diff options
| author | Johannes Ranke <jranke@uni-bremen.de> | 2016-03-24 08:35:26 +0100 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2016-03-24 08:35:26 +0100 |
| commit | 3ea655cdbefcf2056da456b7debc68ba7b535f55 (patch) | |
| tree | fb0aaaab2c0095dbab54cacd23f58502878192ed /inst/web | |
| parent | d432a48c8e7cc2df95d4952af415f18809f60409 (diff) | |
Static documentation rebuilt by staticdocs::build_site()v0.9.42
Diffstat (limited to 'inst/web')
42 files changed, 230 insertions, 223 deletions
diff --git a/inst/web/DFOP.solution.html b/inst/web/DFOP.solution.html index 4746beef..fb73ffb8 100644 --- a/inst/web/DFOP.solution.html +++ b/inst/web/DFOP.solution.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>DFOP.solution. mkin 0.9.43</title> +<title>DFOP.solution. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/Extract.mmkin.html b/inst/web/Extract.mmkin.html index 2d77f552..f31692de 100644 --- a/inst/web/Extract.mmkin.html +++ b/inst/web/Extract.mmkin.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>[.mmkin. mkin 0.9.43</title> +<title>[.mmkin. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> @@ -181,7 +181,7 @@ $calls $time user system elapsed - 0.276 0.000 0.273 + 0.284 0.000 0.286 $mkinmod <mkinmod> model generated with @@ -367,7 +367,7 @@ function (P) } return(mC) } -<environment: 0x2b9db08> +<environment: 0x1bdcb08> $cost_notrans function (P) @@ -389,7 +389,7 @@ function (P) scaleVar = scaleVar) return(mC) } -<environment: 0x2b9db08> +<environment: 0x1bdcb08> $hessian_notrans parent_0 alpha beta @@ -455,7 +455,7 @@ $bparms.state 99.66619 $date -[1] "Thu Mar 24 08:26:19 2016" +[1] "Thu Mar 24 08:28:51 2016" attr(,"class") [1] "mkinfit" "modFit" @@ -540,7 +540,7 @@ $calls $time user system elapsed - 0.088 0.004 0.090 + 0.088 0.008 0.096 $mkinmod <mkinmod> model generated with @@ -727,7 +727,7 @@ function (P) } return(mC) } -<environment: 0x47e8398> +<environment: 0x3827398> $cost_notrans function (P) @@ -749,7 +749,7 @@ function (P) scaleVar = scaleVar) return(mC) } -<environment: 0x47e8398> +<environment: 0x3827398> $hessian_notrans parent_0 k_parent_sink @@ -812,7 +812,7 @@ $bparms.state 99.17407 $date -[1] "Thu Mar 24 08:26:19 2016" +[1] "Thu Mar 24 08:28:51 2016" attr(,"class") [1] "mkinfit" "modFit" @@ -890,7 +890,7 @@ $calls $time user system elapsed - 0.088 0.004 0.090 + 0.088 0.008 0.096 $mkinmod <mkinmod> model generated with @@ -1077,7 +1077,7 @@ function (P) } return(mC) } -<environment: 0x47e8398> +<environment: 0x3827398> $cost_notrans function (P) @@ -1099,7 +1099,7 @@ function (P) scaleVar = scaleVar) return(mC) } -<environment: 0x47e8398> +<environment: 0x3827398> $hessian_notrans parent_0 k_parent_sink @@ -1162,7 +1162,7 @@ $bparms.state 99.17407 $date -[1] "Thu Mar 24 08:26:19 2016" +[1] "Thu Mar 24 08:28:51 2016" attr(,"class") [1] "mkinfit" "modFit" diff --git a/inst/web/FOCUS_2006_DFOP_ref_A_to_B.html b/inst/web/FOCUS_2006_DFOP_ref_A_to_B.html index e5dad993..76911ba7 100644 --- a/inst/web/FOCUS_2006_DFOP_ref_A_to_B.html +++ b/inst/web/FOCUS_2006_DFOP_ref_A_to_B.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>FOCUS_2006_DFOP_ref_A_to_B. mkin 0.9.43</title> +<title>FOCUS_2006_DFOP_ref_A_to_B. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/FOCUS_2006_FOMC_ref_A_to_F.html b/inst/web/FOCUS_2006_FOMC_ref_A_to_F.html index ce238c92..ac250110 100644 --- a/inst/web/FOCUS_2006_FOMC_ref_A_to_F.html +++ b/inst/web/FOCUS_2006_FOMC_ref_A_to_F.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>FOCUS_2006_FOMC_ref_A_to_F. mkin 0.9.43</title> +<title>FOCUS_2006_FOMC_ref_A_to_F. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/FOCUS_2006_HS_ref_A_to_F.html b/inst/web/FOCUS_2006_HS_ref_A_to_F.html index a1abc1b6..e7f5cdd5 100644 --- a/inst/web/FOCUS_2006_HS_ref_A_to_F.html +++ b/inst/web/FOCUS_2006_HS_ref_A_to_F.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>FOCUS_2006_HS_ref_A_to_F. mkin 0.9.43</title> +<title>FOCUS_2006_HS_ref_A_to_F. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/FOCUS_2006_SFO_ref_A_to_F.html b/inst/web/FOCUS_2006_SFO_ref_A_to_F.html index badb4c41..5bbdaa24 100644 --- a/inst/web/FOCUS_2006_SFO_ref_A_to_F.html +++ b/inst/web/FOCUS_2006_SFO_ref_A_to_F.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>FOCUS_2006_SFO_ref_A_to_F. mkin 0.9.43</title> +<title>FOCUS_2006_SFO_ref_A_to_F. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/FOCUS_2006_datasets.html b/inst/web/FOCUS_2006_datasets.html index 7aa7f78b..a2ea626c 100644 --- a/inst/web/FOCUS_2006_datasets.html +++ b/inst/web/FOCUS_2006_datasets.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>FOCUS_2006_datasets. mkin 0.9.43</title> +<title>FOCUS_2006_datasets. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/FOMC.solution.html b/inst/web/FOMC.solution.html index 5c17afe4..2f6a4097 100644 --- a/inst/web/FOMC.solution.html +++ b/inst/web/FOMC.solution.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>FOMC.solution. mkin 0.9.43</title> +<title>FOMC.solution. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/HS.solution.html b/inst/web/HS.solution.html index 7f84cccd..d009aa98 100644 --- a/inst/web/HS.solution.html +++ b/inst/web/HS.solution.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>HS.solution. mkin 0.9.43</title> +<title>HS.solution. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/IORE.solution.html b/inst/web/IORE.solution.html index f735b3ad..1491bccb 100644 --- a/inst/web/IORE.solution.html +++ b/inst/web/IORE.solution.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>IORE.solution. mkin 0.9.43</title> +<title>IORE.solution. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/SFO.solution.html b/inst/web/SFO.solution.html index 2f0d2ff1..33ba8778 100644 --- a/inst/web/SFO.solution.html +++ b/inst/web/SFO.solution.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>SFO.solution. mkin 0.9.43</title> +<title>SFO.solution. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/SFORB.solution.html b/inst/web/SFORB.solution.html index 0285188b..42c5a57f 100644 --- a/inst/web/SFORB.solution.html +++ b/inst/web/SFORB.solution.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>SFORB.solution. mkin 0.9.43</title> +<title>SFORB.solution. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/endpoints.html b/inst/web/endpoints.html index 0566d93f..9ddcc8b4 100644 --- a/inst/web/endpoints.html +++ b/inst/web/endpoints.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>endpoints. mkin 0.9.43</title> +<title>endpoints. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/geometric_mean.html b/inst/web/geometric_mean.html index 82caad0e..9d47ebdc 100644 --- a/inst/web/geometric_mean.html +++ b/inst/web/geometric_mean.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>geometric_mean. mkin 0.9.43</title> +<title>geometric_mean. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/ilr.html b/inst/web/ilr.html index bcf1fc26..cb0eef23 100644 --- a/inst/web/ilr.html +++ b/inst/web/ilr.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>ilr. mkin 0.9.43</title> +<title>ilr. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" René Lehmann and Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/index.html b/inst/web/index.html index 7d034fc9..270d14f3 100644 --- a/inst/web/index.html +++ b/inst/web/index.html @@ -51,7 +51,7 @@ <div class="span8"> <h1>mkin</h1> -<p><a href="https://cran.rstudio.com/web/packages/mkin/index.html"><img src="http://www.r-pkg.org/badges/version/mkin" alt=""/></a></p> +<p><a href="http://cran.r-project.org/package=mkin"><img src="http://www.r-pkg.org/badges/version/mkin" alt=""/></a></p> <p>The R package <strong>mkin</strong> provides calculation routines for the analysis of chemical degradation data, including <b>m</b>ulticompartment <b>kin</b>etics as diff --git a/inst/web/mccall81_245T.html b/inst/web/mccall81_245T.html index 00338040..6266c0b5 100644 --- a/inst/web/mccall81_245T.html +++ b/inst/web/mccall81_245T.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>mccall81_245T. mkin 0.9.43</title> +<title>mccall81_245T. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> @@ -112,10 +112,10 @@ fixed_parms = "k_phenol_sink", quiet = TRUE) summary(fit.2, data = FALSE) </div> -<div class='output'>mkin version: 0.9.43 +<div class='output'>mkin version: 0.9.42 R version: 3.2.4 -Date of fit: Thu Mar 24 08:26:26 2016 -Date of summary: Thu Mar 24 08:26:26 2016 +Date of fit: Thu Mar 24 08:28:59 2016 +Date of summary: Thu Mar 24 08:28:59 2016 Equations: d_T245 = - k_T245_sink * T245 - k_T245_phenol * T245 @@ -124,7 +124,7 @@ d_anisole = + k_phenol_anisole * phenol - k_anisole_sink * anisole Model predictions using solution type deSolve -Fitted with method Port using 246 model solutions performed in 1.451 s +Fitted with method Port using 246 model solutions performed in 1.454 s Weighting: none diff --git a/inst/web/mkin_long_to_wide.html b/inst/web/mkin_long_to_wide.html index e58ce305..de36532f 100644 --- a/inst/web/mkin_long_to_wide.html +++ b/inst/web/mkin_long_to_wide.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>mkin_long_to_wide. mkin 0.9.43</title> +<title>mkin_long_to_wide. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/mkin_wide_to_long.html b/inst/web/mkin_wide_to_long.html index ab4f7dbb..08abc942 100644 --- a/inst/web/mkin_wide_to_long.html +++ b/inst/web/mkin_wide_to_long.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>mkin_wide_to_long. mkin 0.9.43</title> +<title>mkin_wide_to_long. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/mkinds.html b/inst/web/mkinds.html index f89128bf..5e0f5001 100644 --- a/inst/web/mkinds.html +++ b/inst/web/mkinds.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>mkinds. mkin 0.9.43</title> +<title>mkinds. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/mkinerrmin.html b/inst/web/mkinerrmin.html index ba665376..f5ab043f 100644 --- a/inst/web/mkinerrmin.html +++ b/inst/web/mkinerrmin.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>mkinerrmin. mkin 0.9.43</title> +<title>mkinerrmin. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/mkinfit.html b/inst/web/mkinfit.html index 43aee184..07987fc2 100644 --- a/inst/web/mkinfit.html +++ b/inst/web/mkinfit.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>mkinfit. mkin 0.9.43</title> +<title>mkinfit. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> @@ -323,17 +323,17 @@ fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE) summary(fit) </div> -<div class='output'>mkin version: 0.9.43 +<div class='output'>mkin version: 0.9.42 R version: 3.2.4 -Date of fit: Thu Mar 24 08:26:29 2016 -Date of summary: Thu Mar 24 08:26:29 2016 +Date of fit: Thu Mar 24 08:29:01 2016 +Date of summary: Thu Mar 24 08:29:01 2016 Equations: d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent Model predictions using solution type analytical -Fitted with method Port using 64 model solutions performed in 0.205 s +Fitted with method Port using 64 model solutions performed in 0.195 s Weighting: none @@ -409,7 +409,7 @@ print(system.time(fit <- mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "eigen", quiet = TRUE))) </div> <div class='output'> user system elapsed - 1.200 1.252 0.926 + 1.276 1.196 0.935 </div> <div class='input'>coef(fit) </div> diff --git a/inst/web/mkinmod.html b/inst/web/mkinmod.html index 0a361956..b3a396b9 100644 --- a/inst/web/mkinmod.html +++ b/inst/web/mkinmod.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>mkinmod. mkin 0.9.43</title> +<title>mkinmod. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/mkinparplot.html b/inst/web/mkinparplot.html index 59fddb39..8eee6868 100644 --- a/inst/web/mkinparplot.html +++ b/inst/web/mkinparplot.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>mkinparplot. mkin 0.9.43</title> +<title>mkinparplot. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/mkinplot.html b/inst/web/mkinplot.html index 1b9a8a02..c4542072 100644 --- a/inst/web/mkinplot.html +++ b/inst/web/mkinplot.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>mkinplot. mkin 0.9.43</title> +<title>mkinplot. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/mkinpredict.html b/inst/web/mkinpredict.html index c831f011..94c7a011 100644 --- a/inst/web/mkinpredict.html +++ b/inst/web/mkinpredict.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>mkinpredict. mkin 0.9.43</title> +<title>mkinpredict. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> @@ -304,7 +304,7 @@ 201 20 4.978707 27.46227 </div> <div class='output'> user system elapsed - 0.008 0.020 0.005 + 0.008 0.020 0.004 </div> <div class='input'> system.time( print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), @@ -315,7 +315,7 @@ 201 20 4.978707 27.46227 </div> <div class='output'> user system elapsed - 0.008 0.012 0.003 + 0.000 0.024 0.003 </div> <div class='input'> system.time( print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), @@ -326,7 +326,7 @@ 201 20 4.978707 27.46227 </div> <div class='output'> user system elapsed - 0.056 0.000 0.052 + 0.052 0.000 0.052 </div></pre> </div> <div class="span4"> diff --git a/inst/web/mkinresplot.html b/inst/web/mkinresplot.html index b5f32633..5a2388dc 100644 --- a/inst/web/mkinresplot.html +++ b/inst/web/mkinresplot.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>mkinresplot. mkin 0.9.43</title> +<title>mkinresplot. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/mkinsub.html b/inst/web/mkinsub.html index bd8e1de6..2b92fbbc 100644 --- a/inst/web/mkinsub.html +++ b/inst/web/mkinsub.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>mkinsub. mkin 0.9.43</title> +<title>mkinsub. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/mmkin.html b/inst/web/mmkin.html index d2d1ac28..754be268 100644 --- a/inst/web/mmkin.html +++ b/inst/web/mmkin.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>mmkin. mkin 0.9.43</title> +<title>mmkin. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/plot.mkinfit.html b/inst/web/plot.mkinfit.html index 11e11d7f..1da0c8e8 100644 --- a/inst/web/plot.mkinfit.html +++ b/inst/web/plot.mkinfit.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>plot.mkinfit. mkin 0.9.43</title> +<title>plot.mkinfit. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/plot.mmkin.html b/inst/web/plot.mmkin.html index def3a11d..587bf894 100644 --- a/inst/web/plot.mmkin.html +++ b/inst/web/plot.mmkin.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>plot.mmkin. mkin 0.9.43</title> +<title>plot.mmkin. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/print.mkinds.html b/inst/web/print.mkinds.html index 6ff9e02a..285684fd 100644 --- a/inst/web/print.mkinds.html +++ b/inst/web/print.mkinds.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>print.mkinds. mkin 0.9.43</title> +<title>print.mkinds. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/print.mkinmod.html b/inst/web/print.mkinmod.html index 16c59669..d773050d 100644 --- a/inst/web/print.mkinmod.html +++ b/inst/web/print.mkinmod.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>print.mkinmod. mkin 0.9.43</title> +<title>print.mkinmod. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/schaefer07_complex_case.html b/inst/web/schaefer07_complex_case.html index f2975fe6..c6b9e980 100644 --- a/inst/web/schaefer07_complex_case.html +++ b/inst/web/schaefer07_complex_case.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>schaefer07_complex_case. mkin 0.9.43</title> +<title>schaefer07_complex_case. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/summary.mkinfit.html b/inst/web/summary.mkinfit.html index 4ef29e42..6fc6d964 100644 --- a/inst/web/summary.mkinfit.html +++ b/inst/web/summary.mkinfit.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>summary.mkinfit. mkin 0.9.43</title> +<title>summary.mkinfit. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> @@ -157,17 +157,17 @@ <h2 id="examples">Examples</h2> <pre class="examples"><div class='input'> summary(mkinfit(mkinmod(parent = list(type = "SFO")), FOCUS_2006_A, quiet = TRUE)) </div> -<div class='output'>mkin version: 0.9.43 +<div class='output'>mkin version: 0.9.42 R version: 3.2.4 -Date of fit: Thu Mar 24 08:26:38 2016 -Date of summary: Thu Mar 24 08:26:38 2016 +Date of fit: Thu Mar 24 08:29:11 2016 +Date of summary: Thu Mar 24 08:29:11 2016 Equations: d_parent = - k_parent_sink * parent Model predictions using solution type analytical -Fitted with method Port using 35 model solutions performed in 0.099 s +Fitted with method Port using 35 model solutions performed in 0.096 s Weighting: none diff --git a/inst/web/synthetic_data_for_UBA.html b/inst/web/synthetic_data_for_UBA.html index 56a646c1..9fb9d23e 100644 --- a/inst/web/synthetic_data_for_UBA.html +++ b/inst/web/synthetic_data_for_UBA.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>synthetic_data_for_UBA_2014. mkin 0.9.43</title> +<title>synthetic_data_for_UBA_2014. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=""> @@ -32,7 +32,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> diff --git a/inst/web/transform_odeparms.html b/inst/web/transform_odeparms.html index 5e16551f..f12ede67 100644 --- a/inst/web/transform_odeparms.html +++ b/inst/web/transform_odeparms.html @@ -2,7 +2,7 @@ <html lang="en"> <head> <meta charset="utf-8"> -<title>transform_odeparms. mkin 0.9.43</title> +<title>transform_odeparms. mkin 0.9.42</title> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta name="author" content=" Johannes Ranke @@ -34,7 +34,7 @@ <div class="navbar"> <div class="navbar-inner"> <div class="container"> - <a class="brand" href="#">mkin 0.9.43</a> + <a class="brand" href="#">mkin 0.9.42</a> <div class="nav"> <ul class="nav"> <li><a href="index.html"><i class="icon-home icon-white"></i> Index</a></li> @@ -133,10 +133,10 @@ backtransform_odeparms(transparms, mkinmod, transform_rates = TRUE, t fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE) summary(fit, data=FALSE) # See transformed and backtransformed parameters </div> -<div class='output'>mkin version: 0.9.43 +<div class='output'>mkin version: 0.9.42 R version: 3.2.4 -Date of fit: Thu Mar 24 08:26:40 2016 -Date of summary: Thu Mar 24 08:26:40 2016 +Date of fit: Thu Mar 24 08:29:12 2016 +Date of summary: Thu Mar 24 08:29:12 2016 Equations: d_parent = - k_parent_sink * parent - k_parent_m1 * parent @@ -144,7 +144,7 @@ d_m1 = + k_parent_m1 * parent - k_m1_sink * m1 Model predictions using solution type deSolve -Fitted with method Port using 153 model solutions performed in 0.662 s +Fitted with method Port using 153 model solutions performed in 0.654 s Weighting: none diff --git a/inst/web/vignettes/FOCUS_D.html b/inst/web/vignettes/FOCUS_D.html index 1f3c6677..964c1ad8 100644 --- a/inst/web/vignettes/FOCUS_D.html +++ b/inst/web/vignettes/FOCUS_D.html @@ -10,7 +10,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2016-03-23" /> +<meta name="date" content="2016-03-24" /> <title>Example evaluation of FOCUS Example Dataset D</title> @@ -64,7 +64,7 @@ img { <div id="header"> <h1 class="title">Example evaluation of FOCUS Example Dataset D</h1> <h4 class="author"><em>Johannes Ranke</em></h4> -<h4 class="date"><em>2016-03-23</em></h4> +<h4 class="date"><em>2016-03-24</em></h4> </div> @@ -135,10 +135,10 @@ print(FOCUS_2006_D)</code></pre> <p><img src="data:image/png;base64,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" title alt width="672" /></p> <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> <pre class="r"><code>summary(fit)</code></pre> -<pre><code>## mkin version: 0.9.40 -## R version: 3.2.1 -## Date of fit: Mon Jul 20 14:51:06 2015 -## Date of summary: Mon Jul 20 14:51:06 2015 +<pre><code>## mkin version: 0.9.42 +## R version: 3.2.4 +## Date of fit: Thu Mar 24 08:30:54 2016 +## Date of summary: Thu Mar 24 08:30:54 2016 ## ## Equations: ## d_parent = - k_parent_sink * parent - k_parent_m1 * parent @@ -146,7 +146,7 @@ print(FOCUS_2006_D)</code></pre> ## ## Model predictions using solution type deSolve ## -## Fitted with method Port using 153 model solutions performed in 0.66 s +## Fitted with method Port using 153 model solutions performed in 0.659 s ## ## Weighting: none ## @@ -185,9 +185,9 @@ print(FOCUS_2006_D)</code></pre> ## Residual standard error: 3.211 on 36 degrees of freedom ## ## Backtransformed parameters: -## Confidence intervals for internally transformed parameters are asymmetric. -## t-test (unrealistically) based on the assumption of normal distribution -## for estimators of untransformed parameters. +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 99.600000 61.720 2.024e-38 96.330000 1.029e+02 ## k_parent_sink 0.047920 12.780 3.050e-15 0.040890 5.616e-02 diff --git a/inst/web/vignettes/FOCUS_L.html b/inst/web/vignettes/FOCUS_L.html index a7564f72..25551855 100644 --- a/inst/web/vignettes/FOCUS_L.html +++ b/inst/web/vignettes/FOCUS_L.html @@ -10,7 +10,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2016-03-23" /> +<meta name="date" content="2016-03-24" /> <title>Example evaluation of FOCUS Laboratory Data L1 to L3</title> @@ -65,7 +65,7 @@ img { <div id="header"> <h1 class="title">Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author"><em>Johannes Ranke</em></h4> -<h4 class="date"><em>2016-03-23</em></h4> +<h4 class="date"><em>2016-03-24</em></h4> </div> <div id="TOC"> @@ -91,17 +91,17 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> <p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>SFO</code> for parent only degradation models. The following two lines fit the model and produce the summary report of the model fit. This covers the numerical analysis given in the FOCUS report.</p> <pre class="r"><code>m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet=TRUE) summary(m.L1.SFO)</code></pre> -<pre><code>## mkin version: 0.9.40 -## R version: 3.2.1 -## Date of fit: Mon Jul 20 14:51:06 2015 -## Date of summary: Mon Jul 20 14:51:07 2015 +<pre><code>## mkin version: 0.9.42 +## R version: 3.2.4 +## Date of fit: Thu Mar 24 08:30:54 2016 +## Date of summary: Thu Mar 24 08:30:54 2016 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 37 model solutions performed in 0.087 s +## Fitted with method Port using 37 model solutions performed in 0.091 s ## ## Weighting: none ## @@ -131,9 +131,9 @@ summary(m.L1.SFO)</code></pre> ## Residual standard error: 2.948 on 16 degrees of freedom ## ## Backtransformed parameters: -## Confidence intervals for internally transformed parameters are asymmetric. -## t-test (unrealistically) based on the assumption of normal distribution -## for estimators of untransformed parameters. +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 92.47000 67.58 2.170e-21 89.57000 95.3700 ## k_parent_sink 0.09561 24.65 1.867e-14 0.08773 0.1042 @@ -181,10 +181,10 @@ summary(m.L1.SFO)</code></pre> <pre><code>## Warning in mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet = TRUE): Optimisation by method Port did not converge. ## Convergence code is 1</code></pre> <pre class="r"><code>summary(m.L1.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.40 -## R version: 3.2.1 -## Date of fit: Mon Jul 20 14:51:07 2015 -## Date of summary: Mon Jul 20 14:51:07 2015 +<pre><code>## mkin version: 0.9.42 +## R version: 3.2.4 +## Date of fit: Thu Mar 24 08:30:55 2016 +## Date of summary: Thu Mar 24 08:30:55 2016 ## ## ## Warning: Optimisation by method Port did not converge. @@ -196,7 +196,7 @@ summary(m.L1.SFO)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 188 model solutions performed in 0.43 s +## Fitted with method Port using 188 model solutions performed in 0.527 s ## ## Weighting: none ## @@ -230,9 +230,9 @@ summary(m.L1.SFO)</code></pre> ## Residual standard error: 3.045 on 15 degrees of freedom ## ## Backtransformed parameters: -## Confidence intervals for internally transformed parameters are asymmetric. -## t-test (unrealistically) based on the assumption of normal distribution -## for estimators of untransformed parameters. +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 9.247e+01 65.150 4.044e-20 8.944e+01 9.550e+01 ## alpha 5.044e+06 1.271 1.115e-01 5.510e-08 4.618e+20 @@ -261,17 +261,17 @@ FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2)</code></pre> <p>Again, the SFO model is fitted and a summary is obtained:</p> <pre class="r"><code>m.L2.SFO <- mkinfit("SFO", FOCUS_2006_L2_mkin, quiet=TRUE) summary(m.L2.SFO)</code></pre> -<pre><code>## mkin version: 0.9.40 -## R version: 3.2.1 -## Date of fit: Mon Jul 20 14:51:07 2015 -## Date of summary: Mon Jul 20 14:51:07 2015 +<pre><code>## mkin version: 0.9.42 +## R version: 3.2.4 +## Date of fit: Thu Mar 24 08:30:55 2016 +## Date of summary: Thu Mar 24 08:30:55 2016 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 41 model solutions performed in 0.094 s +## Fitted with method Port using 41 model solutions performed in 0.099 s ## ## Weighting: none ## @@ -301,9 +301,9 @@ summary(m.L2.SFO)</code></pre> ## Residual standard error: 5.51 on 10 degrees of freedom ## ## Backtransformed parameters: -## Confidence intervals for internally transformed parameters are asymmetric. -## t-test (unrealistically) based on the assumption of normal distribution -## for estimators of untransformed parameters. +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 91.4700 24.03 1.773e-10 82.9800 99.9500 ## k_parent_sink 0.6629 9.31 1.525e-06 0.5218 0.8421 @@ -349,17 +349,17 @@ plot(m.L2.FOMC) mkinresplot(m.L2.FOMC)</code></pre> <p><img src="data:image/png;base64,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" title alt width="672" /></p> <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.40 -## R version: 3.2.1 -## Date of fit: Mon Jul 20 14:51:08 2015 -## Date of summary: Mon Jul 20 14:51:08 2015 +<pre><code>## mkin version: 0.9.42 +## R version: 3.2.4 +## Date of fit: Thu Mar 24 08:30:56 2016 +## Date of summary: Thu Mar 24 08:30:56 2016 ## ## Equations: ## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 81 model solutions performed in 0.18 s +## Fitted with method Port using 81 model solutions performed in 0.2 s ## ## Weighting: none ## @@ -393,9 +393,9 @@ mkinresplot(m.L2.FOMC)</code></pre> ## Residual standard error: 2.628 on 9 degrees of freedom ## ## Backtransformed parameters: -## Confidence intervals for internally transformed parameters are asymmetric. -## t-test (unrealistically) based on the assumption of normal distribution -## for estimators of untransformed parameters. +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 93.770 50.510 1.173e-12 89.5700 97.970 ## alpha 1.374 5.355 2.296e-04 0.9009 2.097 @@ -421,10 +421,10 @@ plot(m.L2.DFOP)</code></pre> plot(m.L2.DFOP)</code></pre> <p><img src="data:image/png;base64,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" title alt width="672" /></p> <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.40 -## R version: 3.2.1 -## Date of fit: Mon Jul 20 14:51:10 2015 -## Date of summary: Mon Jul 20 14:51:10 2015 +<pre><code>## mkin version: 0.9.42 +## R version: 3.2.4 +## Date of fit: Thu Mar 24 08:30:58 2016 +## Date of summary: Thu Mar 24 08:30:58 2016 ## ## Equations: ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -433,7 +433,7 @@ plot(m.L2.DFOP)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 336 model solutions performed in 0.793 s +## Fitted with method Port using 336 model solutions performed in 0.903 s ## ## Weighting: none ## @@ -467,9 +467,9 @@ plot(m.L2.DFOP)</code></pre> ## Residual standard error: 1.732 on 8 degrees of freedom ## ## Backtransformed parameters: -## Confidence intervals for internally transformed parameters are asymmetric. -## t-test (unrealistically) based on the assumption of normal distribution -## for estimators of untransformed parameters. +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 93.9500 NA NA NA NA ## k1 22.6700 NA NA NA NA @@ -498,17 +498,17 @@ FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3)</code></pre> plot(m.L3.SFO)</code></pre> <p><img src="data:image/png;base64,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" title alt width="672" /></p> <pre class="r"><code>summary(m.L3.SFO)</code></pre> -<pre><code>## mkin version: 0.9.40 -## R version: 3.2.1 -## Date of fit: Mon Jul 20 14:51:10 2015 -## Date of summary: Mon Jul 20 14:51:10 2015 +<pre><code>## mkin version: 0.9.42 +## R version: 3.2.4 +## Date of fit: Thu Mar 24 08:30:58 2016 +## Date of summary: Thu Mar 24 08:30:58 2016 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 43 model solutions performed in 0.103 s +## Fitted with method Port using 43 model solutions performed in 0.104 s ## ## Weighting: none ## @@ -538,9 +538,9 @@ plot(m.L3.SFO)</code></pre> ## Residual standard error: 12.91 on 6 degrees of freedom ## ## Backtransformed parameters: -## Confidence intervals for internally transformed parameters are asymmetric. -## t-test (unrealistically) based on the assumption of normal distribution -## for estimators of untransformed parameters. +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 74.87000 8.853 5.776e-05 54.18000 95.57000 ## k_parent_sink 0.02527 3.067 1.102e-02 0.01138 0.05612 @@ -574,17 +574,17 @@ plot(m.L3.SFO)</code></pre> plot(m.L3.FOMC)</code></pre> <p><img src="data:image/png;base64,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" title alt width="672" /></p> <pre class="r"><code>summary(m.L3.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.40 -## R version: 3.2.1 -## Date of fit: Mon Jul 20 14:51:11 2015 -## Date of summary: Mon Jul 20 14:51:11 2015 +<pre><code>## mkin version: 0.9.42 +## R version: 3.2.4 +## Date of fit: Thu Mar 24 08:30:59 2016 +## Date of summary: Thu Mar 24 08:30:59 2016 ## ## Equations: ## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 83 model solutions performed in 0.187 s +## Fitted with method Port using 83 model solutions performed in 0.208 s ## ## Weighting: none ## @@ -618,9 +618,9 @@ plot(m.L3.FOMC)</code></pre> ## Residual standard error: 4.572 on 5 degrees of freedom ## ## Backtransformed parameters: -## Confidence intervals for internally transformed parameters are asymmetric. -## t-test (unrealistically) based on the assumption of normal distribution -## for estimators of untransformed parameters. +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 96.9700 21.310 2.108e-06 85.2800 108.7000 ## alpha 0.4224 5.867 1.020e-03 0.2725 0.6546 @@ -640,10 +640,10 @@ plot(m.L3.FOMC)</code></pre> plot(m.L3.DFOP)</code></pre> <p><img src="data:image/png;base64,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" title alt width="672" /></p> <pre class="r"><code>summary(m.L3.DFOP, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.40 -## R version: 3.2.1 -## Date of fit: Mon Jul 20 14:51:11 2015 -## Date of summary: Mon Jul 20 14:51:11 2015 +<pre><code>## mkin version: 0.9.42 +## R version: 3.2.4 +## Date of fit: Thu Mar 24 08:30:59 2016 +## Date of summary: Thu Mar 24 08:30:59 2016 ## ## Equations: ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -652,7 +652,7 @@ plot(m.L3.DFOP)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 137 model solutions performed in 0.322 s +## Fitted with method Port using 137 model solutions performed in 0.351 s ## ## Weighting: none ## @@ -690,9 +690,9 @@ plot(m.L3.DFOP)</code></pre> ## Residual standard error: 1.439 on 4 degrees of freedom ## ## Backtransformed parameters: -## Confidence intervals for internally transformed parameters are asymmetric. -## t-test (unrealistically) based on the assumption of normal distribution -## for estimators of untransformed parameters. +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 97.75000 67.970 1.404e-07 93.75000 101.70000 ## k1 0.51620 7.499 8.460e-04 0.35650 0.74750 @@ -722,17 +722,17 @@ FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4)</code></pre> plot(m.L4.SFO)</code></pre> <p><img src="data:image/png;base64,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" title alt width="672" /></p> <pre class="r"><code>summary(m.L4.SFO, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.40 -## R version: 3.2.1 -## Date of fit: Mon Jul 20 14:51:12 2015 -## Date of summary: Mon Jul 20 14:51:12 2015 +<pre><code>## mkin version: 0.9.42 +## R version: 3.2.4 +## Date of fit: Thu Mar 24 08:31:00 2016 +## Date of summary: Thu Mar 24 08:31:00 2016 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 46 model solutions performed in 0.101 s +## Fitted with method Port using 46 model solutions performed in 0.116 s ## ## Weighting: none ## @@ -762,9 +762,9 @@ plot(m.L4.SFO)</code></pre> ## Residual standard error: 3.651 on 6 degrees of freedom ## ## Backtransformed parameters: -## Confidence intervals for internally transformed parameters are asymmetric. -## t-test (unrealistically) based on the assumption of normal distribution -## for estimators of untransformed parameters. +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 96.440000 49.49 2.283e-09 91.670000 1.012e+02 ## k_parent_sink 0.006541 12.50 8.008e-06 0.005378 7.955e-03 @@ -787,17 +787,17 @@ plot(m.L4.SFO)</code></pre> plot(m.L4.FOMC)</code></pre> <p><img src="data:image/png;base64,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" title alt width="672" /></p> <pre class="r"><code>summary(m.L4.FOMC, data = FALSE)</code></pre> -<pre><code>## mkin version: 0.9.40 -## R version: 3.2.1 -## Date of fit: Mon Jul 20 14:51:12 2015 -## Date of summary: Mon Jul 20 14:51:12 2015 +<pre><code>## mkin version: 0.9.42 +## R version: 3.2.4 +## Date of fit: Thu Mar 24 08:31:00 2016 +## Date of summary: Thu Mar 24 08:31:00 2016 ## ## Equations: ## d_parent = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Port using 66 model solutions performed in 0.146 s +## Fitted with method Port using 66 model solutions performed in 0.164 s ## ## Weighting: none ## @@ -831,9 +831,9 @@ plot(m.L4.FOMC)</code></pre> ## Residual standard error: 2.315 on 5 degrees of freedom ## ## Backtransformed parameters: -## Confidence intervals for internally transformed parameters are asymmetric. -## t-test (unrealistically) based on the assumption of normal distribution -## for estimators of untransformed parameters. +## Confidence intervals for internally transformed parameters are asymmetric. +## t-test (unrealistically) based on the assumption of normal distribution +## for estimators of untransformed parameters. ## Estimate t value Pr(>t) Lower Upper ## parent_0 99.1400 59.020 1.322e-08 94.8200 103.500 ## alpha 0.7042 2.685 2.178e-02 0.2703 1.835 diff --git a/inst/web/vignettes/FOCUS_Z.pdf b/inst/web/vignettes/FOCUS_Z.pdf Binary files differindex 8103cba4..3b804e88 100644 --- a/inst/web/vignettes/FOCUS_Z.pdf +++ b/inst/web/vignettes/FOCUS_Z.pdf diff --git a/inst/web/vignettes/compiled_models.html b/inst/web/vignettes/compiled_models.html index 756a2753..50db7e9a 100644 --- a/inst/web/vignettes/compiled_models.html +++ b/inst/web/vignettes/compiled_models.html @@ -10,7 +10,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2016-03-23" /> +<meta name="date" content="2016-03-24" /> <title>Performance benefit by using compiled model definitions in mkin</title> @@ -65,7 +65,7 @@ img { <div id="header"> <h1 class="title">Performance benefit by using compiled model definitions in mkin</h1> <h4 class="author"><em>Johannes Ranke</em></h4> -<h4 class="date"><em>2016-03-23</em></h4> +<h4 class="date"><em>2016-03-24</em></h4> </div> <div id="TOC"> @@ -89,8 +89,10 @@ SFO_SFO <- mkinmod( <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> <p>We can compare the performance of the Eigenvalue based solution against the compiled version and the R implementation of the differential equations using the microbenchmark package.</p> <pre class="r"><code>library("microbenchmark") -library("ggplot2") -mb.1 <- microbenchmark( +library("ggplot2")</code></pre> +<pre><code>## Need help? Try the ggplot2 mailing list: +## http://groups.google.com/group/ggplot2.</code></pre> +<pre class="r"><code>mb.1 <- microbenchmark( "deSolve, not compiled" = mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", use_compiled = FALSE, quiet = TRUE), @@ -98,27 +100,29 @@ mb.1 <- microbenchmark( solution_type = "eigen", quiet = TRUE), "deSolve, compiled" = mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet = TRUE), - times = 3, control = list(warmup = 1)) - -smb.1 <- summary(mb.1) + times = 3, control = list(warmup = 0))</code></pre> +<pre><code>## Warning in microbenchmark(`deSolve, not compiled` = mkinfit(SFO_SFO, +## FOCUS_2006_D, : Could not measure overhead. Your clock might lack +## precision.</code></pre> +<pre class="r"><code>smb.1 <- summary(mb.1) print(mb.1)</code></pre> <pre><code>## Unit: milliseconds ## expr min lq mean median uq -## deSolve, not compiled 9539.3064 9543.1547 9554.1987 9547.0031 9561.6448 -## Eigenvalue based 927.5569 928.1716 943.8293 928.7864 951.9656 -## deSolve, compiled 734.6125 737.3273 739.0161 740.0420 741.2179 +## deSolve, not compiled 9280.0854 9299.6757 9323.2559 9319.2659 9344.8411 +## Eigenvalue based 885.7475 891.8548 907.2823 897.9621 918.0498 +## deSolve, compiled 713.2624 721.4990 728.2856 729.7357 735.7972 ## max neval cld -## 9576.2865 3 c -## 975.1447 3 b -## 742.3938 3 a</code></pre> +## 9370.4163 3 c +## 938.1374 3 b +## 741.8588 3 a</code></pre> <pre class="r"><code>autoplot(mb.1)</code></pre> -<p><img src="data:image/png;base64,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" title alt width="672" /></p> -<p>We see that using the compiled model is by a factor of 12.9 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs:</p> +<p><img src="data:image/png;base64,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" title alt width="672" /></p> +<p>We see that using the compiled model is by a factor of 12.8 faster than using the R version with the default ode solver, and it is even faster than the Eigenvalue based solution implemented in R which does not need iterative solution of the ODEs:</p> <pre class="r"><code>rownames(smb.1) <- smb.1$expr smb.1["median"]/smb.1["deSolve, compiled", "median"]</code></pre> <pre><code>## median -## deSolve, not compiled 12.900624 -## Eigenvalue based 1.255046 +## deSolve, not compiled 12.770742 +## Eigenvalue based 1.230531 ## deSolve, compiled 1.000000</code></pre> </div> <div id="benchmark-for-a-model-that-can-not-be-solved-with-eigenvalues" class="section level2"> @@ -132,23 +136,26 @@ smb.1["median"]/smb.1["deSolve, compiled", "median" "deSolve, not compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, use_compiled = FALSE, quiet = TRUE), "deSolve, compiled" = mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE), - times = 3, control = list(warmup = 1)) -smb.2 <- summary(mb.2) + times = 3, control = list(warmup = 0))</code></pre> +<pre><code>## Warning in microbenchmark(`deSolve, not compiled` = mkinfit(FOMC_SFO, +## FOCUS_2006_D, : Could not measure overhead. Your clock might lack +## precision.</code></pre> +<pre class="r"><code>smb.2 <- summary(mb.2) print(mb.2)</code></pre> <pre><code>## Unit: seconds ## expr min lq mean median uq -## deSolve, not compiled 20.728228 20.867978 20.959811 21.007729 21.075602 -## deSolve, compiled 1.343219 1.382365 1.399697 1.421511 1.427936 -## max neval cld -## 21.143476 3 b -## 1.434362 3 a</code></pre> +## deSolve, not compiled 20.543131 20.661195 20.720383 20.779259 20.809008 +## deSolve, compiled 1.314865 1.316439 1.328049 1.318014 1.334642 +## max neval cld +## 20.83876 3 b +## 1.35127 3 a</code></pre> <pre class="r"><code>smb.2["median"]/smb.2["deSolve, compiled", "median"]</code></pre> <pre><code>## median ## 1 NA ## 2 NA</code></pre> <pre class="r"><code>autoplot(mb.2)</code></pre> -<p><img src="data:image/png;base64,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" title alt width="672" /></p> -<p>Here we get a performance benefit of a factor of 14.8 using the version of the differential equation model compiled from C code using the inline package!</p> +<p><img src="data:image/png;base64,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" title alt width="672" /></p> +<p>Here we get a performance benefit of a factor of 15.8 using the version of the differential equation model compiled from C code using the inline package!</p> <p>This vignette was built with mkin 0.9.42 on</p> <pre><code>## R version 3.2.4 Revised (2016-03-16 r70336) ## Platform: x86_64-pc-linux-gnu (64-bit) diff --git a/inst/web/vignettes/mkin.pdf b/inst/web/vignettes/mkin.pdf Binary files differindex b07cdc72..4e3863d6 100644 --- a/inst/web/vignettes/mkin.pdf +++ b/inst/web/vignettes/mkin.pdf |