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 save/load fitted models."><!-- 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]>
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   <main id="main" class="col-md-9"><div class="page-header">
       <img src="" class="logo" alt=""><h1>Accelerated Failure Time (AFT) Survival Regression Model</h1>

       <div class="d-none name"><code>spark.survreg.Rd</code></div>
     </div>

     <div class="ref-description section level2">
     <p><code>spark.survreg</code> fits an accelerated failure time (AFT) survival regression model on
 a SparkDataFrame. Users can call <code>summary</code> to get a summary of the fitted AFT model,
 <code>predict</code> to make predictions on new data, and <code>write.ml</code>/<code>read.ml</code> to
 save/load fitted models.</p>
     </div>

     <div class="section level2">
     <h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">spark.survreg</span><span class="op">(</span><span class="va">data</span>, <span class="va">formula</span>, <span class="va">...</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># S4 method for SparkDataFrame,formula</span></span>
 <span><span class="fu">spark.survreg</span><span class="op">(</span></span>
 <span>  <span class="va">data</span>,</span>
 <span>  <span class="va">formula</span>,</span>
 <span>  aggregationDepth <span class="op">=</span> <span class="fl">2</span>,</span>
 <span>  stringIndexerOrderType <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">"frequencyDesc"</span>, <span class="st">"frequencyAsc"</span>, <span class="st">"alphabetDesc"</span>,</span>
 <span>    <span class="st">"alphabetAsc"</span><span class="op">)</span></span>
 <span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># S4 method for AFTSurvivalRegressionModel</span></span>
 <span><span class="fu"><a href="summary.html">summary</a></span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># S4 method for AFTSurvivalRegressionModel</span></span>
 <span><span class="fu"><a href="predict.html">predict</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">newData</span><span class="op">)</span></span>
 <span></span>
 <span><span class="co"># S4 method for AFTSurvivalRegressionModel,character</span></span>
 <span><span class="fu"><a href="write.ml.html">write.ml</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">path</span>, overwrite <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
     </div>

     <div class="section level2">
     <h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
     <dl><dt>data</dt>
 <dd><p>a SparkDataFrame for training.</p></dd>


 <dt>formula</dt>
 <dd><p>a symbolic description of the model to be fitted. Currently only a few formula
 operators are supported, including '~', ':', '+', and '-'.
 Note that operator '.' is not supported currently.</p></dd>


 <dt>...</dt>
 <dd><p>additional arguments passed to the method.</p></dd>


 <dt>aggregationDepth</dt>
 <dd><p>The depth for treeAggregate (greater than or equal to 2). If the
 dimensions of features or the number of partitions are large, this
 param could be adjusted to a larger size. This is an expert parameter.
 Default value should be good for most cases.</p></dd>


 <dt>stringIndexerOrderType</dt>
 <dd><p>how to order categories of a string feature column. This is used to
 decide the base level of a string feature as the last category
 after ordering is dropped when encoding strings. Supported options
 are "frequencyDesc", "frequencyAsc", "alphabetDesc", and
 "alphabetAsc". The default value is "frequencyDesc". When the
 ordering is set to "alphabetDesc", this drops the same category
 as R when encoding strings.</p></dd>


 <dt>object</dt>
 <dd><p>a fitted AFT survival regression model.</p></dd>


 <dt>newData</dt>
 <dd><p>a SparkDataFrame for testing.</p></dd>


 <dt>path</dt>
 <dd><p>the directory where the model is saved.</p></dd>


 <dt>overwrite</dt>
 <dd><p>overwrites or not if the output path already exists. Default is FALSE
 which means throw exception if the output path exists.</p></dd>

 </dl></div>
     <div class="section level2">
     <h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>


 <p><code>spark.survreg</code> returns a fitted AFT survival regression model.</p>


 <p><code>summary</code> returns summary information of the fitted model, which is a list.
         The list includes the model's <code>coefficients</code> (features, coefficients,
         intercept and log(scale)).</p>


 <p><code>predict</code> returns a SparkDataFrame containing predicted values
         on the original scale of the data (mean predicted value at scale = 1.0).</p>
     </div>
     <div class="section level2">
     <h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
     <p>spark.survreg since 2.0.0</p>
 <p>summary(AFTSurvivalRegressionModel) since 2.0.0</p>
 <p>predict(AFTSurvivalRegressionModel) since 2.0.0</p>
 <p>write.ml(AFTSurvivalRegressionModel, character) since 2.0.0</p>
     </div>
     <div class="section level2">
     <h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
     <div class="dont-index"><p>survival: <a href="https://cran.r-project.org/package=survival" class="external-link">https://cran.r-project.org/package=survival</a></p>
 <p><a href="write.ml.html">write.ml</a></p></div>
     </div>

     <div class="section level2">
     <h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
     <div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
 <span class="r-in"><span><span class="va">df</span> <span class="op">&lt;-</span> <span class="fu"><a href="createDataFrame.html">createDataFrame</a></span><span class="op">(</span><span class="va">ovarian</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">model</span> <span class="op">&lt;-</span> <span class="fu">spark.survreg</span><span class="op">(</span><span class="va">df</span>, <span class="fu">Surv</span><span class="op">(</span><span class="va">futime</span>, <span class="va">fustat</span><span class="op">)</span> <span class="op">~</span> <span class="va">ecog_ps</span> <span class="op">+</span> <span class="va">rx</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># get a summary of the model</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># make predictions</span></span></span>
 <span class="r-in"><span><span class="va">predicted</span> <span class="op">&lt;-</span> <span class="fu"><a href="predict.html">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">df</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="showDF.html">showDF</a></span><span class="op">(</span><span class="va">predicted</span><span class="op">)</span></span></span>
 <span class="r-in"><span></span></span>
 <span class="r-in"><span><span class="co"># save and load the model</span></span></span>
 <span class="r-in"><span><span class="va">path</span> <span class="op">&lt;-</span> <span class="st">"path/to/model"</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="write.ml.html">write.ml</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">path</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="va">savedModel</span> <span class="op">&lt;-</span> <span class="fu"><a href="read.ml.html">read.ml</a></span><span class="op">(</span><span class="va">path</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="fu"><a href="summary.html">summary</a></span><span class="op">(</span><span class="va">savedModel</span><span class="op">)</span></span></span>
 <span class="r-in"><span><span class="op">}</span></span></span>
 </code></pre></div>
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	<main id="main" class="col-md-9"><div class="page-header">
	<img src="" class="logo" alt=""><h1>Accelerated Failure Time (AFT) Survival Regression Model</h1>

	<div class="d-none name"><code>spark.survreg.Rd</code></div>
	</div>

	<div class="ref-description section level2">
	<p><code>spark.survreg</code> fits an accelerated failure time (AFT) survival regression model on
	a SparkDataFrame. Users can call <code>summary</code> to get a summary of the fitted AFT model,
	<code>predict</code> to make predictions on new data, and <code>write.ml</code>/<code>read.ml</code> to
	save/load fitted models.</p>
	</div>

	<div class="section level2">
	<h2 id="ref-usage">Usage<a class="anchor" aria-label="anchor" href="#ref-usage"></a></h2>
	<div class="sourceCode"><pre class="sourceCode r"><code><span><span class="fu">spark.survreg</span><span class="op">(</span><span class="va">data</span>, <span class="va">formula</span>, <span class="va">...</span><span class="op">)</span></span>
	<span></span>
	<span><span class="co"># S4 method for SparkDataFrame,formula</span></span>
	<span><span class="fu">spark.survreg</span><span class="op">(</span></span>
	<span> <span class="va">data</span>,</span>
	<span> <span class="va">formula</span>,</span>
	<span> aggregationDepth <span class="op">=</span> <span class="fl">2</span>,</span>
	<span> stringIndexerOrderType <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">"frequencyDesc"</span>, <span class="st">"frequencyAsc"</span>, <span class="st">"alphabetDesc"</span>,</span>
	<span> <span class="st">"alphabetAsc"</span><span class="op">)</span></span>
	<span><span class="op">)</span></span>
	<span></span>
	<span><span class="co"># S4 method for AFTSurvivalRegressionModel</span></span>
	<span><span class="fu"><a href="summary.html">summary</a></span><span class="op">(</span><span class="va">object</span><span class="op">)</span></span>
	<span></span>
	<span><span class="co"># S4 method for AFTSurvivalRegressionModel</span></span>
	<span><span class="fu"><a href="predict.html">predict</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">newData</span><span class="op">)</span></span>
	<span></span>
	<span><span class="co"># S4 method for AFTSurvivalRegressionModel,character</span></span>
	<span><span class="fu"><a href="write.ml.html">write.ml</a></span><span class="op">(</span><span class="va">object</span>, <span class="va">path</span>, overwrite <span class="op">=</span> <span class="cn">FALSE</span><span class="op">)</span></span></code></pre></div>
	</div>

	<div class="section level2">
	<h2 id="arguments">Arguments<a class="anchor" aria-label="anchor" href="#arguments"></a></h2>
	<dl><dt>data</dt>
	<dd><p>a SparkDataFrame for training.</p></dd>


	<dt>formula</dt>
	<dd><p>a symbolic description of the model to be fitted. Currently only a few formula
	operators are supported, including '~', ':', '+', and '-'.
	Note that operator '.' is not supported currently.</p></dd>


	<dt>...</dt>
	<dd><p>additional arguments passed to the method.</p></dd>


	<dt>aggregationDepth</dt>
	<dd><p>The depth for treeAggregate (greater than or equal to 2). If the
	dimensions of features or the number of partitions are large, this
	param could be adjusted to a larger size. This is an expert parameter.
	Default value should be good for most cases.</p></dd>


	<dt>stringIndexerOrderType</dt>
	<dd><p>how to order categories of a string feature column. This is used to
	decide the base level of a string feature as the last category
	after ordering is dropped when encoding strings. Supported options
	are "frequencyDesc", "frequencyAsc", "alphabetDesc", and
	"alphabetAsc". The default value is "frequencyDesc". When the
	ordering is set to "alphabetDesc", this drops the same category
	as R when encoding strings.</p></dd>


	<dt>object</dt>
	<dd><p>a fitted AFT survival regression model.</p></dd>


	<dt>newData</dt>
	<dd><p>a SparkDataFrame for testing.</p></dd>


	<dt>path</dt>
	<dd><p>the directory where the model is saved.</p></dd>


	<dt>overwrite</dt>
	<dd><p>overwrites or not if the output path already exists. Default is FALSE
	which means throw exception if the output path exists.</p></dd>

	</dl></div>
	<div class="section level2">
	<h2 id="value">Value<a class="anchor" aria-label="anchor" href="#value"></a></h2>


	<p><code>spark.survreg</code> returns a fitted AFT survival regression model.</p>


	<p><code>summary</code> returns summary information of the fitted model, which is a list.
	The list includes the model's <code>coefficients</code> (features, coefficients,
	intercept and log(scale)).</p>


	<p><code>predict</code> returns a SparkDataFrame containing predicted values
	on the original scale of the data (mean predicted value at scale = 1.0).</p>
	</div>
	<div class="section level2">
	<h2 id="note">Note<a class="anchor" aria-label="anchor" href="#note"></a></h2>
	<p>spark.survreg since 2.0.0</p>
	<p>summary(AFTSurvivalRegressionModel) since 2.0.0</p>
	<p>predict(AFTSurvivalRegressionModel) since 2.0.0</p>
	<p>write.ml(AFTSurvivalRegressionModel, character) since 2.0.0</p>
	</div>
	<div class="section level2">
	<h2 id="see-also">See also<a class="anchor" aria-label="anchor" href="#see-also"></a></h2>
	<div class="dont-index"><p>survival: <a href="https://cran.r-project.org/package=survival" class="external-link">https://cran.r-project.org/package=survival</a></p>
	<p><a href="write.ml.html">write.ml</a></p></div>
	</div>

	<div class="section level2">
	<h2 id="ref-examples">Examples<a class="anchor" aria-label="anchor" href="#ref-examples"></a></h2>
	<div class="sourceCode"><pre class="sourceCode r"><code><span class="r-in"><span><span class="kw">if</span> <span class="op">(</span><span class="cn">FALSE</span><span class="op">)</span> <span class="op">{</span></span></span>
	<span class="r-in"><span><span class="va">df</span> <span class="op"><-</span> <span class="fu"><a href="createDataFrame.html">createDataFrame</a></span><span class="op">(</span><span class="va">ovarian</span><span class="op">)</span></span></span>
	<span class="r-in"><span><span class="va">model</span> <span class="op"><-</span> <span class="fu">spark.survreg</span><span class="op">(</span><span class="va">df</span>, <span class="fu">Surv</span><span class="op">(</span><span class="va">futime</span>, <span class="va">fustat</span><span class="op">)</span> <span class="op">~</span> <span class="va">ecog_ps</span> <span class="op">+</span> <span class="va">rx</span><span class="op">)</span></span></span>
	<span class="r-in"><span></span></span>
	<span class="r-in"><span><span class="co"># get a summary of the model</span></span></span>
	<span class="r-in"><span><span class="fu"><a href="summary.html">summary</a></span><span class="op">(</span><span class="va">model</span><span class="op">)</span></span></span>
	<span class="r-in"><span></span></span>
	<span class="r-in"><span><span class="co"># make predictions</span></span></span>
	<span class="r-in"><span><span class="va">predicted</span> <span class="op"><-</span> <span class="fu"><a href="predict.html">predict</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">df</span><span class="op">)</span></span></span>
	<span class="r-in"><span><span class="fu"><a href="showDF.html">showDF</a></span><span class="op">(</span><span class="va">predicted</span><span class="op">)</span></span></span>
	<span class="r-in"><span></span></span>
	<span class="r-in"><span><span class="co"># save and load the model</span></span></span>
	<span class="r-in"><span><span class="va">path</span> <span class="op"><-</span> <span class="st">"path/to/model"</span></span></span>
	<span class="r-in"><span><span class="fu"><a href="write.ml.html">write.ml</a></span><span class="op">(</span><span class="va">model</span>, <span class="va">path</span><span class="op">)</span></span></span>
	<span class="r-in"><span><span class="va">savedModel</span> <span class="op"><-</span> <span class="fu"><a href="read.ml.html">read.ml</a></span><span class="op">(</span><span class="va">path</span><span class="op">)</span></span></span>
	<span class="r-in"><span><span class="fu"><a href="summary.html">summary</a></span><span class="op">(</span><span class="va">savedModel</span><span class="op">)</span></span></span>
	<span class="r-in"><span><span class="op">}</span></span></span>
	</code></pre></div>
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