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/add_err.html | 139 ++++++++++++++++++++++++++------------------ 1 file changed, 81 insertions(+), 58 deletions(-) (limited to 'docs/reference/add_err.html') diff --git a/docs/reference/add_err.html b/docs/reference/add_err.html index 3a129533..4332ba05 100644 --- a/docs/reference/add_err.html +++ b/docs/reference/add_err.html @@ -19,7 +19,7 @@ may depend on the predicted value and is specified as a standard deviation."> mkin - 1.1.0 + 1.2.0 @@ -46,11 +46,14 @@ may depend on the predicted value and is specified as a standard deviation.">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
    • @@ -58,7 +61,10 @@ may depend on the predicted value and is specified as a standard deviation.">Example evaluation of NAFTA SOP Attachment examples
  • - Some benchmark timings + Benchmark timings for mkin +
    • +
  • + Benchmark timings for saem.mmkin
  • @@ -91,16 +97,16 @@ may depend on the predicted value and is specified as a standard deviation.

    - 
    add_err(
-  prediction,
-  sdfunc,
-  secondary = c("M1", "M2"),
-  n = 10,
-  LOD = 0.1,
-  reps = 2,
-  digits = 1,
-  seed = NA
-)
    + 
    add_err(
+  prediction,
+  sdfunc,
+  secondary = c("M1", "M2"),
+  n = 10,
+  LOD = 0.1,
+  reps = 2,
+  digits = 1,
+  seed = NA
+)
    @@ -108,29 +114,46 @@ may depend on the predicted value and is specified as a standard deviation.

    prediction

    A prediction from a kinetic model as produced by mkinpredict.

    + +
    sdfunc

    A function taking the predicted value as its only argument and returning a standard deviation that should be used for generating the random error terms for this value.

    + +
    secondary

    The names of state variables that should have an initial value of zero

    + +
    n

    The number of datasets to be generated.

    + +
    LOD

    The limit of detection (LOD). Values that are below the LOD after adding the random error will be set to NA.

    + +
    reps

    The number of replicates to be generated within the datasets.

    + +
    digits

    The number of digits to which the values will be rounded.

    + +
    seed

    The seed used for the generation of random numbers. If NA, the seed is not set.

    +

    Value

    -

    A list of datasets compatible with mmkin, i.e. the + + +

    A list of datasets compatible with mmkin, i.e. the components of the list are datasets compatible with mkinfit.

    @@ -147,53 +170,53 @@ https://jrwb.de/posters/piacenza_2015.pdf

    Examples

    - 
    
-# The kinetic model
-m_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"),
-                     M1 = mkinsub("SFO"), use_of_ff = "max")
+    
    
+# The kinetic model
+m_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"),
+                     M1 = mkinsub("SFO"), use_of_ff = "max")
 #> Temporary DLL for differentials generated and loaded
-
-# Generate a prediction for a specific set of parameters
-sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-
-# This is the prediction used for the "Type 2 datasets" on the Piacenza poster
-# from 2015
-d_SFO_SFO <- mkinpredict(m_SFO_SFO,
-                         c(k_parent = 0.1, f_parent_to_M1 = 0.5,
-                           k_M1 = log(2)/1000),
-                         c(parent = 100, M1 = 0),
-                         sampling_times)
-
-# Add an error term with a constant (independent of the value) standard deviation
-# of 10, and generate three datasets
-d_SFO_SFO_err <- add_err(d_SFO_SFO, function(x) 10, n = 3, seed = 123456789 )
-
-# Name the datasets for nicer plotting
-names(d_SFO_SFO_err) <- paste("Dataset", 1:3)
-
-# Name the model in the list of models (with only one member in this case) for
-# nicer plotting later on.  Be quiet and use only one core not to offend CRAN
-# checks
-# \dontrun{
-f_SFO_SFO <- mmkin(list("SFO-SFO" = m_SFO_SFO),
-                   d_SFO_SFO_err, cores = 1,
-                   quiet = TRUE)
-
-plot(f_SFO_SFO)
+
+# Generate a prediction for a specific set of parameters
+sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+
+# This is the prediction used for the "Type 2 datasets" on the Piacenza poster
+# from 2015
+d_SFO_SFO <- mkinpredict(m_SFO_SFO,
+                         c(k_parent = 0.1, f_parent_to_M1 = 0.5,
+                           k_M1 = log(2)/1000),
+                         c(parent = 100, M1 = 0),
+                         sampling_times)
+
+# Add an error term with a constant (independent of the value) standard deviation
+# of 10, and generate three datasets
+d_SFO_SFO_err <- add_err(d_SFO_SFO, function(x) 10, n = 3, seed = 123456789 )
+
+# Name the datasets for nicer plotting
+names(d_SFO_SFO_err) <- paste("Dataset", 1:3)
+
+# Name the model in the list of models (with only one member in this case) for
+# nicer plotting later on.  Be quiet and use only one core not to offend CRAN
+# checks
+# \dontrun{
+f_SFO_SFO <- mmkin(list("SFO-SFO" = m_SFO_SFO),
+                   d_SFO_SFO_err, cores = 1,
+                   quiet = TRUE)
+
+plot(f_SFO_SFO)
 
-
-# We would like to inspect the fit for dataset 3 more closely
-# Using double brackets makes the returned object an mkinfit object
-# instead of a list of mkinfit objects, so plot.mkinfit is used
-plot(f_SFO_SFO[[3]], show_residuals = TRUE)
+
+# We would like to inspect the fit for dataset 3 more closely
+# Using double brackets makes the returned object an mkinfit object
+# instead of a list of mkinfit objects, so plot.mkinfit is used
+plot(f_SFO_SFO[[3]], show_residuals = TRUE)
 
-
-# If we use single brackets, we should give two indices (model and dataset),
-# and plot.mmkin is used
-plot(f_SFO_SFO[1, 3])
+
+# If we use single brackets, we should give two indices (model and dataset),
+# and plot.mmkin is used
+plot(f_SFO_SFO[1, 3])
 
-# }
-
+# }
+
    @@ -208,7 +231,7 @@ https://jrwb.de/posters/piacenza_2015.pdf

    -

    Site built with pkgdown 2.0.3.

    +

    Site built with pkgdown 2.0.6.

    -- cgit v1.2.1