From 42171ba55222383a0d47e5aacd46a972819e7812 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 15 Apr 2020 18:13:04 +0200 Subject: Include random effects in starting parameters - mean_degparms() now optionally returns starting values for fixed and random effects, which makes it possible to obtain acceptable fits also in more difficult cases (with more parameters) - Fix the anova method, as it is currently not enough to inherit from lme: https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17761 - Show fit information, and per default also errmin information in plot.nlme.mmkin() - Examples for nlme.mmkin: Decrease tolerance and increase the number of iterations in the PNLS step in order to be able to fit FOMC-SFO and DFOP-SFO --- man/nlme.Rd | 78 +++++++++---------------------------------------------------- 1 file changed, 11 insertions(+), 67 deletions(-) (limited to 'man/nlme.Rd') diff --git a/man/nlme.Rd b/man/nlme.Rd index f31c7a4f..4a668ac0 100644 --- a/man/nlme.Rd +++ b/man/nlme.Rd @@ -8,17 +8,22 @@ \usage{ nlme_function(object) -mean_degparms(object) +mean_degparms(object, random = FALSE) nlme_data(object) } \arguments{ \item{object}{An mmkin row object containing several fits of the same model to different datasets} + +\item{random}{Should a list with fixed and random effects be returned?} } \value{ A function that can be used with nlme -A named vector containing mean values of the fitted degradation model parameters +If random is FALSE (default), a named vector containing mean values + of the fitted degradation model parameters. If random is TRUE, a list with + fixed and random effects, in the format required by the start argument of + nlme for the case of a single grouping variable ds? A \code{\link{groupedData}} object } @@ -67,71 +72,10 @@ m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink), start = mean_dp) summary(m_nlme) plot(augPred(m_nlme, level = 0:1), layout = c(3, 1)) +# augPred does not seem to work on fits with more than one state +# variable -\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", ]) - assign("nlme_f_sfo_sfo", nlme_f_sfo_sfo, globalenv()) - assign("nlme_f_sfo_sfo_ff", nlme_f_sfo_sfo_ff, globalenv()) - assign("nlme_f_fomc_sfo", nlme_f_fomc_sfo, globalenv()) - - # Allowing for correlations between random effects (not shown) - # 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) - # augPred does not see to work on this object, so no plot is shown - - # 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) - - 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) } +\seealso{ +\code{\link{nlme.mmkin}} } -- cgit v1.2.1