tag:crantastic.org,2005:/authors/1285Latest activity for Fabio Presaghi2019-12-12T12:03:35Zcrantastic.orgtag:crantastic.org,2005:TimelineEvent/960122019-12-12T12:03:35Z2019-12-12T12:03:35Zrandom.polychor.pa was upgraded to version 1.1.4-3<a href="/packages/random-polychor-pa">random.polychor.pa</a> was <span class="action">upgraded</span> to version <a href="/packages/random-polychor-pa/versions/91264">1.1.4-3</a><br /><h3>Package description:</h3><p>The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/532532016-07-26T11:21:04Z2016-07-26T11:21:04Zrandom.polychor.pa was upgraded to version 1.1.4-2<a href="/packages/random-polychor-pa">random.polychor.pa</a> was <span class="action">upgraded</span> to version <a href="/packages/random-polychor-pa/versions/51358">1.1.4-2</a><br /><h3>Package description:</h3><p>The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/349502013-08-09T18:51:09Z2013-08-09T18:51:09Zrandom.polychor.pa was upgraded to version 1.1.3.4<a href="/packages/random-polychor-pa">random.polychor.pa</a> was <span class="action">upgraded</span> to version <a href="/packages/random-polychor-pa/versions/29972">1.1.3.4</a><br /><h3>Package description:</h3><p>The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Version 1.1.1, fixed a minor bug in the regarding the estimated time needed to complete the simulation. Also in this version, the function is now able to manage supplied data.matrix in which variables representing factors (i.e., variables with ordered categories) are present and may cause an error when the Pearson correlation matrix is calculated. Version 1.1.2 simply has updated the function that calculates the polychoric correlation matrix due to changes in the psych() package. Version 1.1.3 tackles two problems signalled by users: 1) the possibility to make available the results of simulation for plotting them in other software. Now the random.polychor.pa will show, upon request, all the data used in the scree-plot. 2) The function polichoric() of the psych() package does not handle data matrices that include 0 as possible category and will cause the function to stop with error. So a check for the detection of the 0 code within the provided data.matrix is now added and will cause the random.polychor.pa function to stop with a warning message.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/300242013-03-04T09:10:54Z2013-03-04T09:10:54Zrandom.polychor.pa was upgraded to version 1.1.3.3<a href="/packages/random-polychor-pa">random.polychor.pa</a> was <span class="action">upgraded</span> to version <a href="/packages/random-polychor-pa/versions/25653">1.1.3.3</a><br /><h3>Package description:</h3><p>The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Version 1.1.1, fixed a minor bug in the regarding the estimated time needed to complete the simulation. Also in this version, the function is now able to manage supplied data.matrix in which variables representing factors (i.e., variables with ordered categories) are present and may cause an error when the Pearson correlation matrix is calculated. Version 1.1.2 simply has updated the function that calculates the polychoric correlation matrix due to changes in the psych() package. Version 1.1.3 tackles two problems signalled by users: 1) the possibility to make available the results of simulation for plotting them in other software. Now the random.polychor.pa will show, upon request, all the data used in the scree-plot. 2) The function polichoric() of the psych() package does not handle data matrices that include 0 as possible category and will cause the function to stop with error. So a check for the detection of the 0 code within the provided data.matrix is now added and will cause the random.polychor.pa function to stop with a warning message.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/188082012-01-10T23:16:09Z2012-01-10T23:16:09Zrandom.polychor.pa was upgraded to version 1.1.3.2<a href="/packages/random-polychor-pa">random.polychor.pa</a> was <span class="action">upgraded</span> to version <a href="/packages/random-polychor-pa/versions/16497">1.1.3.2</a><br /><h3>Package description:</h3><p>The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Version 1.1.1, fixed a minor bug in the regarding the estimated time needed to complete the simulation. Also in this version, the function is now able to manage supplied data.matrix in which variables representing factors (i.e., variables with ordered categories) are present and may cause an error when the Pearson correlation matrix is calculated. Version 1.1.2 simply has updated the function that calculates the polycoric correlation matrix due to changes in the psych() package. Version 1.1.3 tackles two problems signalled by users: 1) the possibility to make available the results of simulation for plotting them in other softwares. Now the random.polychor.pa will show, upon request, all the data used in the scree-plot. 2) The function polichoric() of the psych() package does not handle data matrices that include 0 as possible category and will cause the function to stop with error. So a check for the detection of the 0 code within the provided data.matrix is now added and will cause the random.polychor.pa function to stop with a warning message.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/173162011-11-07T14:30:49Z2011-11-07T14:30:49Zrandom.polychor.pa was upgraded to version 1.1.3.1<a href="/packages/random-polychor-pa">random.polychor.pa</a> was <span class="action">upgraded</span> to version <a href="/packages/random-polychor-pa/versions/15460">1.1.3.1</a><br /><h3>Package description:</h3><p>The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Version 1.1.1, fixed a minor bug in the regarding the estimated time needed to complete the simulation. Also in this version, the function is now able to manage supplied data.matrix in which variables representing factors (i.e., variables with ordered categories) are present and may cause an error when the Pearson correlation matrix is calculated. Version 1.1.2 simply has updated the function that calculates the polycoric correlation matrix due to changes in the psych() package. Version 1.1.3 tackles two problems signalled by users: 1) the possibility to make available the results of simulation for plotting them in other softwares. Now the random.polychor.pa will show, upon request, all the data used in the scree-plot. 2) The function polichoric() of the psych() package does not handle data matrices that include 0 as possible category and will cause the function to stop with error. So a check for the detection of the 0 code within the provided data.matrix is now added and will cause the random.polychor.pa function to stop with a warning message.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/158902011-09-05T19:50:36Z2011-09-05T19:50:36Zrandom.polychor.pa was upgraded to version 1.1.3<a href="/packages/random-polychor-pa">random.polychor.pa</a> was <span class="action">upgraded</span> to version <a href="/packages/random-polychor-pa/versions/14136">1.1.3</a><br /><h3>Package description:</h3><p>The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Version 1.1.1, fixed a minor bug in the regarding the estimated time needed to complete the simulation. Also in this version, the function is now able to manage supplied data.matrix in which variables representing factors (i.e., variables with ordered categories) are present and may cause an error when the Pearson correlation matrix is calculated. Version 1.1.2 simply has updated the function that calculates the polycoric correlation matrix due to changes in the psych() package. Version 1.1.3 tackles two problems signalled by users: 1) the possibility to make available the results of simulation for plotting them in other softwares. Now the random.polychor.pa will show, upon request, all the data used in the scree-plot. 2) The function polichoric() of the psych() package does not handle data matrices that include 0 as possible category and will cause the function to stop with error. So a check for the detection of the 0 code within the provided data.matrix is now added and will cause the random.polychor.pa function to stop with a warning message.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/96762010-11-03T10:30:20Z2010-11-03T10:30:20Zrandom.polychor.pa was upgraded to version 1.1.2<a href="/packages/random-polychor-pa">random.polychor.pa</a> was <span class="action">upgraded</span> to version <a href="/packages/random-polychor-pa/versions/9404">1.1.2</a><br /><h3>Package description:</h3><p>The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Version 1.1.1, fixed a minor bug in the regarding the estimated time needed to complete the simulation. Also in this version, the function is now able to manage supplied data.matrix in which variables representing factors (i.e., variables with ordered categories) are present and may cause an error when the Pearson correlation matrix is calculated. Version 1.1.2 simply has updated the function that calculates the polycoric correlation matrix due to changes in the psych() package.</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/74432010-07-16T10:58:28Z2010-07-16T10:58:28Zrandom.polychor.pa was upgraded to version 1.1.1<a href="/packages/random-polychor-pa">random.polychor.pa</a> was <span class="action">upgraded</span> to version <a href="/packages/random-polychor-pa/versions/7892">1.1.1</a><br /><h3>Package description:</h3><p>The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Version 1.1.1 a minor bug in showing the estimanted time needed to conclude the simulation is fixed. In this version it is also introduced the possibility to manage datafiles containing factor variables (i.e., variables with ordered categories) which in past versions may cause the function to stop computations when the Pearson correlation matrix is computed (due to the fact that in this instance a numerical matrix is expected).</p>crantastic.orgtag:crantastic.org,2005:TimelineEvent/50392010-03-11T11:46:55Z2010-03-11T11:46:55Zrandom.polychor.pa was released<a href="/packages/random-polychor-pa">random.polychor.pa</a> was <span class="action">released</span><br /><h3>Package description:</h3><p>The Function performs a parallel analysis using simulated polychoric correlation matrices. The nth-percentile of the eigenvalues distribution obtained from both the randomly generated and the real data polychoric correlation matrices is returned. A plot comparing the two types of eigenvalues (real and simulated) will help determine the number of real eigenvalues that outperform random data. The function is based on the idea that if real data are non-normal and the polychoric correlation matrix is needed to perform a Factor Analysis, then the Parallel Analysis method used to choose a non-random number of factors should also be based on randomly generated polychoric correlation matrices and not on Pearson correlation matrices. Random data sets are simulated assuming or a uniform or a multinomial distribution or via the bootstrap method of resampling (i.e., random permutations of cases). Also Multigroup Parallel analysis is made available for random (uniform and multinomial distribution and with or without difficulty factor) and bootstrap methods. An option to choose between default or full output is also available as well as a parameter to print Fit Statistics (Chi-squared, TLI, RMSEA, RMR and BIC) for the factor solutions indicated by the Parallel Analysis.</p>crantastic.org