From c73b2f30ec836c949885784ab576e814eb8070a9 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 9 Mar 2021 17:35:47 +0100 Subject: Some improvements for borderline cases - fit_with_errors for saem() - test_log_parms for mean_degparms() and saem() --- docs/dev/reference/nlme.html | 33 ++++++++++++++++++++++----------- 1 file changed, 22 insertions(+), 11 deletions(-) (limited to 'docs/dev/reference/nlme.html') diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html index b850eb3d..78d132e9 100644 --- a/docs/dev/reference/nlme.html +++ b/docs/dev/reference/nlme.html @@ -75,7 +75,7 @@ datasets. They are used internally by the nlme.mmkin() method." /> mkin - 1.0.3.9000 + 1.0.4.9000 @@ -155,7 +155,7 @@ datasets. They are used internally by the nlme.m 
nlme_function(object)
 
-mean_degparms(object, random = FALSE)
+mean_degparms(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6)
 
 nlme_data(object)
@@ -170,6 +170,17 @@ datasets. They are used internally by the nlme.m random

Should a list with fixed and random effects be returned?

+ + test_log_parms +

If TRUE, log parameters are only considered in +the mean calculations if their untransformed counterparts (most likely +rate constants) pass the t-test for significant difference from zero.

+ + + conf.level +

Possibility to adjust the required confidence level +for parameter that are tested if requested by 'test_log_parms'.

+

Value

@@ -211,7 +222,7 @@ nlme for the case of a single grouping variable ds.

nlme_f <- nlme_function(f) # These assignments are necessary for these objects to be # visible to nlme and augPred when evaluation is done by -# pkgdown to generated the html docs. +# pkgdown to generate the html docs. assign("nlme_f", nlme_f, globalenv()) assign("grouped_data", grouped_data, globalenv()) @@ -226,28 +237,28 @@ nlme for the case of a single grouping variable ds.

#> Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) #> Data: grouped_data #> AIC BIC logLik -#> 300.6824 310.2426 -145.3412 +#> 298.2781 307.7372 -144.1391 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> Level: ds #> Structure: Diagonal #> parent_0 log_k_parent_sink Residual -#> StdDev: 1.697361 0.6801209 3.666073 +#> StdDev: 0.937473 0.7098105 3.83543 #> #> Fixed effects: parent_0 + log_k_parent_sink ~ 1 #> Value Std.Error DF t-value p-value -#> parent_0 100.99378 1.3890416 46 72.70753 0 -#> log_k_parent_sink -3.07521 0.4018589 46 -7.65246 0 +#> parent_0 101.76838 1.1445443 45 88.91607 0 +#> log_k_parent_sink -3.05444 0.4195622 45 -7.28008 0 #> Correlation: #> prnt_0 -#> log_k_parent_sink 0.027 +#> log_k_parent_sink 0.034 #> #> Standardized Within-Group Residuals: -#> Min Q1 Med Q3 Max -#> -1.9942823 -0.5622565 0.1791579 0.7165038 2.0704781 +#> Min Q1 Med Q3 Max +#> -2.61693595 -0.21853231 0.05740682 0.57209372 3.04598764 #> -#> Number of Observations: 50 +#> Number of Observations: 49 #> Number of Groups: 3
plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
# augPred does not work on fits with more than one state # variable -- cgit v1.2.1