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| author | Johannes Ranke <jranke@uni-bremen.de> | 2022-03-02 18:03:54 +0100 |
|---|---|---|
| committer | Johannes Ranke <jranke@uni-bremen.de> | 2022-03-02 18:03:54 +0100 |
| commit | 2bb59c88d49b193f278916ad9cc4de83c0de9604 (patch) | |
| tree | f286bba254d7c5f2d32eeb2b11f6148938b0e1e1 /docs/reference/loftest.html | |
| parent | 351248d07f810ccb6c497633a02cd48ee35526e6 (diff) | |
Make tests more platform independent, update docs
Diffstat (limited to 'docs/reference/loftest.html')
| -rw-r--r-- | docs/reference/loftest.html | 483 |
1 files changed, 217 insertions, 266 deletions
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charset=UTF-8"><meta charset="utf-8"><meta http-equiv="X-UA-Compatible" content="IE=edge"><meta name="viewport" content="width=device-width, initial-scale=1.0"><title>Lack-of-fit test for models fitted to data with replicates — loftest • mkin</title><!-- jquery --><script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/3.4.1/jquery.min.js" integrity="sha256-CSXorXvZcTkaix6Yvo6HppcZGetbYMGWSFlBw8HfCJo=" crossorigin="anonymous"></script><!-- Bootstrap --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/css/bootstrap.min.css" integrity="sha256-bZLfwXAP04zRMK2BjiO8iu9pf4FbLqX6zitd+tIvLhE=" crossorigin="anonymous"><script src="https://cdnjs.cloudflare.com/ajax/libs/twitter-bootstrap/3.4.1/js/bootstrap.min.js" integrity="sha256-nuL8/2cJ5NDSSwnKD8VqreErSWHtnEP9E7AySL+1ev4=" crossorigin="anonymous"></script><!-- bootstrap-toc --><link rel="stylesheet" href="../bootstrap-toc.css"><script src="../bootstrap-toc.js"></script><!-- Font Awesome icons --><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/all.min.css" integrity="sha256-mmgLkCYLUQbXn0B1SRqzHar6dCnv9oZFPEC1g1cwlkk=" crossorigin="anonymous"><link rel="stylesheet" href="https://cdnjs.cloudflare.com/ajax/libs/font-awesome/5.12.1/css/v4-shims.min.css" integrity="sha256-wZjR52fzng1pJHwx4aV2AO3yyTOXrcDW7jBpJtTwVxw=" crossorigin="anonymous"><!-- clipboard.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/clipboard.js/2.0.6/clipboard.min.js" integrity="sha256-inc5kl9MA1hkeYUt+EC3BhlIgyp/2jDIyBLS6k3UxPI=" crossorigin="anonymous"></script><!-- headroom.js --><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/headroom.min.js" integrity="sha256-AsUX4SJE1+yuDu5+mAVzJbuYNPHj/WroHuZ8Ir/CkE0=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/headroom/0.11.0/jQuery.headroom.min.js" integrity="sha256-ZX/yNShbjqsohH1k95liqY9Gd8uOiE1S4vZc+9KQ1K4=" crossorigin="anonymous"></script><!-- pkgdown --><link href="../pkgdown.css" rel="stylesheet"><script src="../pkgdown.js"></script><meta property="og:title" content="Lack-of-fit test for models fitted to data with replicates — loftest"><meta property="og:description" content="This is a generic function with a method currently only defined for mkinfit objects. It fits an anova model to the data contained in the object and compares the likelihoods using the likelihood ratio test -lrtest.default from the lmtest package." /> - - - - -<!-- mathjax --> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script> -<script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script> - -<!--[if lt IE 9]> +lrtest.default from the lmtest package."><!-- mathjax --><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/MathJax.js" integrity="sha256-nvJJv9wWKEm88qvoQl9ekL2J+k/RWIsaSScxxlsrv8k=" crossorigin="anonymous"></script><script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.5/config/TeX-AMS-MML_HTMLorMML.js" integrity="sha256-84DKXVJXs0/F8OTMzX4UR909+jtl4G7SPypPavF+GfA=" crossorigin="anonymous"></script><!--[if lt IE 9]> <script src="https://oss.maxcdn.com/html5shiv/3.7.3/html5shiv.min.js"></script> <script src="https://oss.maxcdn.com/respond/1.4.2/respond.min.js"></script> -<![endif]--> +<![endif]--></head><body data-spy="scroll" data-target="#toc"> + - - - </head> - - <body data-spy="scroll" data-target="#toc"> <div class="container template-reference-topic"> - <header> - <div class="navbar navbar-default navbar-fixed-top" role="navigation"> + <header><div class="navbar navbar-default navbar-fixed-top" role="navigation"> <div class="container"> <div class="navbar-header"> <button type="button" class="navbar-toggle collapsed" data-toggle="collapse" data-target="#navbar" aria-expanded="false"> @@ -75,13 +20,12 @@ lrtest.default from the lmtest package." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.0.3</span> + <span class="version label label-default" data-toggle="tooltip" data-placement="bottom" title="Released version">1.1.0</span> </span> </div> <div id="navbar" class="navbar-collapse collapse"> - <ul class="nav navbar-nav"> - <li> + <ul class="nav navbar-nav"><li> <a href="../reference/index.html">Functions and data</a> </li> <li class="dropdown"> @@ -90,8 +34,7 @@ lrtest.default from the lmtest package." /> <span class="caret"></span> </a> - <ul class="dropdown-menu" role="menu"> - <li> + <ul class="dropdown-menu" role="menu"><li> <a href="../articles/mkin.html">Introduction to mkin</a> </li> <li> @@ -101,6 +44,9 @@ lrtest.default from the lmtest package." /> <a href="../articles/FOCUS_L.html">Example evaluation of FOCUS Laboratory Data L1 to L3</a> </li> <li> + <a href="../articles/web_only/dimethenamid_2018.html">Example evaluations of dimethenamid data from 2018 with nonlinear mixed-effects models</a> + </li> + <li> <a href="../articles/web_only/FOCUS_Z.html">Example evaluation of FOCUS Example Dataset Z</a> </li> <li> @@ -115,34 +61,27 @@ lrtest.default from the lmtest package." /> <li> <a href="../articles/web_only/benchmarks.html">Some benchmark timings</a> </li> - </ul> -</li> + </ul></li> <li> <a href="../news/index.html">News</a> </li> - </ul> - <ul class="nav navbar-nav navbar-right"> - <li> - <a href="https://github.com/jranke/mkin/"> + </ul><ul class="nav navbar-nav navbar-right"><li> + <a href="https://github.com/jranke/mkin/" class="external-link"> <span class="fab fa-github fa-lg"></span> </a> </li> - </ul> - - </div><!--/.nav-collapse --> + </ul></div><!--/.nav-collapse --> </div><!--/.container --> </div><!--/.navbar --> - </header> - -<div class="row"> + </header><div class="row"> <div class="col-md-9 contents"> <div class="page-header"> <h1>Lack-of-fit test for models fitted to data with replicates</h1> - <small class="dont-index">Source: <a href='https://github.com/jranke/mkin/blob/master/R/loftest.R'><code>R/loftest.R</code></a></small> + <small class="dont-index">Source: <a href="https://github.com/jranke/mkin/blob/HEAD/R/loftest.R" class="external-link"><code>R/loftest.R</code></a></small> <div class="hidden name"><code>loftest.Rd</code></div> </div> @@ -150,216 +89,228 @@ lrtest.default from the lmtest package." /> <p>This is a generic function with a method currently only defined for mkinfit objects. It fits an anova model to the data contained in the object and compares the likelihoods using the likelihood ratio test -<code><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest.default</a></code> from the lmtest package.</p> +<code><a href="https://rdrr.io/pkg/lmtest/man/lrtest.html" class="external-link">lrtest.default</a></code> from the lmtest package.</p> </div> - <pre class="usage"><span class='fu'>loftest</span><span class='op'>(</span><span class='va'>object</span>, <span class='va'>...</span><span class='op'>)</span> + <div id="ref-usage"> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="fu">loftest</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span> -<span class='co'># S3 method for mkinfit</span> -<span class='fu'>loftest</span><span class='op'>(</span><span class='va'>object</span>, <span class='va'>...</span><span class='op'>)</span></pre> - - <h2 class="hasAnchor" id="arguments"><a class="anchor" href="#arguments"></a>Arguments</h2> - <table class="ref-arguments"> - <colgroup><col class="name" /><col class="desc" /></colgroup> - <tr> - <th>object</th> - <td><p>A model object with a defined loftest method</p></td> - </tr> - <tr> - <th>...</th> - <td><p>Not used</p></td> - </tr> - </table> - - <h2 class="hasAnchor" id="details"><a class="anchor" href="#details"></a>Details</h2> +<span class="co"># S3 method for mkinfit</span> +<span class="fu">loftest</span><span class="op">(</span><span class="va">object</span>, <span class="va">...</span><span class="op">)</span></code></pre></div> + </div> + <div id="arguments"> + <h2>Arguments</h2> + <dl><dt>object</dt> +<dd><p>A model object with a defined loftest method</p></dd> +<dt>...</dt> +<dd><p>Not used</p></dd> +</dl></div> + <div id="details"> + <h2>Details</h2> <p>The anova model is interpreted as the simplest form of an mkinfit model, assuming only a constant variance about the means, but not enforcing any structure of the means, so we have one model parameter for every mean of replicate samples.</p> - <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2> - - <div class='dont-index'><p>lrtest</p></div> + </div> + <div id="see-also"> + <h2>See also</h2> + <div class="dont-index"><p>lrtest</p></div> + </div> - <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> - <pre class="examples"><div class='input'><span class='co'># \dontrun{</span> -<span class='va'>test_data</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span><span class='op'>(</span><span class='va'>synthetic_data_for_UBA_2014</span><span class='op'>[[</span><span class='fl'>12</span><span class='op'>]</span><span class='op'>]</span><span class='op'>$</span><span class='va'>data</span>, <span class='va'>name</span> <span class='op'>==</span> <span class='st'>"parent"</span><span class='op'>)</span> -<span class='va'>sfo_fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='st'>"SFO"</span>, <span class='va'>test_data</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='plot.mkinfit.html'>plot_res</a></span><span class='op'>(</span><span class='va'>sfo_fit</span><span class='op'>)</span> <span class='co'># We see a clear pattern in the residuals</span> -</div><div class='img'><img src='loftest-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>loftest</span><span class='op'>(</span><span class='va'>sfo_fit</span><span class='op'>)</span> <span class='co'># We have a clear lack of fit</span> -</div><div class='output co'>#> Likelihood ratio test -#> -#> Model 1: ANOVA with error model const -#> Model 2: SFO with error model const -#> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 10 -40.710 -#> 2 3 -63.954 -7 46.487 7.027e-08 *** -#> --- -#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</div><div class='input'><span class='co'>#</span> -<span class='co'># We try a different model (the one that was used to generate the data)</span> -<span class='va'>dfop_fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='st'>"DFOP"</span>, <span class='va'>test_data</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='plot.mkinfit.html'>plot_res</a></span><span class='op'>(</span><span class='va'>dfop_fit</span><span class='op'>)</span> <span class='co'># We don't see systematic deviations, but heteroscedastic residuals</span> -</div><div class='img'><img src='loftest-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># therefore we should consider adapting the error model, although we have</span> -<span class='fu'>loftest</span><span class='op'>(</span><span class='va'>dfop_fit</span><span class='op'>)</span> <span class='co'># no lack of fit</span> -</div><div class='output co'>#> Likelihood ratio test -#> -#> Model 1: ANOVA with error model const -#> Model 2: DFOP with error model const -#> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 10 -40.710 -#> 2 5 -42.453 -5 3.485 0.6257</div><div class='input'><span class='co'>#</span> -<span class='co'># This is the anova model used internally for the comparison</span> -<span class='va'>test_data_anova</span> <span class='op'><-</span> <span class='va'>test_data</span> -<span class='va'>test_data_anova</span><span class='op'>$</span><span class='va'>time</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/factor.html'>as.factor</a></span><span class='op'>(</span><span class='va'>test_data_anova</span><span class='op'>$</span><span class='va'>time</span><span class='op'>)</span> -<span class='va'>anova_fit</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/lm.html'>lm</a></span><span class='op'>(</span><span class='va'>value</span> <span class='op'>~</span> <span class='va'>time</span>, data <span class='op'>=</span> <span class='va'>test_data_anova</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>anova_fit</span><span class='op'>)</span> -</div><div class='output co'>#> -#> Call: -#> lm(formula = value ~ time, data = test_data_anova) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -6.1000 -0.5625 0.0000 0.5625 6.1000 -#> -#> Coefficients: -#> Estimate Std. Error t value Pr(>|t|) -#> (Intercept) 103.150 2.323 44.409 7.44e-12 *** -#> time1 -19.950 3.285 -6.073 0.000185 *** -#> time3 -50.800 3.285 -15.465 8.65e-08 *** -#> time7 -68.500 3.285 -20.854 6.28e-09 *** -#> time14 -79.750 3.285 -24.278 1.63e-09 *** -#> time28 -86.000 3.285 -26.181 8.35e-10 *** -#> time60 -94.900 3.285 -28.891 3.48e-10 *** -#> time90 -98.500 3.285 -29.986 2.49e-10 *** -#> time120 -100.450 3.285 -30.580 2.09e-10 *** -#> --- -#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 -#> -#> Residual standard error: 3.285 on 9 degrees of freedom -#> Multiple R-squared: 0.9953, Adjusted R-squared: 0.9912 -#> F-statistic: 240.5 on 8 and 9 DF, p-value: 1.417e-09 -#> </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/logLik.html'>logLik</a></span><span class='op'>(</span><span class='va'>anova_fit</span><span class='op'>)</span> <span class='co'># We get the same likelihood and degrees of freedom</span> -</div><div class='output co'>#> 'log Lik.' -40.71015 (df=10)</div><div class='input'><span class='co'>#</span> -<span class='va'>test_data_2</span> <span class='op'><-</span> <span class='va'>synthetic_data_for_UBA_2014</span><span class='op'>[[</span><span class='fl'>12</span><span class='op'>]</span><span class='op'>]</span><span class='op'>$</span><span class='va'>data</span> -<span class='va'>m_synth_SFO_lin</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"M1"</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='st'>"M2"</span><span class='op'>)</span>, - M2 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>sfo_lin_fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>m_synth_SFO_lin</span>, <span class='va'>test_data_2</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='plot.mkinfit.html'>plot_res</a></span><span class='op'>(</span><span class='va'>sfo_lin_fit</span><span class='op'>)</span> <span class='co'># not a good model, we try parallel formation</span> -</div><div class='img'><img src='loftest-3.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>loftest</span><span class='op'>(</span><span class='va'>sfo_lin_fit</span><span class='op'>)</span> -</div><div class='output co'>#> Likelihood ratio test -#> -#> Model 1: ANOVA with error model const -#> Model 2: m_synth_SFO_lin with error model const and fixed parameter(s) M1_0, M2_0 -#> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 28 -93.606 -#> 2 7 -171.927 -21 156.64 < 2.2e-16 *** -#> --- -#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</div><div class='input'><span class='co'>#</span> -<span class='va'>m_synth_SFO_par</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span>, to <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"M1"</span>, <span class='st'>"M2"</span><span class='op'>)</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, - M2 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>sfo_par_fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>m_synth_SFO_par</span>, <span class='va'>test_data_2</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='plot.mkinfit.html'>plot_res</a></span><span class='op'>(</span><span class='va'>sfo_par_fit</span><span class='op'>)</span> <span class='co'># much better for metabolites</span> -</div><div class='img'><img src='loftest-4.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>loftest</span><span class='op'>(</span><span class='va'>sfo_par_fit</span><span class='op'>)</span> -</div><div class='output co'>#> Likelihood ratio test -#> -#> Model 1: ANOVA with error model const -#> Model 2: m_synth_SFO_par with error model const and fixed parameter(s) M1_0, M2_0 -#> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 28 -93.606 -#> 2 7 -156.331 -21 125.45 < 2.2e-16 *** -#> --- -#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</div><div class='input'><span class='co'>#</span> -<span class='va'>m_synth_DFOP_par</span> <span class='op'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span><span class='op'>(</span>parent <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"DFOP"</span>, to <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span><span class='op'>(</span><span class='st'>"M1"</span>, <span class='st'>"M2"</span><span class='op'>)</span><span class='op'>)</span>, - M1 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, - M2 <span class='op'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span><span class='op'>(</span>type <span class='op'>=</span> <span class='st'>"SFO"</span><span class='op'>)</span>, use_of_ff <span class='op'>=</span> <span class='st'>"max"</span><span class='op'>)</span> -</div><div class='output co'>#> <span class='message'>Temporary DLL for differentials generated and loaded</span></div><div class='input'><span class='va'>dfop_par_fit</span> <span class='op'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span><span class='op'>(</span><span class='va'>m_synth_DFOP_par</span>, <span class='va'>test_data_2</span>, quiet <span class='op'>=</span> <span class='cn'>TRUE</span><span class='op'>)</span> -<span class='fu'><a href='plot.mkinfit.html'>plot_res</a></span><span class='op'>(</span><span class='va'>dfop_par_fit</span><span class='op'>)</span> <span class='co'># No visual lack of fit</span> -</div><div class='img'><img src='loftest-5.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>loftest</span><span class='op'>(</span><span class='va'>dfop_par_fit</span><span class='op'>)</span> <span class='co'># no lack of fit found by the test</span> -</div><div class='output co'>#> Likelihood ratio test -#> -#> Model 1: ANOVA with error model const -#> Model 2: m_synth_DFOP_par with error model const and fixed parameter(s) M1_0, M2_0 -#> #Df LogLik Df Chisq Pr(>Chisq) -#> 1 28 -93.606 -#> 2 9 -102.763 -19 18.313 0.5016</div><div class='input'><span class='co'>#</span> -<span class='co'># The anova model used for comparison in the case of transformation products</span> -<span class='va'>test_data_anova_2</span> <span class='op'><-</span> <span class='va'>dfop_par_fit</span><span class='op'>$</span><span class='va'>data</span> -<span class='va'>test_data_anova_2</span><span class='op'>$</span><span class='va'>variable</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/factor.html'>as.factor</a></span><span class='op'>(</span><span class='va'>test_data_anova_2</span><span class='op'>$</span><span class='va'>variable</span><span class='op'>)</span> -<span class='va'>test_data_anova_2</span><span class='op'>$</span><span class='va'>time</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/factor.html'>as.factor</a></span><span class='op'>(</span><span class='va'>test_data_anova_2</span><span class='op'>$</span><span class='va'>time</span><span class='op'>)</span> -<span class='va'>anova_fit_2</span> <span class='op'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/lm.html'>lm</a></span><span class='op'>(</span><span class='va'>observed</span> <span class='op'>~</span> <span class='va'>time</span><span class='op'>:</span><span class='va'>variable</span> <span class='op'>-</span> <span class='fl'>1</span>, data <span class='op'>=</span> <span class='va'>test_data_anova_2</span><span class='op'>)</span> -<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span><span class='op'>(</span><span class='va'>anova_fit_2</span><span class='op'>)</span> -</div><div class='output co'>#> -#> Call: -#> lm(formula = observed ~ time:variable - 1, data = test_data_anova_2) -#> -#> Residuals: -#> Min 1Q Median 3Q Max -#> -6.1000 -0.5875 0.0000 0.5875 6.1000 -#> -#> Coefficients: (2 not defined because of singularities) -#> Estimate Std. Error t value Pr(>|t|) -#> time0:variableparent 103.150 1.573 65.562 < 2e-16 *** -#> time1:variableparent 83.200 1.573 52.882 < 2e-16 *** -#> time3:variableparent 52.350 1.573 33.274 < 2e-16 *** -#> time7:variableparent 34.650 1.573 22.024 < 2e-16 *** -#> time14:variableparent 23.400 1.573 14.873 6.35e-14 *** -#> time28:variableparent 17.150 1.573 10.901 5.47e-11 *** -#> time60:variableparent 8.250 1.573 5.244 1.99e-05 *** -#> time90:variableparent 4.650 1.573 2.956 0.006717 ** -#> time120:variableparent 2.700 1.573 1.716 0.098507 . -#> time0:variableM1 NA NA NA NA -#> time1:variableM1 11.850 1.573 7.532 6.93e-08 *** -#> time3:variableM1 22.700 1.573 14.428 1.26e-13 *** -#> time7:variableM1 33.050 1.573 21.007 < 2e-16 *** -#> time14:variableM1 31.250 1.573 19.863 < 2e-16 *** -#> time28:variableM1 18.900 1.573 12.013 7.02e-12 *** -#> time60:variableM1 7.550 1.573 4.799 6.28e-05 *** -#> time90:variableM1 3.850 1.573 2.447 0.021772 * -#> time120:variableM1 2.050 1.573 1.303 0.204454 -#> time0:variableM2 NA NA NA NA -#> time1:variableM2 6.700 1.573 4.259 0.000254 *** -#> time3:variableM2 16.750 1.573 10.646 8.93e-11 *** -#> time7:variableM2 25.800 1.573 16.399 6.89e-15 *** -#> time14:variableM2 28.600 1.573 18.178 6.35e-16 *** -#> time28:variableM2 25.400 1.573 16.144 9.85e-15 *** -#> time60:variableM2 21.600 1.573 13.729 3.81e-13 *** -#> time90:variableM2 17.800 1.573 11.314 2.51e-11 *** -#> time120:variableM2 14.100 1.573 8.962 2.79e-09 *** -#> --- -#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 -#> -#> Residual standard error: 2.225 on 25 degrees of freedom -#> Multiple R-squared: 0.9979, Adjusted R-squared: 0.9957 -#> F-statistic: 469.2 on 25 and 25 DF, p-value: < 2.2e-16 -#> </div><div class='input'><span class='co'># }</span> -</div></pre> + <div id="ref-examples"> + <h2>Examples</h2> + <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span class="co"># \dontrun{</span></span> +<span class="r-in"><span class="va">test_data</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/subset.html" class="external-link">subset</a></span><span class="op">(</span><span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span>, <span class="va">name</span> <span class="op">==</span> <span class="st">"parent"</span><span class="op">)</span></span> +<span class="r-in"><span class="va">sfo_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"SFO"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_fit</span><span class="op">)</span> <span class="co"># We see a clear pattern in the residuals</span></span> +<span class="r-plt img"><img src="loftest-1.png" alt="" width="700" height="433"></span> +<span class="r-in"><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_fit</span><span class="op">)</span> <span class="co"># We have a clear lack of fit</span></span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> Model 2: SFO with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq) </span> +<span class="r-out co"><span class="r-pr">#></span> 1 10 -40.710 </span> +<span class="r-out co"><span class="r-pr">#></span> 2 3 -63.954 -7 46.487 7.027e-08 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-in"><span class="co">#</span></span> +<span class="r-in"><span class="co"># We try a different model (the one that was used to generate the data)</span></span> +<span class="r-in"><span class="va">dfop_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="st">"DFOP"</span>, <span class="va">test_data</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">dfop_fit</span><span class="op">)</span> <span class="co"># We don't see systematic deviations, but heteroscedastic residuals</span></span> +<span class="r-plt img"><img src="loftest-2.png" alt="" width="700" height="433"></span> +<span class="r-in"><span class="co"># therefore we should consider adapting the error model, although we have</span></span> +<span class="r-in"><span class="fu">loftest</span><span class="op">(</span><span class="va">dfop_fit</span><span class="op">)</span> <span class="co"># no lack of fit</span></span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> Model 2: DFOP with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq)</span> +<span class="r-out co"><span class="r-pr">#></span> 1 10 -40.710 </span> +<span class="r-out co"><span class="r-pr">#></span> 2 5 -42.453 -5 3.485 0.6257</span> +<span class="r-in"><span class="co">#</span></span> +<span class="r-in"><span class="co"># This is the anova model used internally for the comparison</span></span> +<span class="r-in"><span class="va">test_data_anova</span> <span class="op"><-</span> <span class="va">test_data</span></span> +<span class="r-in"><span class="va">test_data_anova</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova</span><span class="op">$</span><span class="va">time</span><span class="op">)</span></span> +<span class="r-in"><span class="va">anova_fit</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm</a></span><span class="op">(</span><span class="va">value</span> <span class="op">~</span> <span class="va">time</span>, data <span class="op">=</span> <span class="va">test_data_anova</span><span class="op">)</span></span> +<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">anova_fit</span><span class="op">)</span></span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Call:</span> +<span class="r-out co"><span class="r-pr">#></span> lm(formula = value ~ time, data = test_data_anova)</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Residuals:</span> +<span class="r-out co"><span class="r-pr">#></span> Min 1Q Median 3Q Max </span> +<span class="r-out co"><span class="r-pr">#></span> -6.1000 -0.5625 0.0000 0.5625 6.1000 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Coefficients:</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Std. Error t value Pr(>|t|) </span> +<span class="r-out co"><span class="r-pr">#></span> (Intercept) 103.150 2.323 44.409 7.44e-12 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time1 -19.950 3.285 -6.073 0.000185 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time3 -50.800 3.285 -15.465 8.65e-08 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time7 -68.500 3.285 -20.854 6.28e-09 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time14 -79.750 3.285 -24.278 1.63e-09 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time28 -86.000 3.285 -26.181 8.35e-10 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time60 -94.900 3.285 -28.891 3.48e-10 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time90 -98.500 3.285 -29.986 2.49e-10 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time120 -100.450 3.285 -30.580 2.09e-10 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Residual standard error: 3.285 on 9 degrees of freedom</span> +<span class="r-out co"><span class="r-pr">#></span> Multiple R-squared: 0.9953, Adjusted R-squared: 0.9912 </span> +<span class="r-out co"><span class="r-pr">#></span> F-statistic: 240.5 on 8 and 9 DF, p-value: 1.417e-09</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/stats/logLik.html" class="external-link">logLik</a></span><span class="op">(</span><span class="va">anova_fit</span><span class="op">)</span> <span class="co"># We get the same likelihood and degrees of freedom</span></span> +<span class="r-out co"><span class="r-pr">#></span> 'log Lik.' -40.71015 (df=10)</span> +<span class="r-in"><span class="co">#</span></span> +<span class="r-in"><span class="va">test_data_2</span> <span class="op"><-</span> <span class="va">synthetic_data_for_UBA_2014</span><span class="op">[[</span><span class="fl">12</span><span class="op">]</span><span class="op">]</span><span class="op">$</span><span class="va">data</span></span> +<span class="r-in"><span class="va">m_synth_SFO_lin</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M1"</span><span class="op">)</span>,</span> +<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="st">"M2"</span><span class="op">)</span>,</span> +<span class="r-in"> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span class="va">sfo_lin_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_SFO_lin</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_lin_fit</span><span class="op">)</span> <span class="co"># not a good model, we try parallel formation</span></span> +<span class="r-plt img"><img src="loftest-3.png" alt="" width="700" height="433"></span> +<span class="r-in"><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_lin_fit</span><span class="op">)</span></span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> Model 2: m_synth_SFO_lin with error model const and fixed parameter(s) M1_0, M2_0</span> +<span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq) </span> +<span class="r-out co"><span class="r-pr">#></span> 1 28 -93.606 </span> +<span class="r-out co"><span class="r-pr">#></span> 2 7 -171.927 -21 156.64 < 2.2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-in"><span class="co">#</span></span> +<span class="r-in"><span class="va">m_synth_SFO_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span> +<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span> +<span class="r-in"> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span class="va">sfo_par_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_SFO_par</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">sfo_par_fit</span><span class="op">)</span> <span class="co"># much better for metabolites</span></span> +<span class="r-plt img"><img src="loftest-4.png" alt="" width="700" height="433"></span> +<span class="r-in"><span class="fu">loftest</span><span class="op">(</span><span class="va">sfo_par_fit</span><span class="op">)</span></span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> Model 2: m_synth_SFO_par with error model const and fixed parameter(s) M1_0, M2_0</span> +<span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq) </span> +<span class="r-out co"><span class="r-pr">#></span> 1 28 -93.606 </span> +<span class="r-out co"><span class="r-pr">#></span> 2 7 -156.331 -21 125.45 < 2.2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-in"><span class="co">#</span></span> +<span class="r-in"><span class="va">m_synth_DFOP_par</span> <span class="op"><-</span> <span class="fu"><a href="mkinmod.html">mkinmod</a></span><span class="op">(</span>parent <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"DFOP"</span>, to <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html" class="external-link">c</a></span><span class="op">(</span><span class="st">"M1"</span>, <span class="st">"M2"</span><span class="op">)</span><span class="op">)</span>,</span> +<span class="r-in"> M1 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>,</span> +<span class="r-in"> M2 <span class="op">=</span> <span class="fu"><a href="https://rdrr.io/r/base/list.html" class="external-link">list</a></span><span class="op">(</span>type <span class="op">=</span> <span class="st">"SFO"</span><span class="op">)</span>, use_of_ff <span class="op">=</span> <span class="st">"max"</span><span class="op">)</span></span> +<span class="r-msg co"><span class="r-pr">#></span> Temporary DLL for differentials generated and loaded</span> +<span class="r-in"><span class="va">dfop_par_fit</span> <span class="op"><-</span> <span class="fu"><a href="mkinfit.html">mkinfit</a></span><span class="op">(</span><span class="va">m_synth_DFOP_par</span>, <span class="va">test_data_2</span>, quiet <span class="op">=</span> <span class="cn">TRUE</span><span class="op">)</span></span> +<span class="r-in"><span class="fu"><a href="plot.mkinfit.html">plot_res</a></span><span class="op">(</span><span class="va">dfop_par_fit</span><span class="op">)</span> <span class="co"># No visual lack of fit</span></span> +<span class="r-plt img"><img src="loftest-5.png" alt="" width="700" height="433"></span> +<span class="r-in"><span class="fu">loftest</span><span class="op">(</span><span class="va">dfop_par_fit</span><span class="op">)</span> <span class="co"># no lack of fit found by the test</span></span> +<span class="r-out co"><span class="r-pr">#></span> Likelihood ratio test</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Model 1: ANOVA with error model const</span> +<span class="r-out co"><span class="r-pr">#></span> Model 2: m_synth_DFOP_par with error model const and fixed parameter(s) M1_0, M2_0</span> +<span class="r-out co"><span class="r-pr">#></span> #Df LogLik Df Chisq Pr(>Chisq)</span> +<span class="r-out co"><span class="r-pr">#></span> 1 28 -93.606 </span> +<span class="r-out co"><span class="r-pr">#></span> 2 9 -102.763 -19 18.313 0.5016</span> +<span class="r-in"><span class="co">#</span></span> +<span class="r-in"><span class="co"># The anova model used for comparison in the case of transformation products</span></span> +<span class="r-in"><span class="va">test_data_anova_2</span> <span class="op"><-</span> <span class="va">dfop_par_fit</span><span class="op">$</span><span class="va">data</span></span> +<span class="r-in"><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">variable</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">variable</span><span class="op">)</span></span> +<span class="r-in"><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">time</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/factor.html" class="external-link">as.factor</a></span><span class="op">(</span><span class="va">test_data_anova_2</span><span class="op">$</span><span class="va">time</span><span class="op">)</span></span> +<span class="r-in"><span class="va">anova_fit_2</span> <span class="op"><-</span> <span class="fu"><a href="https://rdrr.io/r/stats/lm.html" class="external-link">lm</a></span><span class="op">(</span><span class="va">observed</span> <span class="op">~</span> <span class="va">time</span><span class="op">:</span><span class="va">variable</span> <span class="op">-</span> <span class="fl">1</span>, data <span class="op">=</span> <span class="va">test_data_anova_2</span><span class="op">)</span></span> +<span class="r-in"><span class="fu"><a href="https://rdrr.io/r/base/summary.html" class="external-link">summary</a></span><span class="op">(</span><span class="va">anova_fit_2</span><span class="op">)</span></span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Call:</span> +<span class="r-out co"><span class="r-pr">#></span> lm(formula = observed ~ time:variable - 1, data = test_data_anova_2)</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Residuals:</span> +<span class="r-out co"><span class="r-pr">#></span> Min 1Q Median 3Q Max </span> +<span class="r-out co"><span class="r-pr">#></span> -6.1000 -0.5875 0.0000 0.5875 6.1000 </span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Coefficients: (2 not defined because of singularities)</span> +<span class="r-out co"><span class="r-pr">#></span> Estimate Std. Error t value Pr(>|t|) </span> +<span class="r-out co"><span class="r-pr">#></span> time0:variableparent 103.150 1.573 65.562 < 2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time1:variableparent 83.200 1.573 52.882 < 2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time3:variableparent 52.350 1.573 33.274 < 2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time7:variableparent 34.650 1.573 22.024 < 2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time14:variableparent 23.400 1.573 14.873 6.35e-14 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time28:variableparent 17.150 1.573 10.901 5.47e-11 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time60:variableparent 8.250 1.573 5.244 1.99e-05 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time90:variableparent 4.650 1.573 2.956 0.006717 ** </span> +<span class="r-out co"><span class="r-pr">#></span> time120:variableparent 2.700 1.573 1.716 0.098507 . </span> +<span class="r-out co"><span class="r-pr">#></span> time0:variableM1 NA NA NA NA </span> +<span class="r-out co"><span class="r-pr">#></span> time1:variableM1 11.850 1.573 7.532 6.93e-08 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time3:variableM1 22.700 1.573 14.428 1.26e-13 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time7:variableM1 33.050 1.573 21.007 < 2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time14:variableM1 31.250 1.573 19.863 < 2e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time28:variableM1 18.900 1.573 12.013 7.02e-12 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time60:variableM1 7.550 1.573 4.799 6.28e-05 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time90:variableM1 3.850 1.573 2.447 0.021772 * </span> +<span class="r-out co"><span class="r-pr">#></span> time120:variableM1 2.050 1.573 1.303 0.204454 </span> +<span class="r-out co"><span class="r-pr">#></span> time0:variableM2 NA NA NA NA </span> +<span class="r-out co"><span class="r-pr">#></span> time1:variableM2 6.700 1.573 4.259 0.000254 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time3:variableM2 16.750 1.573 10.646 8.93e-11 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time7:variableM2 25.800 1.573 16.399 6.89e-15 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time14:variableM2 28.600 1.573 18.178 6.35e-16 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time28:variableM2 25.400 1.573 16.144 9.85e-15 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time60:variableM2 21.600 1.573 13.729 3.81e-13 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time90:variableM2 17.800 1.573 11.314 2.51e-11 ***</span> +<span class="r-out co"><span class="r-pr">#></span> time120:variableM2 14.100 1.573 8.962 2.79e-09 ***</span> +<span class="r-out co"><span class="r-pr">#></span> ---</span> +<span class="r-out co"><span class="r-pr">#></span> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-out co"><span class="r-pr">#></span> Residual standard error: 2.225 on 25 degrees of freedom</span> +<span class="r-out co"><span class="r-pr">#></span> Multiple R-squared: 0.9979, Adjusted R-squared: 0.9957 </span> +<span class="r-out co"><span class="r-pr">#></span> F-statistic: 469.2 on 25 and 25 DF, p-value: < 2.2e-16</span> +<span class="r-out co"><span class="r-pr">#></span> </span> +<span class="r-in"><span class="co"># }</span></span> +</code></pre></div> + </div> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> - <nav id="toc" data-toggle="toc" class="sticky-top"> - <h2 data-toc-skip>Contents</h2> - </nav> - </div> + <nav id="toc" data-toggle="toc" class="sticky-top"><h2 data-toc-skip>Contents</h2> + </nav></div> </div> - <footer> - <div class="copyright"> - <p>Developed by Johannes Ranke.</p> + <footer><div class="copyright"> + <p></p><p>Developed by Johannes Ranke.</p> </div> <div class="pkgdown"> - <p>Site built with <a href="https://pkgdown.r-lib.org/">pkgdown</a> 1.6.1.</p> + <p></p><p>Site built with <a href="https://pkgdown.r-lib.org/" class="external-link">pkgdown</a> 2.0.2.</p> </div> - </footer> - </div> + </footer></div> - </body> -</html> + + </body></html> |