R Biplot Psych

7/28/2022 admin

Added a biplot.psych function to do biplots of fa or principal results. Added an unbiased estimate of kurtosis in the kurtosi function. Added a predict function to fa and principal (predict.fa and predict.principal, both called from predict.psych) Added lavaan.diagram to draw the output from a cfa or sem from the lavaan package. Scale # covariance biplot (scale = 1), form biplot (scale = 0). When scale = 1, the inner product between the variables approximates the covariance and the distance between the points approximates the Mahalanobis distance. Obs.scale # 标准化观测值 var.scale # 标准化变异 pc.biplot # 兼容 biplot.princomp groups # 组信息. Rotations and transformations are either part of psych (Promax and cluster), of base R (varimax), or of GPArotation (simplimax, quartimax, oblimin). Some of the statistics reported are more appropriate for (maximum likelihood) factor analysis rather than principal components analysis, and are reported to allow comparisons with these other models. R/biplot.psych.R In psych: Procedures for Psychological, Psychometric, and Personality Research Defines functions plotone. #rewritten Sept 18,2013 to.

Working on a recent project to characterize the underlying factors of urban typology, we needed a quick means of visualizing the factor loadings of each indicator. We had used the “pysch” library’s “fa” function to get the analysis done. However, a quick visualization approach was not forthcoming. One of my research assistants found this fantastic example using ‘ggplot2’. However, getting the ggplot function call to work was not quite as straightforward as in the example shown.

In case you are in a similar situation, where you have obtained the loading matrix directly from the ‘fa’ function and want to use the beautiful rendering of the ggplot facet-bar approach, below is code that you might find useful.

The additional steps involve converting the loading matrix to a dataframe and then converting the row names to a dataframe column. Ignore the descriptive factor names chosen for this example, and the input file can be dataframe or matrix.

</div><table><tbody><tr><td><div><div>2</div><div>4</div><div>6</div><div>8</div><div>10</div><div>12</div><div>14</div><div>16</div><div>18</div><div>20</div><div>22</div><div>24</div><div>26</div><div>28</div><div>30</div><div>32</div></div></td><td><div><div><span>library</span><span>(</span><span>data</span><span>.</span><span>table</span><span>)</span></div><div><span>library</span><span>(</span><span>reshape2</span><span>)</span></div><div><span>options</span><span>(</span><span>width</span><span>=</span><span>240</span><span>)</span></div><div><span>df</span><span>=</span><span>read</span><span>.</span><span>csv</span><span>(</span><span>'precities.csv'</span><span>)</span></div><div><span>#scree(df)</span></div><div><span>#fa.parallel(df2)</span></div><div><span>#loadings</span></div><div><span># print(fa(df,9,rotate='oblimin',fm='minres')$loadings,cut=.2)</span></div><div><span>load</span><span>=</span><span>load</span><span>$</span><span>loadings</span></div><div><span>load</span><span>=</span><span>data</span><span>.</span><span>frame</span><span>(</span><span>load</span><span>)</span></div><div><span>colnames</span><span>(</span><span>load</span><span>)</span><span>[</span><span>1</span><span>]</span><span><</span><span>-</span><span>'Indicators'</span></div><div><span>colnames</span><span>(</span><span>load</span><span>)</span><span>[</span><span>2</span><span>:</span><span>10</span><span>]</span><span><</span><span>-</span><span>c</span><span>(</span><span>'Urbanization'</span><span>,</span><span>'Density, metro'</span><span>,</span><span>'Cost, innovation'</span><span>,</span><span>'Public/active transit'</span><span>,</span><span>'Congestion'</span><span>,</span><span>'BRT'</span><span>,</span><span>'Bikeshare'</span><span>,</span><span>'Urban poverty'</span><span>,</span><span>'Urban sprawl'</span><span>)</span></div><div><span>load</span><span>.</span><span>m</span><span><</span><span>-</span><span>melt</span><span>(</span><span>load</span><span>,</span><span>id</span><span>=</span><span>'Indicators'</span><span>,</span><span>variable</span><span>.</span><span>name</span><span>=</span><span>'Factors'</span><span>,</span><span>value</span><span>.</span><span>name</span><span>=</span><span>'Loading'</span><span>,</span><span>measure</span><span>=</span><span>colnames</span><span>(</span><span>load</span><span>)</span><span>[</span><span>2</span><span>:</span><span>10</span><span>]</span><span>)</span></div><div><span>#load.m <- melt(load, variable.name='Factors', value.name='Loading', measure = c('Urbanization','Density, metro','Cost, innovation','Public/active transit','Congestion','BRT','Bikeshare','Urban poverty','Urban sprawl'))</span></div><div><span>loadPlot</span><span><</span><span>-</span><span>ggplot</span><span>(</span><span>load</span><span>.</span><span>m</span><span>,</span><span>aes</span><span>(</span><span>Indicators</span><span>,</span><span>abs</span><span>(</span><span>Loading</span><span>)</span><span>,</span><span>fill</span><span>=</span><span>Loading</span><span>)</span><span>)</span><span>+</span><span>facet_wrap</span><span>(</span><span>~</span><span>Factors</span><span>,</span><span>nrow</span><span>=</span><span>1</span><span>)</span><span>+</span><span>geom_bar</span><span>(</span><span>stat</span><span>=</span><span>'identity'</span><span>)</span><span>+</span><span>coord_flip</span><span>(</span><span>)</span><span>+</span></div><div><span>scale_fill_gradient2</span><span>(</span><span>name</span><span>=</span><span>'Loading'</span><span>,</span><span>high</span><span>=</span><span>'blue'</span><span>,</span><span>mid</span><span>=</span><span>'white'</span><span>,</span><span>low</span><span>=</span><span>'red'</span><span>,</span><span>midpoint</span><span>=</span><span>0</span><span>,</span><span>guide</span><span>=</span><span>F</span><span>)</span><span>+</span><span>ylab</span><span>(</span><span>'Loading'</span><span>)</span><span>+</span><span>theme_bw</span><span>(</span><span>base_size</span><span>=</span><span>10</span><span>)</span></div></div></td></tr></tbody></table><p>Hi,</p><p>I have a prcomp object (generated using the prcomp function) and I am trying to generate a biplot using ggbiplot, however I am confused about the different scaling options and their impact on the meaning of the plot. </p><p>scale = covariance biplot (scale = 1), form biplot (scale = 0). When scale = 1, the inner product between the variables approximates the covariance and the distance between the points approximates the Mahalanobis distance.</p><h3>How To Read Biplot</h3><p>obs.scale = scale factor to apply to observations</p><img src='https://imgv2-2-f.scribdassets.com/img/document/353824089/original/f84c48b6d1/1609605418?v=1' alt='R Biplot Psych' title='R Biplot Psych' /><h3>How To Interpret Pca Biplot</h3><p>var.scale = scale factor to apply to variables</p><p>I am not sure which of these scaling options I should choose for a prcomp object? I have seen lots of people set obs.scale = 1 and var.scale = 1, but I don't understand the reason for this.</p><img src='https://www.spandidos-publications.com/article_images/etm/21/1/etm-21-01-09450-g00.jpg' alt='How to interpret pca biplot' title='How to interpret pca biplot' /><p>What does the line length and angle actually correspond to in a biplot? I get the general idea that a high value on PC1 indicates that the variable has a strong influence on PC1 whilst a small value indicates a small influence. And that if the arrow is pointing to the right, then that variable has a positive impact on the PC. </p><img src='https://i.stack.imgur.com/wxmKf.png' alt='R Biplot Psych' title='R Biplot Psych' /><p>Thanks for the help!!</p><p>Best wishes,</p><p>Lucy</p></div><footer class="alt footer-primary primary-site"></footer></article></div><div class="block-row-panel"></div></div></main><div id="sm-page-container" class="primary-nav-items sm-entry-page entry-container col-extra-sm" role="complementary"><aside id="aside-entry-clear" class="main-block-new entry-box-site clear-entry-article"><form role="search" method="get" class="panel-box-single" action="#"><div class="md-article-site"><input type="search" class="site-layout-fix" placeholder="Search …" value="" name="s" title="Search:"></div><div class="fn-article-abbr"><button type="submit" class="abbr-lg-default">Search</button></div></form></aside><aside id="label-default-panel" class="clear-entry-article post-fix-extra main-block-new"><h3 class="default-col-article">Most Viewed News</h3><ul><li><a href='/slots-empire-sign-up'>Slots Empire Sign Up</a></li><li><a href='/optical-zoom'>Optical Zoom</a></li><li><a href='/x-quote-after-hours'>X Quote After Hours</a></li><li><a href='/carslotcom'>Carslot.com</a></li><li><a href='/caucasian-meaning'>Caucasian Meaning</a></li><br></ul></aside></div></div> </div></div></div><footer id="new-copyright-footer" class="footer-top-fix" role="contentinfo"><div class="col-block-entry"><div class="label-abbr-fix"><div class="row-content-single site-fix-layout">© 2022 tisilige1979.netlify.com. </div><div class="site-fix-layout fn-panel-container"><div class="site-fix-layout-content"></div></div></div></div></footer></body></html>