From 3cddc58e6fcdd5341b354dc5b5f784ec8190f62b Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Mon, 14 Mar 2022 15:41:56 +0100 Subject: Reduce check time for CRAN, release --- docs/reference/Rplot001.png | Bin 14083 -> 1011 bytes docs/reference/Rplot002.png | Bin 13699 -> 57977 bytes docs/reference/Rplot003.png | Bin 48687 -> 59128 bytes docs/reference/create_deg_func.html | 10 +++++----- docs/reference/nlme.mmkin.html | 9 ++++----- docs/reference/summary.nlme.mmkin.html | 17 +++++++++++------ 6 files changed, 20 insertions(+), 16 deletions(-) (limited to 'docs/reference') diff --git a/docs/reference/Rplot001.png b/docs/reference/Rplot001.png index ca982688..17a35806 100644 Binary files a/docs/reference/Rplot001.png and b/docs/reference/Rplot001.png differ diff --git a/docs/reference/Rplot002.png b/docs/reference/Rplot002.png index de2d61aa..ddd3a0d7 100644 Binary files a/docs/reference/Rplot002.png and b/docs/reference/Rplot002.png differ diff --git a/docs/reference/Rplot003.png b/docs/reference/Rplot003.png index f8bf10bb..fa29fc43 100644 Binary files a/docs/reference/Rplot003.png and b/docs/reference/Rplot003.png differ diff --git a/docs/reference/create_deg_func.html b/docs/reference/create_deg_func.html index 57516fba..2516edff 100644 --- a/docs/reference/create_deg_func.html +++ b/docs/reference/create_deg_func.html @@ -111,8 +111,8 @@ #> Temporary DLL for differentials generated and loaded FOCUS_D <- subset(FOCUS_2006_D, value != 0) # to avoid warnings fit_1 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = "analytical", quiet = TRUE) -fit_2 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = "deSolve", quiet = TRUE) # \dontrun{ +fit_2 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = "deSolve", quiet = TRUE) if (require(rbenchmark)) benchmark( analytical = mkinfit(SFO_SFO, FOCUS_D, solution_type = "analytical", quiet = TRUE), @@ -120,8 +120,8 @@ replications = 2) #> Loading required package: rbenchmark #> test replications elapsed relative user.self sys.self user.child -#> 1 analytical 2 0.406 1.000 0.407 0.000 0 -#> 2 deSolve 2 0.698 1.719 0.695 0.003 0 +#> 1 analytical 2 0.394 1.000 0.393 0 0 +#> 2 deSolve 2 0.678 1.721 0.677 0 0 #> sys.child #> 1 0 #> 2 0 @@ -134,8 +134,8 @@ deSolve = mkinfit(DFOP_SFO, FOCUS_D, solution_type = "deSolve", quiet = TRUE), replications = 2) #> test replications elapsed relative user.self sys.self user.child -#> 1 analytical 2 0.855 1.000 0.856 0 0 -#> 2 deSolve 2 1.588 1.857 1.587 0 0 +#> 1 analytical 2 0.829 1.000 0.829 0 0 +#> 2 deSolve 2 1.559 1.881 1.559 0 0 #> sys.child #> 1 0 #> 2 0 diff --git a/docs/reference/nlme.mmkin.html b/docs/reference/nlme.mmkin.html index 4b59afed..1f071ffa 100644 --- a/docs/reference/nlme.mmkin.html +++ b/docs/reference/nlme.mmkin.html @@ -97,7 +97,7 @@ have been obtained by fitting the same model to a list of datasets.

data = "auto", fixed = lapply(as.list(names(mean_degparms(model))), function(el) eval(parse(text = paste(el, 1, sep = "~")))), - random = pdDiag(fixed), + random = pdDiag(fixed), groups, start = mean_degparms(model, random = TRUE, test_log_parms = TRUE), correlation = NULL, @@ -187,12 +187,11 @@ methods that will automatically work on 'nlme.mmkin' objects, such as

Examples

ds <- lapply(experimental_data_for_UBA_2019[6:10],
  function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
-f <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1)
-library(nlme)
-f_nlme_sfo <- nlme(f["SFO", ])
 
 # \dontrun{
-
+  f <- mmkin(c("SFO", "DFOP"), ds, quiet = TRUE, cores = 1)
+  library(nlme)
+  f_nlme_sfo <- nlme(f["SFO", ])
   f_nlme_dfop <- nlme(f["DFOP", ])
   anova(f_nlme_sfo, f_nlme_dfop)
 #>             Model df      AIC      BIC    logLik   Test  L.Ratio p-value
diff --git a/docs/reference/summary.nlme.mmkin.html b/docs/reference/summary.nlme.mmkin.html
index d2ed2a86..3f98bec7 100644
--- a/docs/reference/summary.nlme.mmkin.html
+++ b/docs/reference/summary.nlme.mmkin.html
@@ -96,7 +96,7 @@ endpoints such as formation fractions and DT50 values. Optionally
 
     

     
# S3 method for nlme.mmkin
-summary(
+summary(
   object,
   data = FALSE,
   verbose = FALSE,
@@ -193,16 +193,20 @@ José Pinheiro and Douglas Bates for the components inherited from nlme

     n = 1)[[1]]
 })
 
+# \dontrun{
 # Evaluate using mmkin and nlme
 library(nlme)
 f_mmkin <- mmkin("SFO", ds_sfo_syn, quiet = TRUE, error_model = "tc", cores = 1)
+#> Warning: Optimisation did not converge:
+#> iteration limit reached without convergence (10)
 f_nlme <- nlme(f_mmkin)
-summary(f_nlme, data = TRUE)
+#> Warning: Iteration 4, LME step: nlminb() did not converge (code = 1). PORT message: false convergence (8)
+summary(f_nlme, data = TRUE)
 #> nlme version used for fitting:      3.1.155 
 #> mkin version used for pre-fitting:  1.1.0 
-#> R version used for fitting:         4.1.2 
-#> Date of fit:     Mon Mar  7 13:15:33 2022 
-#> Date of summary: Mon Mar  7 13:15:33 2022 
+#> R version used for fitting:         4.1.3 
+#> Date of fit:     Mon Mar 14 08:58:29 2022 
+#> Date of summary: Mon Mar 14 08:58:29 2022 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -212,7 +216,7 @@ José Pinheiro and Douglas Bates for the components inherited from nlme

 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted in 0.528 s using 4 iterations
+#> Fitted in 0.527 s using 4 iterations
 #> 
 #> Variance model: Two-component variance function 
 #> 
@@ -352,6 +356,7 @@ José Pinheiro and Douglas Bates for the components inherited from nlme

 #>  ds 5 parent   90     20.8    22.692  -1.89165 1.7926    -1.055273
 #>  ds 5 parent  120     13.4    13.768  -0.36790 1.0876    -0.338259
 #>  ds 5 parent  120     13.8    13.768   0.03210 1.0876     0.029517
+# }
 
 

     

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