From 3eefecf0adfbb30b8fb895c244dea6903bcb3e9c Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 31 Jan 2019 16:55:20 +0100 Subject: Restore NAMESPACE which was accidentally overwritten by pkgdown -> roxygen --- docs/reference/add_err.html | 20 ++++++++++++-------- 1 file changed, 12 insertions(+), 8 deletions(-) (limited to 'docs/reference/add_err.html') diff --git a/docs/reference/add_err.html b/docs/reference/add_err.html index 78574e92..c8024d66 100644 --- a/docs/reference/add_err.html +++ b/docs/reference/add_err.html @@ -193,7 +193,7 @@

Examples

# The kinetic model m_SFO_SFO <- mkinmod(parent = mkinsub("SFO", "M1"), - M1 = mkinsub("SFO"), use_of_ff = "max")
#> Error in mkinmod(parent = mkinsub("SFO", "M1"), M1 = mkinsub("SFO"), use_of_ff = "max"): konnte Funktion "mkinmod" nicht finden
+ M1 = mkinsub("SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
# Generate a prediction for a specific set of parameters sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120) @@ -203,12 +203,15 @@ c(k_parent = 0.1, f_parent_to_M1 = 0.5, k_M1 = log(2)/1000), c(parent = 100, M1 = 0), - sampling_times)
#> Error in 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): konnte Funktion "mkinpredict" nicht finden
+ 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 )
#> Error in add_err(d_SFO_SFO, function(x) 10, n = 3, seed = 123456789): konnte Funktion "add_err" nicht finden
+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)
#> Error in names(d_SFO_SFO_err) <- paste("Dataset", 1:3): Objekt 'd_SFO_SFO_err' nicht gefunden
+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 the faster Levenberg-Marquardt algorithm, as the datasets @@ -216,15 +219,16 @@ # checks f_SFO_SFO <- mmkin(list("SFO-SFO" = m_SFO_SFO), d_SFO_SFO_err, cores = 1, - quiet = TRUE, method.modFit = "Marq")
#> Error in mmkin(list(`SFO-SFO` = m_SFO_SFO), d_SFO_SFO_err, cores = 1, quiet = TRUE, method.modFit = "Marq"): konnte Funktion "mmkin" nicht finden
-plot(f_SFO_SFO)
#> Error in plot(f_SFO_SFO): Objekt 'f_SFO_SFO' nicht gefunden
+ quiet = TRUE, method.modFit = "Marq") + +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)
#> Error in plot(f_SFO_SFO[[3]], show_residuals = TRUE): Objekt 'f_SFO_SFO' nicht gefunden
+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])
#> Error in plot(f_SFO_SFO[1, 3]): Objekt 'f_SFO_SFO' nicht gefunden
+plot(f_SFO_SFO[1, 3])