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Lrnr_pca.html
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<!-- Generated by pkgdown: do not edit by hand -->
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<title>Principal Component Analysis and Regression — Lrnr_pca • sl3</title>
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<meta property="og:title" content="Principal Component Analysis and Regression — Lrnr_pca" />
<meta property="og:description" content="This learner provides facilities for performing principal components analysis
(PCA) to reduce the dimensionality of a data set to a pre-specified value.
For further details, consult the documentation of prcomp from the core
package stats. This learner object is primarily intended for use with
other learners as part of a pre-processing pipeline." />
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<h1>Principal Component Analysis and Regression</h1>
<small class="dont-index">Source: <a href='https://github.com/tlverse/sl3/blob/master/R/Lrnr_pca.R'><code>R/Lrnr_pca.R</code></a></small>
<div class="hidden name"><code>Lrnr_pca.Rd</code></div>
</div>
<div class="ref-description">
<p>This learner provides facilities for performing principal components analysis
(PCA) to reduce the dimensionality of a data set to a pre-specified value.
For further details, consult the documentation of <code>prcomp</code> from the core
package <code>stats</code>. This learner object is primarily intended for use with
other learners as part of a pre-processing pipeline.</p>
</div>
<h2 class="hasAnchor" id="format"><a class="anchor" href="#format"></a>Format</h2>
<p><code>R6Class</code> object.</p>
<h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2>
<p>Learner object with methods for training and prediction. See
<code><a href='Lrnr_base.html'>Lrnr_base</a></code> for documentation on learners.</p>
<h2 class="hasAnchor" id="parameters"><a class="anchor" href="#parameters"></a>Parameters</h2>
<dl>
<dt><code>n_comp</code></dt><dd><p>A <code>numeric</code> value indicating the number of
components to be produced as a result of the PCA dimensionality
reduction. For convenience, this defaults to two (2) components.</p></dd>
<dt><code>center</code></dt><dd><p>A <code>logical</code> value indicating whether the input
data matrix should be centered before performing PCA. This defaults to
<code>TRUE</code> since that is the recommended practice. Consider consulting
the documentation of <code>prcomp</code> for details.</p></dd>
<dt><code>scale.</code></dt><dd><p>A <code>logical</code> value indicating whether the input
data matrix should be scaled (to unit variance) before performing PCA.
Consider consulting the documentation of <code>prcomp</code> for details.</p></dd>
<dt><code>...</code></dt><dd><p>Other optional parameters to be passed to <code>prcomp</code>.
Consider consulting the documentation of <code>prcomp</code> for details.</p></dd>
</dl>
<h2 class="hasAnchor" id="common-parameters"><a class="anchor" href="#common-parameters"></a>Common Parameters</h2>
<p>Individual learners have their own sets of parameters. Below is a list of shared parameters, implemented by <code>Lrnr_base</code>, and shared
by all learners.</p>
<dl>
<dt><code>covariates</code></dt><dd><p>A character vector of covariates. The learner will use this to subset the covariates for any specified task</p></dd>
<dt><code>outcome_type</code></dt><dd><p>A <code><a href='Variable_Type.html'>variable_type</a></code> object used to control the outcome_type used by the learner. Overrides the task outcome_type if specified</p></dd>
<dt><code>...</code></dt><dd><p>All other parameters should be handled by the invidual learner classes. See the documentation for the learner class you're instantiating</p></dd>
</dl>
<h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2>
<div class='dont-index'><p>Other Learners:
<code><a href='Custom_chain.html'>Custom_chain</a></code>,
<code><a href='Lrnr_HarmonicReg.html'>Lrnr_HarmonicReg</a></code>,
<code><a href='Lrnr_arima.html'>Lrnr_arima</a></code>,
<code><a href='Lrnr_bartMachine.html'>Lrnr_bartMachine</a></code>,
<code><a href='Lrnr_base.html'>Lrnr_base</a></code>,
<code><a href='Lrnr_bayesglm.html'>Lrnr_bayesglm</a></code>,
<code><a href='Lrnr_bilstm.html'>Lrnr_bilstm</a></code>,
<code><a href='Lrnr_bound.html'>Lrnr_bound</a></code>,
<code><a href='Lrnr_caret.html'>Lrnr_caret</a></code>,
<code><a href='Lrnr_cv_selector.html'>Lrnr_cv_selector</a></code>,
<code><a href='Lrnr_cv.html'>Lrnr_cv</a></code>,
<code><a href='Lrnr_dbarts.html'>Lrnr_dbarts</a></code>,
<code><a href='Lrnr_define_interactions.html'>Lrnr_define_interactions</a></code>,
<code><a href='Lrnr_density_discretize.html'>Lrnr_density_discretize</a></code>,
<code><a href='Lrnr_density_hse.html'>Lrnr_density_hse</a></code>,
<code><a href='Lrnr_density_semiparametric.html'>Lrnr_density_semiparametric</a></code>,
<code><a href='Lrnr_earth.html'>Lrnr_earth</a></code>,
<code><a href='Lrnr_expSmooth.html'>Lrnr_expSmooth</a></code>,
<code><a href='Lrnr_gam.html'>Lrnr_gam</a></code>,
<code><a href='Lrnr_gbm.html'>Lrnr_gbm</a></code>,
<code><a href='Lrnr_glm_fast.html'>Lrnr_glm_fast</a></code>,
<code><a href='Lrnr_glmnet.html'>Lrnr_glmnet</a></code>,
<code><a href='Lrnr_glm.html'>Lrnr_glm</a></code>,
<code><a href='Lrnr_grf.html'>Lrnr_grf</a></code>,
<code><a href='Lrnr_gru_keras.html'>Lrnr_gru_keras</a></code>,
<code><a href='Lrnr_gts.html'>Lrnr_gts</a></code>,
<code><a href='Lrnr_h2o_grid.html'>Lrnr_h2o_grid</a></code>,
<code><a href='Lrnr_hal9001.html'>Lrnr_hal9001</a></code>,
<code><a href='Lrnr_haldensify.html'>Lrnr_haldensify</a></code>,
<code><a href='Lrnr_hts.html'>Lrnr_hts</a></code>,
<code><a href='Lrnr_independent_binomial.html'>Lrnr_independent_binomial</a></code>,
<code><a href='Lrnr_lightgbm.html'>Lrnr_lightgbm</a></code>,
<code><a href='Lrnr_lstm_keras.html'>Lrnr_lstm_keras</a></code>,
<code><a href='Lrnr_lstm.html'>Lrnr_lstm</a></code>,
<code><a href='Lrnr_mean.html'>Lrnr_mean</a></code>,
<code><a href='Lrnr_multiple_ts.html'>Lrnr_multiple_ts</a></code>,
<code><a href='Lrnr_multivariate.html'>Lrnr_multivariate</a></code>,
<code><a href='Lrnr_nnet.html'>Lrnr_nnet</a></code>,
<code><a href='Lrnr_nnls.html'>Lrnr_nnls</a></code>,
<code><a href='Lrnr_optim.html'>Lrnr_optim</a></code>,
<code><a href='Lrnr_pkg_SuperLearner.html'>Lrnr_pkg_SuperLearner</a></code>,
<code><a href='Lrnr_polspline.html'>Lrnr_polspline</a></code>,
<code><a href='Lrnr_pooled_hazards.html'>Lrnr_pooled_hazards</a></code>,
<code><a href='Lrnr_randomForest.html'>Lrnr_randomForest</a></code>,
<code><a href='Lrnr_ranger.html'>Lrnr_ranger</a></code>,
<code><a href='Lrnr_revere_task.html'>Lrnr_revere_task</a></code>,
<code><a href='Lrnr_rpart.html'>Lrnr_rpart</a></code>,
<code><a href='Lrnr_rugarch.html'>Lrnr_rugarch</a></code>,
<code><a href='Lrnr_screener_augment.html'>Lrnr_screener_augment</a></code>,
<code><a href='Lrnr_screener_coefs.html'>Lrnr_screener_coefs</a></code>,
<code><a href='Lrnr_screener_correlation.html'>Lrnr_screener_correlation</a></code>,
<code><a href='Lrnr_screener_importance.html'>Lrnr_screener_importance</a></code>,
<code><a href='Lrnr_sl.html'>Lrnr_sl</a></code>,
<code><a href='Lrnr_solnp_density.html'>Lrnr_solnp_density</a></code>,
<code><a href='Lrnr_solnp.html'>Lrnr_solnp</a></code>,
<code><a href='Lrnr_stratified.html'>Lrnr_stratified</a></code>,
<code><a href='Lrnr_subset_covariates.html'>Lrnr_subset_covariates</a></code>,
<code><a href='Lrnr_svm.html'>Lrnr_svm</a></code>,
<code><a href='Lrnr_tsDyn.html'>Lrnr_tsDyn</a></code>,
<code><a href='Lrnr_ts_weights.html'>Lrnr_ts_weights</a></code>,
<code><a href='Lrnr_xgboost.html'>Lrnr_xgboost</a></code>,
<code><a href='Pipeline.html'>Pipeline</a></code>,
<code><a href='Stack.html'>Stack</a></code>,
<code><a href='define_h2o_X.html'>define_h2o_X</a>()</code>,
<code><a href='undocumented_learner.html'>undocumented_learner</a></code></p></div>
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