m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"),
-#' A1 = mkinsub("SFO"), use_of_ff = "max")
-#' m_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
-#' A1 = mkinsub("SFO"))
-#' m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
-#' A1 = mkinsub("SFO"))
-#' m_sforb_sfo <- mkinmod(parent = mkinsub("SFORB", "A1"),
-#' A1 = mkinsub("SFO"))
-#'
-#' f_2 <- mmkin(list("SFO-SFO" = m_sfo_sfo,
-#' "SFO-SFO-ff" = m_sfo_sfo_ff,
-#' "FOMC-SFO" = m_fomc_sfo,
-#' "DFOP-SFO" = m_dfop_sfo,
-#' "SFORB-SFO" = m_sforb_sfo),
-#' ds_2)
-#'
-#' f_nlme_sfo_sfo <- memkin(f_2[1, ])
-#' f_nlme_sfo_sfo_2 <- memkin(f_2[1, ], "pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1)") # explicit
-#' f_nlme_sfo_sfo_3 <- memkin(f_2[1, ], "pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 ~ 1)") # reduced
-#' f_nlme_sfo_sfo_4 <- memkin(f_2[1, ], "pdDiag(parent_0 + log_k_parent_sink ~ 1)") # further reduced
-#' \dontrun{
-#' f_nlme_sfo_sfo_ff <- memkin(f_2[2, ]) # does not converge with maxIter = 50
-#' }
-#' f_nlme_fomc_sfo <- memkin(f_2[3, ])
-#' \dontrun{
-#' f_nlme_dfop_sfo <- memkin(f_2[4, ]) # apparently underdetermined
-#' f_nlme_sforb_sfo <- memkin(f_2[5, ]) # also does not converge
-#' }
-#' anova(f_nlme_fomc_sfo, f_nlme_sfo_sfo, f_nlme_sfo_sfo_4)
-#' @export
-memkin <- function(object, random_spec = "auto", ...) {
- if (nrow(object) > 1) stop("Only row objects allowed")
- ds_names <- colnames(object)
-
- p_mat_start_trans <- sapply(object, parms, transformed = TRUE)
- colnames(p_mat_start_trans) <- ds_names
-
- p_names_mean_function <- setdiff(rownames(p_mat_start_trans), names(object[[1]]$errparms))
- p_start_mean_function <- apply(p_mat_start_trans[p_names_mean_function, ], 1, mean)
-
- ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")])
- names(ds_list) <- ds_names
- ds_nlme <- purrr::map_dfr(ds_list, function(x) x, .id = "ds")
- ds_nlme$variable <- as.character(ds_nlme$variable)
- ds_nlme_grouped <- groupedData(observed ~ time | ds, ds_nlme)
-
- mkin_model <- object[[1]]$mkinmod
-
- # Inspired by https://stackoverflow.com/a/12983961/3805440
- # and https://stackoverflow.com/a/26280789/3805440
- model_function_alist <- replicate(length(p_names_mean_function) + 2, substitute())
- names(model_function_alist) <- c("name", "time", p_names_mean_function)
-
- model_function_body <- quote({
- arg_frame <- as.data.frame(as.list((environment())), stringsAsFactors = FALSE)
- res_frame <- arg_frame[1:2]
- parm_frame <- arg_frame[-(1:2)]
- parms_unique <- unique(parm_frame)
-
- n_unique <- nrow(parms_unique)
-
- times_ds <- list()
- names_ds <- list()
- for (i in 1:n_unique) {
- times_ds[[i]] <-
- arg_frame[which(arg_frame[[3]] == parms_unique[i, 1]), "time"]
- names_ds[[i]] <-
- arg_frame[which(arg_frame[[3]] == parms_unique[i, 1]), "name"]
- }
-
- res_list <- lapply(1:n_unique, function(x) {
- transparms_optim <- unlist(parms_unique[x, , drop = TRUE])
- parms_fixed <- object[[1]]$bparms.fixed
-
- odeini_optim_parm_names <- grep('_0$', names(transparms_optim), value = TRUE)
- odeini_optim <- transparms_optim[odeini_optim_parm_names]
- names(odeini_optim) <- gsub('_0$', '', odeini_optim_parm_names)
- odeini_fixed_parm_names <- grep('_0$', names(parms_fixed), value = TRUE)
- odeini_fixed <- parms_fixed[odeini_fixed_parm_names]
- names(odeini_fixed) <- gsub('_0$', '', odeini_fixed_parm_names)
- odeini <- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)]
-
- ode_transparms_optim_names <- setdiff(names(transparms_optim), odeini_optim_parm_names)
- odeparms_optim <- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], mkin_model,
- transform_rates = object[[1]]$transform_rates,
- transform_fractions = object[[1]]$transform_fractions)
- odeparms_fixed_names <- setdiff(names(parms_fixed), odeini_fixed_parm_names)
- odeparms_fixed <- parms_fixed[odeparms_fixed_names]
- odeparms <- c(odeparms_optim, odeparms_fixed)
-
- out_wide <- mkinpredict(mkin_model,
- odeparms = odeparms, odeini = odeini,
- solution_type = object[[1]]$solution_type,
- outtimes = sort(unique(times_ds[[x]])))
- out_array <- out_wide[, -1, drop = FALSE]
- rownames(out_array) <- as.character(unique(times_ds[[x]]))
- out_times <- as.character(times_ds[[x]])
- out_names <- as.character(names_ds[[x]])
- out_values <- mapply(function(times, names) out_array[times, names],
- out_times, out_names)
- return(as.numeric(out_values))
- })
- res <- unlist(res_list)
- return(res)
- })
- model_function <- as.function(c(model_function_alist, model_function_body))
- # For some reason, using envir = parent.frame() here is not enough,
- # we need to use assign
- assign("model_function", model_function, envir = parent.frame())
-
- random_spec <- if (random_spec[1] == "auto") {
- paste0("pdDiag(", paste(p_names_mean_function, collapse = " + "), " ~ 1),\n")
- } else {
- paste0(random_spec, ",\n")
- }
- nlme_call_text <- paste0(
- "nlme(observed ~ model_function(variable, time, ",
- paste(p_names_mean_function, collapse = ", "), "),\n",
- " data = ds_nlme_grouped,\n",
- " fixed = ", paste(p_names_mean_function, collapse = " + "), " ~ 1,\n",
- " random = ", random_spec, "\n",
- " start = p_start_mean_function)\n")
-
- f_nlme <- eval(parse(text = nlme_call_text))
-
- return(f_nlme)
-}
diff --git a/R/mkinsub.R b/R/mkinsub.R
index db91ca00..f87c230a 100644
--- a/R/mkinsub.R
+++ b/R/mkinsub.R
@@ -27,11 +27,12 @@
#' parent = mkinsub("SFO", "m1"),
#' m1 = mkinsub("SFO"))
#'
-#' # Now supplying full names
-#' SFO_SFO.2 <- mkinmod(
-#' parent = mkinsub("SFO", "m1", full_name = "Test compound"),
-#' m1 = mkinsub("SFO", full_name = "Metabolite M1"))
-#'
+#' \dontrun{
+#' # Now supplying full names
+#' SFO_SFO.2 <- mkinmod(
+#' parent = mkinsub("SFO", "m1", full_name = "Test compound"),
+#' m1 = mkinsub("SFO", full_name = "Metabolite M1"))
+#' }
#' @export
mkinsub <- function(submodel, to = NULL, sink = TRUE, full_name = NA)
{
diff --git a/R/nlme.R b/R/nlme.R
new file mode 100644
index 00000000..b17fe15a
--- /dev/null
+++ b/R/nlme.R
@@ -0,0 +1,213 @@
+#' Estimation of parameter distributions from mmkin row objects
+#'
+#' This function sets up and attempts to fit a mixed effects model to
+#' an mmkin row object. An mmkin row object is essentially a list of mkinfit
+#' objects that have been obtained by fitting the same model to a list of
+#' datasets.
+#'
+#' @param object An mmkin row object containing several fits of the same model to different datasets
+#' @import nlme
+#' @importFrom purrr map_dfr
+#' @return A named vector containing mean values of the fitted degradation model parameters
+#' @rdname nlme
+#' @examples
+#' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+#' m_SFO <- mkinmod(parent = mkinsub("SFO"))
+#' d_SFO_1 <- mkinpredict(m_SFO,
+#' c(k_parent_sink = 0.1),
+#' c(parent = 98), sampling_times)
+#' d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time")
+#' d_SFO_2 <- mkinpredict(m_SFO,
+#' c(k_parent_sink = 0.05),
+#' c(parent = 102), sampling_times)
+#' d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time")
+#' d_SFO_3 <- mkinpredict(m_SFO,
+#' c(k_parent_sink = 0.02),
+#' c(parent = 103), sampling_times)
+#' d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")
+#'
+#' d1 <- add_err(d_SFO_1, function(value) 3, n = 1)
+#' d2 <- add_err(d_SFO_2, function(value) 2, n = 1)
+#' d3 <- add_err(d_SFO_3, function(value) 4, n = 1)
+#' ds <- c(d1 = d1, d2 = d2, d3 = d3)
+#'
+#' f <- mmkin("SFO", ds, cores = 1, quiet = TRUE)
+#' mean_dp <- mean_degparms(f)
+#' grouped_data <- nlme_data(f)
+#' nlme_f <- nlme_function(f)
+#'
+#' library(nlme)
+#' m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),
+#' data = grouped_data,
+#' fixed = parent_0 + log_k_parent_sink ~ 1,
+#' random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
+#' start = mean_dp)
+#' summary(m_nlme)
+#'
+#' \dontrun{
+#' Test on some real data
+#' ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
+#' function(x) x$data[c("name", "time", "value")])
+#' m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
+#' A1 = mkinsub("SFO"), use_of_ff = "min")
+#' m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"),
+#' A1 = mkinsub("SFO"), use_of_ff = "max")
+#' m_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
+#' A1 = mkinsub("SFO"))
+#' m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
+#' A1 = mkinsub("SFO"))
+#' m_sforb_sfo <- mkinmod(parent = mkinsub("SFORB", "A1"),
+#' A1 = mkinsub("SFO"))
+#'
+#' f_2 <- mmkin(list("SFO-SFO" = m_sfo_sfo,
+#' "SFO-SFO-ff" = m_sfo_sfo_ff,
+#' "FOMC-SFO" = m_fomc_sfo,
+#' "DFOP-SFO" = m_dfop_sfo,
+#' "SFORB-SFO" = m_sforb_sfo),
+#' ds_2)
+#'
+#' grouped_data_2 <- nlme_data(f_2["SFO-SFO", ])
+#'
+#' mean_dp_sfo_sfo <- mean_degparms(f_2["SFO-SFO", ])
+#' mean_dp_sfo_sfo_ff <- mean_degparms(f_2["SFO-SFO-ff", ])
+#' mean_dp_fomc_sfo <- mean_degparms(f_2["FOMC-SFO", ])
+#' mean_dp_dfop_sfo <- mean_degparms(f_2["DFOP-SFO", ])
+#' mean_dp_sforb_sfo <- mean_degparms(f_2["SFORB-SFO", ])
+#'
+#' nlme_f_sfo_sfo <- nlme_function(f_2["SFO-SFO", ])
+#' nlme_f_sfo_sfo_ff <- nlme_function(f_2["SFO-SFO-ff", ])
+#' nlme_f_fomc_sfo <- nlme_function(f_2["FOMC-SFO", ])
+#'
+#' # Allowing for correlations between random effects leads to non-convergence
+#' f_nlme_sfo_sfo <- nlme(value ~ nlme_f_sfo_sfo(name, time,
+#' parent_0, log_k_parent_sink, log_k_parent_A1, log_k_A1_sink),
+#' data = grouped_data_2,
+#' fixed = parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1,
+#' random = pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1),
+#' start = mean_dp_sfo_sfo)
+#'
+#' # The same model fitted with transformed formation fractions does not converge
+#' f_nlme_sfo_sfo_ff <- nlme(value ~ nlme_f_sfo_sfo_ff(name, time,
+#' parent_0, log_k_parent, log_k_A1, f_parent_ilr_1),
+#' data = grouped_data_2,
+#' fixed = parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~ 1,
+#' random = pdDiag(parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~ 1),
+#' start = mean_dp_sfo_sfo_ff)
+#'
+#' # It does converge with this version of reduced random effects
+#' f_nlme_sfo_sfo_ff <- nlme(value ~ nlme_f_sfo_sfo_ff(name, time,
+#' parent_0, log_k_parent, log_k_A1, f_parent_ilr_1),
+#' data = grouped_data_2,
+#' fixed = parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~ 1,
+#' random = pdDiag(parent_0 + log_k_parent ~ 1),
+#' start = mean_dp_sfo_sfo_ff)
+#'
+#' f_nlme_fomc_sfo <- nlme(value ~ nlme_f_fomc_sfo(name, time,
+#' parent_0, log_alpha, log_beta, log_k_A1, f_parent_ilr_1),
+#' data = grouped_data_2,
+#' fixed = parent_0 + log_alpha + log_beta + log_k_A1 + f_parent_ilr_1 ~ 1,
+#' random = pdDiag(parent_0 + log_alpha + log_beta + log_k_A1 + f_parent_ilr_1 ~ 1),
+#' start = mean_dp_fomc_sfo)
+#'
+#' # DFOP-SFO and SFORB-SFO did not converge with full random effects
+#'
+#' anova(f_nlme_fomc_sfo, f_nlme_sfo_sfo)
+#' }
+#' @export
+mean_degparms <- function(object) {
+ if (nrow(object) > 1) stop("Only row objects allowed")
+ p_mat_start_trans <- sapply(object, parms, transformed = TRUE)
+ mean_degparm_names <- setdiff(rownames(p_mat_start_trans), names(object[[1]]$errparms))
+ res <- apply(p_mat_start_trans[mean_degparm_names, ], 1, mean)
+ return(res)
+}
+
+#' @rdname nlme
+#' @importFrom purrr map_dfr
+#' @return A groupedData data object
+#' @export
+nlme_data <- function(object) {
+ if (nrow(object) > 1) stop("Only row objects allowed")
+ ds_names <- colnames(object)
+
+ ds_list <- lapply(object, function(x) x$data[c("time", "variable", "observed")])
+ names(ds_list) <- ds_names
+ ds_nlme <- purrr::map_dfr(ds_list, function(x) x, .id = "ds")
+ ds_nlme$variable <- as.character(ds_nlme$variable)
+ ds_nlme_renamed <- data.frame(ds = ds_nlme$ds, name = ds_nlme$variable,
+ time = ds_nlme$time, value = ds_nlme$observed,
+ stringsAsFactors = FALSE)
+ ds_nlme_grouped <- groupedData(value ~ time | ds, ds_nlme_renamed)
+ return(ds_nlme_grouped)
+}
+
+#' @rdname nlme
+#' @return A function that can be used with nlme
+#' @export
+nlme_function <- function(object) {
+ if (nrow(object) > 1) stop("Only row objects allowed")
+
+ mkin_model <- object[[1]]$mkinmod
+
+ degparm_names <- names(mean_degparms(object))
+
+ # Inspired by https://stackoverflow.com/a/12983961/3805440
+ # and https://stackoverflow.com/a/26280789/3805440
+ model_function_alist <- replicate(length(degparm_names) + 2, substitute())
+ names(model_function_alist) <- c("name", "time", degparm_names)
+
+ model_function_body <- quote({
+ arg_frame <- as.data.frame(as.list((environment())), stringsAsFactors = FALSE)
+ res_frame <- arg_frame[1:2]
+ parm_frame <- arg_frame[-(1:2)]
+ parms_unique <- unique(parm_frame)
+
+ n_unique <- nrow(parms_unique)
+
+ times_ds <- list()
+ names_ds <- list()
+ for (i in 1:n_unique) {
+ times_ds[[i]] <-
+ arg_frame[which(arg_frame[[3]] == parms_unique[i, 1]), "time"]
+ names_ds[[i]] <-
+ arg_frame[which(arg_frame[[3]] == parms_unique[i, 1]), "name"]
+ }
+
+ res_list <- lapply(1:n_unique, function(x) {
+ transparms_optim <- unlist(parms_unique[x, , drop = TRUE])
+ parms_fixed <- object[[1]]$bparms.fixed
+
+ odeini_optim_parm_names <- grep('_0$', names(transparms_optim), value = TRUE)
+ odeini_optim <- transparms_optim[odeini_optim_parm_names]
+ names(odeini_optim) <- gsub('_0$', '', odeini_optim_parm_names)
+ odeini_fixed_parm_names <- grep('_0$', names(parms_fixed), value = TRUE)
+ odeini_fixed <- parms_fixed[odeini_fixed_parm_names]
+ names(odeini_fixed) <- gsub('_0$', '', odeini_fixed_parm_names)
+ odeini <- c(odeini_optim, odeini_fixed)[names(mkin_model$diffs)]
+
+ ode_transparms_optim_names <- setdiff(names(transparms_optim), odeini_optim_parm_names)
+ odeparms_optim <- backtransform_odeparms(transparms_optim[ode_transparms_optim_names], mkin_model,
+ transform_rates = object[[1]]$transform_rates,
+ transform_fractions = object[[1]]$transform_fractions)
+ odeparms_fixed_names <- setdiff(names(parms_fixed), odeini_fixed_parm_names)
+ odeparms_fixed <- parms_fixed[odeparms_fixed_names]
+ odeparms <- c(odeparms_optim, odeparms_fixed)
+
+ out_wide <- mkinpredict(mkin_model,
+ odeparms = odeparms, odeini = odeini,
+ solution_type = object[[1]]$solution_type,
+ outtimes = sort(unique(times_ds[[x]])))
+ out_array <- out_wide[, -1, drop = FALSE]
+ rownames(out_array) <- as.character(unique(times_ds[[x]]))
+ out_times <- as.character(times_ds[[x]])
+ out_names <- as.character(names_ds[[x]])
+ out_values <- mapply(function(times, names) out_array[times, names],
+ out_times, out_names)
+ return(as.numeric(out_values))
+ })
+ res <- unlist(res_list)
+ return(res)
+ })
+ model_function <- as.function(c(model_function_alist, model_function_body))
+ return(model_function)
+}
diff --git a/check.log b/check.log
index 3bf43d07..0a75a713 100644
--- a/check.log
+++ b/check.log
@@ -24,27 +24,7 @@ Maintainer: ‘Johannes Ranke <jranke@uni-bremen.de>’
* checking for future file timestamps ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
-* checking top-level files ... WARNING
-Conversion of ‘README.md’ failed:
-[WARNING] This document format requires a nonempty <title> element.
- Please specify either ‘title’ or ‘pagetitle’ in the metadata.
- Falling back to ‘README’
-Could not fetch https://codecov.io/github/jranke/mkin/branch/master/graphs/badge.svg
-HttpExceptionRequest Request {
- host = "codecov.io"
- port = 443
- secure = True
- requestHeaders = []
- path = "/github/jranke/mkin/branch/master/graphs/badge.svg"
- queryString = ""
- method = "GET"
- proxy = Nothing
- rawBody = False
- redirectCount = 10
- responseTimeout = ResponseTimeoutDefault
- requestVersion = HTTP/1.1
-}
- ConnectionTimeout
+* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
@@ -64,13 +44,7 @@ HttpExceptionRequest Request {
* checking R code for possible problems ... OK
* checking Rd files ... OK
* checking Rd metadata ... OK
-* checking Rd line widths ... NOTE
-Rd file 'memkin.Rd':
- \examples lines wider than 100 characters:
- f_nlme_sfo_sfo_2 <- memkin(f_2[1, ], "pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1)") # explicit
- f_nlme_sfo_sfo_3 <- memkin(f_2[1, ], "pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 ~ 1)") # reduced
-
-These lines will be truncated in the PDF manual.
+* checking Rd line widths ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
@@ -82,41 +56,7 @@ These lines will be truncated in the PDF manual.
* checking data for ASCII and uncompressed saves ... OK
* checking installed files from ‘inst/doc’ ... OK
* checking files in ‘vignettes’ ... OK
-* checking examples ... ERROR
-Running examples in ‘mkin-Ex.R’ failed
-The error most likely occurred in:
-
-> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
-> ### Name: memkin
-> ### Title: Estimation of parameter distributions from mmkin row objects
-> ### Aliases: memkin
->
-> ### ** Examples
->
-> sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-> m_SFO <- mkinmod(parent = mkinsub("SFO"))
-> d_SFO_1 <- mkinpredict(m_SFO,
-+ c(k_parent_sink = 0.1),
-+ c(parent = 98), sampling_times)
-> d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time")
-> d_SFO_2 <- mkinpredict(m_SFO,
-+ c(k_parent_sink = 0.05),
-+ c(parent = 102), sampling_times)
-> d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time")
-> d_SFO_3 <- mkinpredict(m_SFO,
-+ c(k_parent_sink = 0.02),
-+ c(parent = 103), sampling_times)
-> d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")
->
-> d1 <- add_err(d_SFO_1, function(value) 3, n = 1)
-> d2 <- add_err(d_SFO_2, function(value) 2, n = 1)
-> d3 <- add_err(d_SFO_3, function(value) 4, n = 1)
-> ds <- c(d1 = d1, d2 = d2, d3 = d3)
->
-> f <- mmkin("SFO", ds)
-Error in .check_ncores(cores) : 8 simultaneous processes spawned
-Calls: mmkin -> mclapply -> .check_ncores
-Execution halted
+* checking examples ... OK
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ... SKIPPED
* checking for unstated dependencies in vignettes ... OK
@@ -126,8 +66,5 @@ Execution halted
* checking for detritus in the temp directory ... OK
* DONE
-Status: 1 ERROR, 1 WARNING, 1 NOTE
-See
- ‘/home/jranke/git/mkin/mkin.Rcheck/00check.log’
-for details.
+Status: OK
diff --git a/man/memkin.Rd b/man/memkin.Rd
deleted file mode 100644
index 8ae6100a..00000000
--- a/man/memkin.Rd
+++ /dev/null
@@ -1,84 +0,0 @@
-% Generated by roxygen2: do not edit by hand
-% Please edit documentation in R/memkin.R
-\name{memkin}
-\alias{memkin}
-\title{Estimation of parameter distributions from mmkin row objects}
-\usage{
-memkin(object, random_spec = "auto", ...)
-}
-\arguments{
-\item{object}{An mmkin row object containing several fits of the same model to different datasets}
-
-\item{random_spec}{Either "auto" or a specification of random effects for \code{\link{nlme}}
-given as a character vector}
-
-\item{...}{Additional arguments passed to \code{\link{nlme}}}
-}
-\value{
-An nlme object
-}
-\description{
-This function sets up and attempts to fit a mixed effects model to
-an mmkin row object which is essentially a list of mkinfit objects
-that have been obtained by fitting the same model to a list of
-datasets.
-}
-\examples{
-sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-m_SFO <- mkinmod(parent = mkinsub("SFO"))
-d_SFO_1 <- mkinpredict(m_SFO,
- c(k_parent_sink = 0.1),
- c(parent = 98), sampling_times)
-d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time")
-d_SFO_2 <- mkinpredict(m_SFO,
- c(k_parent_sink = 0.05),
- c(parent = 102), sampling_times)
-d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time")
-d_SFO_3 <- mkinpredict(m_SFO,
- c(k_parent_sink = 0.02),
- c(parent = 103), sampling_times)
-d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")
-
-d1 <- add_err(d_SFO_1, function(value) 3, n = 1)
-d2 <- add_err(d_SFO_2, function(value) 2, n = 1)
-d3 <- add_err(d_SFO_3, function(value) 4, n = 1)
-ds <- c(d1 = d1, d2 = d2, d3 = d3)
-
-f <- mmkin("SFO", ds)
-x <- memkin(f)
-summary(x)
-
-ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
- function(x) x$data[c("name", "time", "value")])
-m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
- A1 = mkinsub("SFO"), use_of_ff = "min")
-m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"),
- A1 = mkinsub("SFO"), use_of_ff = "max")
-m_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
- A1 = mkinsub("SFO"))
-m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
- A1 = mkinsub("SFO"))
-m_sforb_sfo <- mkinmod(parent = mkinsub("SFORB", "A1"),
- A1 = mkinsub("SFO"))
-
-f_2 <- mmkin(list("SFO-SFO" = m_sfo_sfo,
- "SFO-SFO-ff" = m_sfo_sfo_ff,
- "FOMC-SFO" = m_fomc_sfo,
- "DFOP-SFO" = m_dfop_sfo,
- "SFORB-SFO" = m_sforb_sfo),
- ds_2)
-
-f_nlme_sfo_sfo <- memkin(f_2[1, ])
-f_nlme_sfo_sfo_2 <- memkin(f_2[1, ], "pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1)") # explicit
-f_nlme_sfo_sfo_3 <- memkin(f_2[1, ], "pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 ~ 1)") # reduced
-f_nlme_sfo_sfo_4 <- memkin(f_2[1, ], "pdDiag(parent_0 + log_k_parent_sink ~ 1)") # further reduced
-\dontrun{
- f_nlme_sfo_sfo_ff <- memkin(f_2[2, ]) # does not converge with maxIter = 50
-}
-f_nlme_fomc_sfo <- memkin(f_2[3, ])
-\dontrun{
- f_nlme_dfop_sfo <- memkin(f_2[4, ]) # apparently underdetermined
- f_nlme_sforb_sfo <- memkin(f_2[5, ]) # also does not converge
-}
-anova(f_nlme_fomc_sfo, f_nlme_sfo_sfo, f_nlme_sfo_sfo_4)
-}
diff --git a/man/mkinsub.Rd b/man/mkinsub.Rd
index 6522a37e..81615a00 100644
--- a/man/mkinsub.Rd
+++ b/man/mkinsub.Rd
@@ -40,11 +40,12 @@ SFO_SFO.2 <- mkinmod(
parent = mkinsub("SFO", "m1"),
m1 = mkinsub("SFO"))
-# Now supplying full names
-SFO_SFO.2 <- mkinmod(
- parent = mkinsub("SFO", "m1", full_name = "Test compound"),
- m1 = mkinsub("SFO", full_name = "Metabolite M1"))
-
+\dontrun{
+ # Now supplying full names
+ SFO_SFO.2 <- mkinmod(
+ parent = mkinsub("SFO", "m1", full_name = "Test compound"),
+ m1 = mkinsub("SFO", full_name = "Metabolite M1"))
+ }
}
\author{
Johannes Ranke
diff --git a/man/nlme.Rd b/man/nlme.Rd
new file mode 100644
index 00000000..e74ba16d
--- /dev/null
+++ b/man/nlme.Rd
@@ -0,0 +1,134 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/nlme.R
+\name{mean_degparms}
+\alias{mean_degparms}
+\alias{nlme_data}
+\alias{nlme_function}
+\title{Estimation of parameter distributions from mmkin row objects}
+\usage{
+mean_degparms(object)
+
+nlme_data(object)
+
+nlme_function(object)
+}
+\arguments{
+\item{object}{An mmkin row object containing several fits of the same model to different datasets}
+}
+\value{
+A named vector containing mean values of the fitted degradation model parameters
+
+A groupedData data object
+
+A function that can be used with nlme
+}
+\description{
+This function sets up and attempts to fit a mixed effects model to
+an mmkin row object. An mmkin row object is essentially a list of mkinfit
+objects that have been obtained by fitting the same model to a list of
+datasets.
+}
+\examples{
+sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+m_SFO <- mkinmod(parent = mkinsub("SFO"))
+d_SFO_1 <- mkinpredict(m_SFO,
+ c(k_parent_sink = 0.1),
+ c(parent = 98), sampling_times)
+d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time")
+d_SFO_2 <- mkinpredict(m_SFO,
+ c(k_parent_sink = 0.05),
+ c(parent = 102), sampling_times)
+d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time")
+d_SFO_3 <- mkinpredict(m_SFO,
+ c(k_parent_sink = 0.02),
+ c(parent = 103), sampling_times)
+d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")
+
+d1 <- add_err(d_SFO_1, function(value) 3, n = 1)
+d2 <- add_err(d_SFO_2, function(value) 2, n = 1)
+d3 <- add_err(d_SFO_3, function(value) 4, n = 1)
+ds <- c(d1 = d1, d2 = d2, d3 = d3)
+
+f <- mmkin("SFO", ds, cores = 1, quiet = TRUE)
+mean_dp <- mean_degparms(f)
+grouped_data <- nlme_data(f)
+nlme_f <- nlme_function(f)
+
+library(nlme)
+m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),
+ data = grouped_data,
+ fixed = parent_0 + log_k_parent_sink ~ 1,
+ random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
+ start = mean_dp)
+summary(m_nlme)
+
+\dontrun{
+ Test on some real data
+ ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
+ function(x) x$data[c("name", "time", "value")])
+ m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
+ A1 = mkinsub("SFO"), use_of_ff = "min")
+ m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"),
+ A1 = mkinsub("SFO"), use_of_ff = "max")
+ m_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
+ A1 = mkinsub("SFO"))
+ m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
+ A1 = mkinsub("SFO"))
+ m_sforb_sfo <- mkinmod(parent = mkinsub("SFORB", "A1"),
+ A1 = mkinsub("SFO"))
+
+ f_2 <- mmkin(list("SFO-SFO" = m_sfo_sfo,
+ "SFO-SFO-ff" = m_sfo_sfo_ff,
+ "FOMC-SFO" = m_fomc_sfo,
+ "DFOP-SFO" = m_dfop_sfo,
+ "SFORB-SFO" = m_sforb_sfo),
+ ds_2)
+
+ grouped_data_2 <- nlme_data(f_2["SFO-SFO", ])
+
+ mean_dp_sfo_sfo <- mean_degparms(f_2["SFO-SFO", ])
+ mean_dp_sfo_sfo_ff <- mean_degparms(f_2["SFO-SFO-ff", ])
+ mean_dp_fomc_sfo <- mean_degparms(f_2["FOMC-SFO", ])
+ mean_dp_dfop_sfo <- mean_degparms(f_2["DFOP-SFO", ])
+ mean_dp_sforb_sfo <- mean_degparms(f_2["SFORB-SFO", ])
+
+ nlme_f_sfo_sfo <- nlme_function(f_2["SFO-SFO", ])
+ nlme_f_sfo_sfo_ff <- nlme_function(f_2["SFO-SFO-ff", ])
+ nlme_f_fomc_sfo <- nlme_function(f_2["FOMC-SFO", ])
+
+ # Allowing for correlations between random effects leads to non-convergence
+ f_nlme_sfo_sfo <- nlme(value ~ nlme_f_sfo_sfo(name, time,
+ parent_0, log_k_parent_sink, log_k_parent_A1, log_k_A1_sink),
+ data = grouped_data_2,
+ fixed = parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1,
+ random = pdDiag(parent_0 + log_k_parent_sink + log_k_parent_A1 + log_k_A1_sink ~ 1),
+ start = mean_dp_sfo_sfo)
+
+ # The same model fitted with transformed formation fractions does not converge
+ f_nlme_sfo_sfo_ff <- nlme(value ~ nlme_f_sfo_sfo_ff(name, time,
+ parent_0, log_k_parent, log_k_A1, f_parent_ilr_1),
+ data = grouped_data_2,
+ fixed = parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~ 1,
+ random = pdDiag(parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~ 1),
+ start = mean_dp_sfo_sfo_ff)
+
+ # It does converge with this version of reduced random effects
+ f_nlme_sfo_sfo_ff <- nlme(value ~ nlme_f_sfo_sfo_ff(name, time,
+ parent_0, log_k_parent, log_k_A1, f_parent_ilr_1),
+ data = grouped_data_2,
+ fixed = parent_0 + log_k_parent + log_k_A1 + f_parent_ilr_1 ~ 1,
+ random = pdDiag(parent_0 + log_k_parent ~ 1),
+ start = mean_dp_sfo_sfo_ff)
+
+ f_nlme_fomc_sfo <- nlme(value ~ nlme_f_fomc_sfo(name, time,
+ parent_0, log_alpha, log_beta, log_k_A1, f_parent_ilr_1),
+ data = grouped_data_2,
+ fixed = parent_0 + log_alpha + log_beta + log_k_A1 + f_parent_ilr_1 ~ 1,
+ random = pdDiag(parent_0 + log_alpha + log_beta + log_k_A1 + f_parent_ilr_1 ~ 1),
+ start = mean_dp_fomc_sfo)
+
+ # DFOP-SFO and SFORB-SFO did not converge with full random effects
+
+ anova(f_nlme_fomc_sfo, f_nlme_sfo_sfo)
+}
+}
diff --git a/vignettes/web_only/FOCUS_Z.R b/vignettes/web_only/FOCUS_Z.R
deleted file mode 100644
index 0c19794e..00000000
--- a/vignettes/web_only/FOCUS_Z.R
+++ /dev/null
@@ -1,115 +0,0 @@
-## ---- include = FALSE---------------------------------------------------------
-require(knitr)
-options(digits = 5)
-opts_chunk$set(engine='R', tidy = FALSE)
-
-## ---- echo = TRUE, fig = TRUE, fig.width = 8, fig.height = 7------------------
-library(mkin, quietly = TRUE)
-LOD = 0.5
-FOCUS_2006_Z = data.frame(
- t = c(0, 0.04, 0.125, 0.29, 0.54, 1, 2, 3, 4, 7, 10, 14, 21,
- 42, 61, 96, 124),
- Z0 = c(100, 81.7, 70.4, 51.1, 41.2, 6.6, 4.6, 3.9, 4.6, 4.3, 6.8,
- 2.9, 3.5, 5.3, 4.4, 1.2, 0.7),
- Z1 = c(0, 18.3, 29.6, 46.3, 55.1, 65.7, 39.1, 36, 15.3, 5.6, 1.1,
- 1.6, 0.6, 0.5 * LOD, NA, NA, NA),
- Z2 = c(0, NA, 0.5 * LOD, 2.6, 3.8, 15.3, 37.2, 31.7, 35.6, 14.5,
- 0.8, 2.1, 1.9, 0.5 * LOD, NA, NA, NA),
- Z3 = c(0, NA, NA, NA, NA, 0.5 * LOD, 9.2, 13.1, 22.3, 28.4, 32.5,
- 25.2, 17.2, 4.8, 4.5, 2.8, 4.4))
-
-FOCUS_2006_Z_mkin <- mkin_wide_to_long(FOCUS_2006_Z)
-
-## ----FOCUS_2006_Z_fits_1, echo=TRUE, fig.height=6-----------------------------
-Z.2a <- mkinmod(Z0 = mkinsub("SFO", "Z1"),
- Z1 = mkinsub("SFO"))
-m.Z.2a <- mkinfit(Z.2a, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.2a)
-summary(m.Z.2a, data = FALSE)$bpar
-
-## ----FOCUS_2006_Z_fits_2, echo=TRUE, fig.height=6-----------------------------
-Z.2a.ff <- mkinmod(Z0 = mkinsub("SFO", "Z1"),
- Z1 = mkinsub("SFO"),
- use_of_ff = "max")
-
-m.Z.2a.ff <- mkinfit(Z.2a.ff, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.2a.ff)
-summary(m.Z.2a.ff, data = FALSE)$bpar
-
-## ----FOCUS_2006_Z_fits_3, echo=TRUE, fig.height=6-----------------------------
-Z.3 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE),
- Z1 = mkinsub("SFO"), use_of_ff = "max")
-m.Z.3 <- mkinfit(Z.3, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.3)
-summary(m.Z.3, data = FALSE)$bpar
-
-## ----FOCUS_2006_Z_fits_5, echo=TRUE, fig.height=7-----------------------------
-Z.5 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE),
- Z1 = mkinsub("SFO", "Z2", sink = FALSE),
- Z2 = mkinsub("SFO"), use_of_ff = "max")
-m.Z.5 <- mkinfit(Z.5, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.5)
-
-## ----FOCUS_2006_Z_fits_6, echo=TRUE, fig.height=8-----------------------------
-Z.FOCUS <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE),
- Z1 = mkinsub("SFO", "Z2", sink = FALSE),
- Z2 = mkinsub("SFO", "Z3"),
- Z3 = mkinsub("SFO"),
- use_of_ff = "max")
-m.Z.FOCUS <- mkinfit(Z.FOCUS, FOCUS_2006_Z_mkin,
- parms.ini = m.Z.5$bparms.ode,
- quiet = TRUE)
-plot_sep(m.Z.FOCUS)
-summary(m.Z.FOCUS, data = FALSE)$bpar
-endpoints(m.Z.FOCUS)
-
-## ----FOCUS_2006_Z_fits_7, echo=TRUE, fig.height=8-----------------------------
-Z.mkin.1 <- mkinmod(Z0 = mkinsub("SFO", "Z1", sink = FALSE),
- Z1 = mkinsub("SFO", "Z2", sink = FALSE),
- Z2 = mkinsub("SFO", "Z3"),
- Z3 = mkinsub("SFORB"))
-m.Z.mkin.1 <- mkinfit(Z.mkin.1, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.mkin.1)
-summary(m.Z.mkin.1, data = FALSE)$cov.unscaled
-
-## ----FOCUS_2006_Z_fits_9, echo=TRUE, fig.height=8-----------------------------
-Z.mkin.3 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE),
- Z1 = mkinsub("SFO", "Z2", sink = FALSE),
- Z2 = mkinsub("SFO"))
-m.Z.mkin.3 <- mkinfit(Z.mkin.3, FOCUS_2006_Z_mkin, quiet = TRUE)
-plot_sep(m.Z.mkin.3)
-
-## ----FOCUS_2006_Z_fits_10, echo=TRUE, fig.height=8----------------------------
-Z.mkin.4 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE),
- Z1 = mkinsub("SFO", "Z2", sink = FALSE),
- Z2 = mkinsub("SFO", "Z3"),
- Z3 = mkinsub("SFO"))
-m.Z.mkin.4 <- mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin,
- parms.ini = m.Z.mkin.3$bparms.ode,
- quiet = TRUE)
-plot_sep(m.Z.mkin.4)
-
-## ----FOCUS_2006_Z_fits_11, echo=TRUE, fig.height=8----------------------------
-Z.mkin.5 <- mkinmod(Z0 = mkinsub("SFORB", "Z1", sink = FALSE),
- Z1 = mkinsub("SFO", "Z2", sink = FALSE),
- Z2 = mkinsub("SFO", "Z3"),
- Z3 = mkinsub("SFORB"))
-m.Z.mkin.5 <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin,
- parms.ini = m.Z.mkin.4$bparms.ode[1:4],
- quiet = TRUE)
-plot_sep(m.Z.mkin.5)
-
-## ----FOCUS_2006_Z_fits_11a, echo=TRUE-----------------------------------------
-m.Z.mkin.5a <- mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin,
- parms.ini = c(m.Z.mkin.5$bparms.ode[1:7],
- k_Z3_bound_free = 0),
- fixed_parms = "k_Z3_bound_free",
- quiet = TRUE)
-plot_sep(m.Z.mkin.5a)
-
-## ----FOCUS_2006_Z_fits_11b, echo=TRUE-----------------------------------------
-mkinparplot(m.Z.mkin.5a)
-
-## ----FOCUS_2006_Z_fits_11b_endpoints, echo=TRUE-------------------------------
-endpoints(m.Z.mkin.5a)
-
diff --git a/vignettes/web_only/compiled_models.R b/vignettes/web_only/compiled_models.R
deleted file mode 100644
index 691037ec..00000000
--- a/vignettes/web_only/compiled_models.R
+++ /dev/null
@@ -1,61 +0,0 @@
-## ---- include = FALSE---------------------------------------------------------
-library(knitr)
-opts_chunk$set(tidy = FALSE, cache = FALSE)
-
-## ----check_gcc, eval = FALSE--------------------------------------------------
-# pkgbuild::has_compiler()
-
-## ----Rprofile, eval = FALSE---------------------------------------------------
-# Sys.setenv(PATH = paste("C:/Rtools/bin", Sys.getenv("PATH"), sep=";"))
-
-## ----HOME, eval = FALSE-------------------------------------------------------
-# Sys.getenv("HOME")
-
-## ----create_SFO_SFO-----------------------------------------------------------
-library("mkin", quietly = TRUE)
-SFO_SFO <- mkinmod(
- parent = mkinsub("SFO", "m1"),
- m1 = mkinsub("SFO"))
-
-## ----benchmark_SFO_SFO, fig.height = 3, message = FALSE, warning = FALSE------
-if (require(rbenchmark)) {
- b.1 <- benchmark(
- "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_2006_D,
- solution_type = "deSolve",
- use_compiled = FALSE, quiet = TRUE),
- "Eigenvalue based" = mkinfit(SFO_SFO, FOCUS_2006_D,
- solution_type = "eigen", quiet = TRUE),
- "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_2006_D,
- solution_type = "deSolve", quiet = TRUE),
- replications = 3)
- print(b.1)
- factor_SFO_SFO <- round(b.1["1", "relative"])
-} else {
- factor_SFO_SFO <- NA
- print("R package rbenchmark is not available")
-}
-
-## ----benchmark_FOMC_SFO, fig.height = 3, warning = FALSE----------------------
-if (require(rbenchmark)) {
- FOMC_SFO <- mkinmod(
- parent = mkinsub("FOMC", "m1"),
- m1 = mkinsub( "SFO"))
-
- b.2 <- benchmark(
- "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D,
- use_compiled = FALSE, quiet = TRUE),
- "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE),
- replications = 3)
- print(b.2)
- factor_FOMC_SFO <- round(b.2["1", "relative"])
-} else {
- factor_FOMC_SFO <- NA
- print("R package benchmark is not available")
-}
-
-## ----sessionInfo, echo = FALSE------------------------------------------------
-cat(utils::capture.output(utils::sessionInfo())[1:3], sep = "\n")
-if(!inherits(try(cpuinfo <- readLines("/proc/cpuinfo")), "try-error")) {
- cat(gsub("model name\t: ", "CPU model: ", cpuinfo[grep("model name", cpuinfo)[1]]))
-}
-

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