From a9427a09abdf7ce9aaeae7c7190f90c8f2e5ef52 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 15 Feb 2021 14:08:13 +0100 Subject: Improve README, introductory vignette and some other docs Also bump version to 1.0.3. --- docs/reference/nlme.mmkin.html | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) (limited to 'docs/reference/nlme.mmkin.html') diff --git a/docs/reference/nlme.mmkin.html b/docs/reference/nlme.mmkin.html index 2e4f6337..189e34ef 100644 --- a/docs/reference/nlme.mmkin.html +++ b/docs/reference/nlme.mmkin.html @@ -74,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." /> mkin - 1.0.0 + 1.0.3 @@ -157,7 +157,7 @@ have been obtained by fitting the same model to a list of datasets.

data = "auto", fixed = lapply(as.list(names(mean_degparms(model))), function(el) eval(parse(text = paste(el, 1, sep = "~")))), - random = pdDiag(fixed), + random = pdDiag(fixed), groups, start = mean_degparms(model, random = TRUE), correlation = NULL, @@ -290,7 +290,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as anova(f_nlme_sfo, f_nlme_dfop)
#> Model df AIC BIC logLik Test L.Ratio p-value #> f_nlme_sfo 1 5 625.0539 637.5529 -307.5269 -#> f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9268 <.0001
print(f_nlme_dfop) +#> f_nlme_dfop 2 9 495.1270 517.6253 -238.5635 1 vs 2 137.9269 <.0001
print(f_nlme_dfop)
#> Kinetic nonlinear mixed-effects model fit by maximum likelihood #> #> Structural model: @@ -318,7 +318,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
endpoints(f_nlme_dfop)
#> $distimes #> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 10.79857 100.7937 30.34192 4.193937 43.85442 +#> parent 10.79857 100.7937 30.34193 4.193938 43.85443 #>
ds_2 <- lapply(experimental_data_for_UBA_2019[6:10], function(x) x$data[c("name", "time", "value")]) @@ -350,8 +350,8 @@ methods that will automatically work on 'nlme.mmkin' objects, such as
anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo)
#> Model df AIC BIC logLik Test L.Ratio p-value -#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 -#> f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3274 <.0001
+#> f_nlme_dfop_sfo 1 13 843.8548 884.6201 -408.9274 +#> f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3273 <.0001
endpoints(f_nlme_sfo_sfo)
#> $ff #> parent_sink parent_A1 A1_sink @@ -364,12 +364,12 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #>
endpoints(f_nlme_dfop_sfo)
#> $ff #> parent_A1 parent_sink -#> 0.2768574 0.7231426 +#> 0.2768575 0.7231425 #> #> $distimes #> DT50 DT90 DT50back DT50_k1 DT50_k2 -#> parent 11.07091 104.6320 31.49738 4.462384 46.20825 -#> A1 162.30523 539.1663 NA NA NA +#> parent 11.07091 104.6320 31.49737 4.462384 46.20825 +#> A1 162.30492 539.1653 NA NA NA #>
if (length(findFunction("varConstProp")) > 0) { # tc error model for nlme available # Attempts to fit metabolite kinetics with the tc error model are possible, @@ -396,7 +396,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Fixed effects: #> list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) #> parent_0 log_k1 log_k2 g_qlogis -#> 94.04775 -1.82340 -4.16715 0.05685 +#> 94.04774 -1.82340 -4.16716 0.05686 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k1 ~ 1, log_k2 ~ 1, g_qlogis ~ 1) @@ -410,7 +410,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Formula: ~fitted(.) #> Parameter estimates: #> const prop -#> 2.23224114 0.01262341
+#> 2.23223147 0.01262395
f_2_obs <- update(f_2, error_model = "obs") f_nlme_sfo_sfo_obs <- nlme(f_2_obs["SFO-SFO", ]) print(f_nlme_sfo_sfo_obs) @@ -442,7 +442,7 @@ methods that will automatically work on 'nlme.mmkin' objects, such as #> Formula: ~1 | name #> Parameter estimates: #> parent A1 -#> 1.0000000 0.2050003
f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ], +#> 1.0000000 0.2049995
f_nlme_dfop_sfo_obs <- nlme(f_2_obs["DFOP-SFO", ], control = list(pnlsMaxIter = 120, tolerance = 5e-4)) f_2_tc <- update(f_2, error_model = "tc") @@ -452,8 +452,8 @@ methods that will automatically work on 'nlme.mmkin' objects, such as anova(f_nlme_dfop_sfo, f_nlme_dfop_sfo_obs)
#> Model df AIC BIC logLik Test L.Ratio -#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 -#> f_nlme_dfop_sfo_obs 2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32089 +#> f_nlme_dfop_sfo 1 13 843.8548 884.6201 -408.9274 +#> f_nlme_dfop_sfo_obs 2 14 817.5338 861.4350 -394.7669 1 vs 2 28.32093 #> p-value #> f_nlme_dfop_sfo #> f_nlme_dfop_sfo_obs <.0001
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