diff options
Diffstat (limited to 'R')
| -rw-r--r-- | R/nlme.R | 1 | ||||
| -rw-r--r-- | R/nlme.mmkin.R | 47 | ||||
| -rw-r--r-- | R/summary.nlme.mmkin.R | 1 |
3 files changed, 29 insertions, 20 deletions
@@ -54,7 +54,6 @@ #' # The procedure is greatly simplified by the nlme.mmkin function #' f_nlme <- nlme(f) #' plot(f_nlme) -#' #' @return A function that can be used with nlme #' @export nlme_function <- function(object) { diff --git a/R/nlme.mmkin.R b/R/nlme.mmkin.R index a9e1694f..b434bbf4 100644 --- a/R/nlme.mmkin.R +++ b/R/nlme.mmkin.R @@ -56,15 +56,15 @@ get_deg_func <- function() { #' f <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1) #' library(nlme) #' f_nlme_sfo <- nlme(f["SFO", ]) -#' f_nlme_dfop <- nlme(f["DFOP", ]) -#' AIC(f_nlme_sfo, f_nlme_dfop) -#' print(f_nlme_dfop) -#' plot(f_nlme_dfop) -#' endpoints(f_nlme_dfop) #' #' \dontrun{ -#' f_nlme_2 <- nlme(f["SFO", ], start = c(parent_0 = 100, log_k_parent = 0.1)) -#' update(f_nlme_2, random = parent_0 ~ 1) +#' +#' f_nlme_dfop <- nlme(f["DFOP", ]) +#' anova(f_nlme_sfo, f_nlme_dfop) +#' print(f_nlme_dfop) +#' plot(f_nlme_dfop) +#' endpoints(f_nlme_dfop) +#' #' 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"), @@ -82,14 +82,15 @@ get_deg_func <- function() { #' f_nlme_sfo_sfo <- nlme(f_2["SFO-SFO", ]) #' plot(f_nlme_sfo_sfo) #' -#' # With formation fractions -#' f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ]) -#' plot(f_nlme_sfo_sfo_ff) +#' # With formation fractions this does not coverge with defaults +#' # f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ]) +#' #plot(f_nlme_sfo_sfo_ff) #' -#' # For the following fit we need to increase pnlsMaxIter and the tolerance -#' # to get convergence -#' f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ], -#' control = list(pnlsMaxIter = 120, tolerance = 5e-4), verbose = TRUE) +#' # With the log-Cholesky parameterization, this converges in 11 +#' # iterations and around 100 seconds, but without tweaking control +#' # parameters (with pdDiag, increasing the tolerance and pnlsMaxIter was +#' # necessary) +#' f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ]) #' #' plot(f_nlme_dfop_sfo) #' @@ -114,10 +115,18 @@ get_deg_func <- function() { #' ds_2, quiet = TRUE, error_model = "obs") #' f_nlme_sfo_sfo_obs <- nlme(f_2_obs["SFO-SFO", ]) #' print(f_nlme_sfo_sfo_obs) -#' # The same with DFOP-SFO does not converge, apparently the variances of -#' # parent and A1 are too similar in this case, so that the model is -#' # overparameterised -#' #f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ], control = list(maxIter = 100)) +#' f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ]) +#' +#' f_2_tc <- mmkin(list("SFO-SFO" = m_sfo_sfo, +#' "DFOP-SFO" = m_dfop_sfo), +#' ds_2, quiet = TRUE, error_model = "tc") +#' # f_nlme_sfo_sfo_tc <- nlme(f_2_tc["SFO-SFO", ]) # stops with error message +#' f_nlme_dfop_sfo_tc <- nlme(f_2_tc["DFOP-SFO", ]) +#' # We get warnings about false convergence in the LME step in several iterations +#' # but as the last such warning occurs in iteration 25 and we have 28 iterations +#' # we can ignore these +#' 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, @@ -160,7 +169,7 @@ nlme.mmkin <- function(model, data = sys.frame(sys.parent()), eval(parse(text = paste(el, 1, sep = "~")))) if (missing(random)) { - thisCall[["random"]] <- pdDiag(thisCall[["fixed"]]) + thisCall[["random"]] <- pdLogChol(thisCall[["fixed"]]) } error_model <- model[[1]]$err_mod diff --git a/R/summary.nlme.mmkin.R b/R/summary.nlme.mmkin.R index 42326b39..29f1207b 100644 --- a/R/summary.nlme.mmkin.R +++ b/R/summary.nlme.mmkin.R @@ -55,6 +55,7 @@ #' ds_sfo_mean <- lapply(k_in, pred_sfo) #' names(ds_sfo_mean) <- paste("ds", 1:5) #' +#' set.seed(12345) #' ds_sfo_syn <- lapply(ds_sfo_mean, function(ds) { #' add_err(ds, #' sdfunc = function(value) sqrt(1^2 + value^2 * 0.07^2), |