diff options
Diffstat (limited to 'R')
| -rw-r--r-- | R/saemix.R | 9 | ||||
| -rw-r--r-- | R/summary.saem.mmkin.R | 39 |
2 files changed, 31 insertions, 17 deletions
@@ -44,6 +44,10 @@ #' # functions from saemix #' library(saemix) #' compare.saemix(list(f_saem_sfo$so, f_saem_fomc$so, f_saem_dfop$so)) +#' plot(f_saem_fomc$so, plot.type = "convergence") +#' plot(f_saem_fomc$so, plot.type = "individual.fit") +#' plot(f_saem_fomc$so, plot.type = "npde") +#' plot(f_saem_fomc$so, plot.type = "vpc") #' #' f_mmkin_parent_tc <- update(f_mmkin_parent, error_model = "tc") #' f_saem_fomc_tc <- saem(f_mmkin_parent_tc["FOMC", ]) @@ -64,11 +68,12 @@ #' # analytical solutions written for saemix. When using the analytical #' # solutions written for mkin this took around four minutes #' f_saem_sfo_sfo <- saem(f_mmkin["SFO-SFO", ]) -#' f_saem_dfop_sfo <- saem(f_mmkin["SFO-SFO", ]) +#' f_saem_dfop_sfo <- saem(f_mmkin["DFOP-SFO", ]) +#' summary(f_saem_dfop_sfo, data = FALSE) #' #' # Using a single core, the following takes about 6 minutes, using 10 cores #' # it is slower instead of faster -#' f_saem_fomc <- saem(f_mmkin["FOMC-SFO", ], cores = 1) +#' #f_saem_fomc <- saem(f_mmkin["FOMC-SFO", ], cores = 1) #' } #' @export saem <- function(object, control, ...) UseMethod("saem") diff --git a/R/summary.saem.mmkin.R b/R/summary.saem.mmkin.R index 4c9776a6..a8917144 100644 --- a/R/summary.saem.mmkin.R +++ b/R/summary.saem.mmkin.R @@ -34,25 +34,34 @@ #' @author Johannes Ranke for the mkin specific parts #' saemix authors for the parts inherited from saemix. #' @examples -#' # Generate five datasets following SFO kinetics +#' # Generate five datasets following DFOP-SFO kinetics #' sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) -#' dt50_sfo_in_pop <- 50 -#' k_in_pop <- log(2) / dt50_sfo_in_pop +#' dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "m1"), +#' m1 = mkinsub("SFO"), quiet = TRUE) #' set.seed(1234) -#' k_in <- rlnorm(5, log(k_in_pop), 0.5) -#' SFO <- mkinmod(parent = mkinsub("SFO")) +#' k1_in <- rlnorm(5, log(0.1), 0.3) +#' k2_in <- rlnorm(5, log(0.02), 0.3) +#' g_in <- plogis(rnorm(5, qlogis(0.5), 0.3)) +#' f_parent_to_m1_in <- plogis(rnorm(5, qlogis(0.3), 0.3)) +#' k_m1_in <- rlnorm(5, log(0.02), 0.3) #' -#' pred_sfo <- function(k) { -#' mkinpredict(SFO, -#' c(k_parent = k), -#' c(parent = 100), +#' pred_dfop_sfo <- function(k1, k2, g, f_parent_to_m1, k_m1) { +#' mkinpredict(dfop_sfo, +#' c(k1 = k1, k2 = k2, g = g, f_parent_to_m1 = f_parent_to_m1, k_m1 = k_m1), +#' c(parent = 100, m1 = 0), #' sampling_times) #' } #' -#' ds_sfo_mean <- lapply(k_in, pred_sfo) -#' names(ds_sfo_mean) <- paste("ds", 1:5) +#' ds_mean_dfop_sfo <- lapply(1:5, function(i) { +#' mkinpredict(dfop_sfo, +#' c(k1 = k1_in[i], k2 = k2_in[i], g = g_in[i], +#' f_parent_to_m1 = f_parent_to_m1_in[i], k_m1 = k_m1_in[i]), +#' c(parent = 100, m1 = 0), +#' sampling_times) +#' }) +#' names(ds_mean_dfop_sfo) <- paste("ds", 1:5) #' -#' ds_sfo_syn <- lapply(ds_sfo_mean, function(ds) { +#' ds_syn_dfop_sfo <- lapply(ds_mean_dfop_sfo, function(ds) { #' add_err(ds, #' sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2), #' n = 1)[[1]] @@ -60,9 +69,9 @@ #' #' \dontrun{ #' # Evaluate using mmkin and saem -#' f_mmkin <- mmkin("SFO", ds_sfo_syn, quiet = TRUE, error_model = "tc", cores = 1) -#' f_saem <- saem(f_mmkin) -#' summary(f_saem, data = TRUE) +#' f_mmkin_dfop_sfo <- mmkin(list(dfop_sfo), ds_syn_dfop_sfo, quiet = TRUE, error_model = "tc", cores = 5) +#' f_saem_dfop_sfo <- saem(f_mmkin_dfop_sfo) +#' summary(f_saem_dfop_sfo, data = TRUE) #' } #' #' @export |