From e5d1df9a9b1f0951d7dfbaf24eee4294470b73e2 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 17 Nov 2022 14:54:20 +0100 Subject: Complete update of online docs for v1.2.0 --- docs/reference/nlme.html | 137 +++++++++++++++++++++++++---------------------- 1 file changed, 74 insertions(+), 63 deletions(-) (limited to 'docs/reference/nlme.html') diff --git a/docs/reference/nlme.html b/docs/reference/nlme.html index ff8de9f3..83576e56 100644 --- a/docs/reference/nlme.html +++ b/docs/reference/nlme.html @@ -20,7 +20,7 @@ datasets. They are used internally by the nlme.mmkin() method."> mkin - 1.1.0 + 1.2.0 @@ -47,11 +47,14 @@ datasets. They are used internally by the nlme.mmkin() method."> Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models
  • - Example evaluation of FOCUS Example Dataset Z + Short demo of the multistart method
  • Performance benefit by using compiled model definitions in mkin
  • +
  • + Example evaluation of FOCUS Example Dataset Z +
  • Calculation of time weighted average concentrations with mkin
  • @@ -59,7 +62,10 @@ datasets. They are used internally by the nlme.mmkin() method."> Example evaluation of NAFTA SOP Attachment examples
  • - Some benchmark timings + Benchmark timings for mkin +
  • +
  • + Benchmark timings for saem.mmkin
  • @@ -93,20 +99,25 @@ datasets. They are used internally by the nlme.m
    -
    nlme_function(object)
    -
    -nlme_data(object)
    +
    nlme_function(object)
    +
    +nlme_data(object)

    Arguments

    object

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

    +

    Value

    -

    A function that can be used with nlme -A groupedData object

    + + +

    A function that can be used with nlme

    + + +

    A groupedData object

    See also

    @@ -115,78 +126,78 @@ A

    Examples

    -
    sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
    -m_SFO <- mkinmod(parent = mkinsub("SFO"))
    -d_SFO_1 <- mkinpredict(m_SFO,
    -  c(k_parent = 0.1),
    -  c(parent = 98), sampling_times)
    -d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time")
    -d_SFO_2 <- mkinpredict(m_SFO,
    -  c(k_parent = 0.05),
    -  c(parent = 102), sampling_times)
    -d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time")
    -d_SFO_3 <- mkinpredict(m_SFO,
    -  c(k_parent = 0.02),
    -  c(parent = 103), sampling_times)
    -d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")
    -
    -d1 <- add_err(d_SFO_1, function(value) 3, n = 1)
    -d2 <- add_err(d_SFO_2, function(value) 2, n = 1)
    -d3 <- add_err(d_SFO_3, function(value) 4, n = 1)
    -ds <- c(d1 = d1, d2 = d2, d3 = d3)
    -
    -f <- mmkin("SFO", ds, cores = 1, quiet = TRUE)
    -mean_dp <- mean_degparms(f)
    -grouped_data <- nlme_data(f)
    -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 generate the html docs.
    -assign("nlme_f", nlme_f, globalenv())
    -assign("grouped_data", grouped_data, globalenv())
    -
    -library(nlme)
    -m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),
    -  data = grouped_data,
    -  fixed = parent_0 + log_k_parent_sink ~ 1,
    -  random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
    -  start = mean_dp)
    -summary(m_nlme)
    +    
    sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
    +m_SFO <- mkinmod(parent = mkinsub("SFO"))
    +d_SFO_1 <- mkinpredict(m_SFO,
    +  c(k_parent = 0.1),
    +  c(parent = 98), sampling_times)
    +d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time")
    +d_SFO_2 <- mkinpredict(m_SFO,
    +  c(k_parent = 0.05),
    +  c(parent = 102), sampling_times)
    +d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time")
    +d_SFO_3 <- mkinpredict(m_SFO,
    +  c(k_parent = 0.02),
    +  c(parent = 103), sampling_times)
    +d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")
    +
    +d1 <- add_err(d_SFO_1, function(value) 3, n = 1)
    +d2 <- add_err(d_SFO_2, function(value) 2, n = 1)
    +d3 <- add_err(d_SFO_3, function(value) 4, n = 1)
    +ds <- c(d1 = d1, d2 = d2, d3 = d3)
    +
    +f <- mmkin("SFO", ds, cores = 1, quiet = TRUE)
    +mean_dp <- mean_degparms(f)
    +grouped_data <- nlme_data(f)
    +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 generate the html docs.
    +assign("nlme_f", nlme_f, globalenv())
    +assign("grouped_data", grouped_data, globalenv())
    +
    +library(nlme)
    +m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),
    +  data = grouped_data,
    +  fixed = parent_0 + log_k_parent_sink ~ 1,
    +  random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
    +  start = mean_dp)
    +summary(m_nlme)
     #> Nonlinear mixed-effects model fit by maximum likelihood
     #>   Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) 
     #>   Data: grouped_data 
     #>        AIC      BIC    logLik
    -#>   300.6824 310.2426 -145.3412
    +#>   266.6428 275.8935 -128.3214
     #> 
     #> 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
    +#>            parent_0 log_k_parent_sink Residual
    +#> StdDev: 0.000368491         0.7058039 3.065183
     #> 
     #> 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
    +#>                       Value Std.Error DF   t-value p-value
    +#> parent_0          101.18323 0.7900461 43 128.07257       0
    +#> log_k_parent_sink  -3.08708 0.4171755 43  -7.39995       0
     #>  Correlation: 
     #>                   prnt_0
    -#> log_k_parent_sink 0.027 
    +#> log_k_parent_sink 0.031 
     #> 
     #> Standardized Within-Group Residuals:
    -#>        Min         Q1        Med         Q3        Max 
    -#> -1.9942823 -0.5622565  0.1791579  0.7165038  2.0704781 
    +#>         Min          Q1         Med          Q3         Max 
    +#> -2.38427070 -0.52059848  0.03593021  0.39987268  2.73188969 
     #> 
    -#> Number of Observations: 50
    +#> Number of Observations: 47
     #> Number of Groups: 3 
    -plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
    +plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
     
    -# augPred does not work on fits with more than one state
    -# variable
    -#
    -# The procedure is greatly simplified by the nlme.mmkin function
    -f_nlme <- nlme(f)
    -plot(f_nlme)
    +# augPred does not work on fits with more than one state
    +# variable
    +#
    +# The procedure is greatly simplified by the nlme.mmkin function
    +f_nlme <- nlme(f)
    +plot(f_nlme)
     
     
    @@ -202,7 +213,7 @@ A -

    Site built with pkgdown 2.0.3.

    +

    Site built with pkgdown 2.0.6.

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