From 0c9b2f0e3c8ce65cb790c9e048476784cbbea070 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Fri, 11 Jun 2021 11:14:45 +0200 Subject: Finished 'summary.nlmixr.mmkin', checks, docs --- docs/dev/reference/nlme.html | 41 ++++++++++------------------------------- 1 file changed, 10 insertions(+), 31 deletions(-) (limited to 'docs/dev/reference/nlme.html') diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html index 78d132e9..55a94443 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 - 1.0.4.9000 + 1.0.5 @@ -155,8 +155,6 @@ datasets. They are used internally by the nlme.m 
nlme_function(object)
 
-mean_degparms(object, random = FALSE, test_log_parms = FALSE, conf.level = 0.6)
-
 nlme_data(object)

Arguments

@@ -166,30 +164,11 @@ datasets. They are used internally by the nlme.m object

An mmkin row object containing several fits of the same model to different datasets

- - 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

A function that can be used with nlme

-

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 groupedData object

See also

@@ -217,7 +196,7 @@ nlme for the case of a single grouping variable ds.

ds <- c(d1 = d1, d2 = d2, d3 = d3) f <- mmkin("SFO", ds, cores = 1, quiet = TRUE) -mean_dp <- mean_degparms(f) +mean_dp <- mean_degparms(f) grouped_data <- nlme_data(f) nlme_f <- nlme_function(f) # These assignments are necessary for these objects to be @@ -237,28 +216,28 @@ 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 -#> 298.2781 307.7372 -144.1391 +#> 300.6824 310.2426 -145.3412 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> Level: ds #> Structure: Diagonal #> parent_0 log_k_parent_sink Residual -#> StdDev: 0.937473 0.7098105 3.83543 +#> StdDev: 1.697361 0.6801209 3.666073 #> #> Fixed effects: parent_0 + log_k_parent_sink ~ 1 #> Value Std.Error DF t-value p-value -#> parent_0 101.76838 1.1445443 45 88.91607 0 -#> log_k_parent_sink -3.05444 0.4195622 45 -7.28008 0 +#> parent_0 100.99378 1.3890416 46 72.70753 0 +#> log_k_parent_sink -3.07521 0.4018589 46 -7.65246 0 #> Correlation: #> prnt_0 -#> log_k_parent_sink 0.034 +#> log_k_parent_sink 0.027 #> #> Standardized Within-Group Residuals: -#> Min Q1 Med Q3 Max -#> -2.61693595 -0.21853231 0.05740682 0.57209372 3.04598764 +#> Min Q1 Med Q3 Max +#> -1.9942823 -0.5622565 0.1791579 0.7165038 2.0704781 #> -#> Number of Observations: 49 +#> Number of Observations: 50 #> 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