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author | Johannes Ranke <jranke@uni-bremen.de> | 2022-11-14 21:47:45 +0100 |
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committer | Johannes Ranke <jranke@uni-bremen.de> | 2022-11-14 21:47:45 +0100 |
commit | 9275bcb39b5ee25753ef489d334b4906401970b3 (patch) | |
tree | 9ddaaf518f8655f38a44f1a0516335989085d490 /docs/dev/reference/mhmkin.html | |
parent | d6168b0851a52adeaed8099e41a960d79acedae9 (diff) |
Update online docs
Diffstat (limited to 'docs/dev/reference/mhmkin.html')
-rw-r--r-- | docs/dev/reference/mhmkin.html | 24 |
1 files changed, 20 insertions, 4 deletions
diff --git a/docs/dev/reference/mhmkin.html b/docs/dev/reference/mhmkin.html index cbfeae60..e87e20a1 100644 --- a/docs/dev/reference/mhmkin.html +++ b/docs/dev/reference/mhmkin.html @@ -99,7 +99,7 @@ mixed-effects model fitting functions.</p> </div> <div id="ref-usage"> - <div class="sourceCode"><pre><code>mhmkin(objects, backend = "saemix", algorithm = "saem", ...) + <div class="sourceCode"><pre><code>mhmkin(objects, ...) # S3 method for mmkin mhmkin(objects, ...) @@ -108,6 +108,9 @@ mhmkin(objects, ...) mhmkin( objects, backend = "saemix", + algorithm = "saem", + no_random_effect = NULL, + auto_ranef_threshold = 3, ..., cores = if (Sys.info()["sysname"] == "Windows") 1 else parallel::detectCores(), cluster = NULL @@ -129,6 +132,11 @@ Alternatively, a single <a href="mmkin.html">mmkin</a> object containing fits of degradation models to the same data</p></dd> +<dt>...</dt> +<dd><p>Further arguments that will be passed to the nonlinear mixed-effects +model fitting function.</p></dd> + + <dt>backend</dt> <dd><p>The backend to be used for fitting. Currently, only saemix is supported</p></dd> @@ -138,9 +146,17 @@ supported</p></dd> <dd><p>The algorithm to be used for fitting (currently not used)</p></dd> -<dt>...</dt> -<dd><p>Further arguments that will be passed to the nonlinear mixed-effects -model fitting function.</p></dd> +<dt>no_random_effect</dt> +<dd><p>Default is NULL and will be passed to <a href="saem.html">saem</a>. If +you specify "auto", random effects are only included if the number +of datasets in which the parameter passed the t-test is at least 'auto_ranef_threshold'. +Beware that while this may make for convenient model reduction or even +numerical stability of the algorithm, it will likely lead to +underparameterised models.</p></dd> + + +<dt>auto_ranef_threshold</dt> +<dd><p>See 'no_random_effect.</p></dd> <dt>cores</dt> |