The previous article showed steps to perform Procrustean Individual Differences Scaling (PINDIS) using ‘Select’ method. This article explains application of PINDIS separately in Hamlet II. It also presents an example using PINDIS analysis to understand the application in depth. Accessing PINDIS separately is possible only after creating the input file using ‘Select’ function.
Steps to access PINDIS separately
Follow the steps provided in the last article to save the input file (“.inp”) in order to access the PINDIS separately.
Thereafter follow the below steps in Hamlet II.
- In the toolbar, go to ‘MDS’.
- Click on ‘PINDIS- Procrustean Individual Differences Scaling’.
Furthermore, follow the below four steps.
- Select the number of dimensions to be shown in the resulting graph (step 1 in the figure above).
- Click on ‘…’ to browse and select the saved file (step 2 in the figure above).
- Click on ‘Open’ (step 3 of the figure above).
- If a prompt by a pop-up ‘By default, the centroid will be used for reference’ shows up, then click on ‘Yes’.
Another dialogue box will appear which will confirm the configuration. Click on ‘Fit data to this configuration’ to continue to the analysis. Some of the users might receive a warning box stating ‘The viewpoint is not at the origin! ZOOM out to continue’. Keep clicking on ‘OK’ till it zooms out automatically in the background and optimizes the shape accordingly.
How to edit the graph?
In a new output window, a three-dimensional graph will appear with all the text points plotted based on their relationship according to the centroids (figure below). Use different functions in the task bar below in order to edit the graph.
Performing PINDIS analysis
In this section, PINDIS analysis is performed using an example to illustrate the use and significance of this type of multi-dimensional scaling analysis. For this example, use the following Michigan-Nijmegen Integrated Smallest Space Analysis (MINISSA in Hamlet II for multidimensional scaling) output file.
The hypothesis configurations will emerge for this data. In the figure below, these derived results have been rescaled based on the biggest coordinate value available to make it easier to view and understand. This also includes changes in sample text file to match the parameters with the contrasting text file for simpler analysis.
Findings of subject space graphical representation
Subject space shows the relationship of the different transcripts under comparison and provides normalized dimension weights of the similarity matrices. It is in furtherance to the results of MINISSA sets. The figure below exhibits the subject space where the points are present in a way that they are nearest to the centroid.
Click on ‘Data’ and then ‘View or edit data’ in order to view the normalized dimension weights of the graph.
Normalized Dimension Weights
The above ‘Normalized Dimension Weights’ show the placement and plotting of each of these subjects on different dimensions in Subject Space as shown in the figure above.
Click on ‘Next’ in order to view the results for different PINDIS transformations of each subject individually. Right-click on the endpoints of the labels to view a new graph on individual information.
Generate the two individual configurations in the image above by clicking on ‘Next’. These configuration graphs will display the three-dimensional plotting of the similarity transformations of each of the texts available in individual files. The table below emerges after testing the similarity of each of the texts based on their relative centroids. Each of the dimensions is showcasing closely related texts by assigning significant weights accordingly.
|Normed scalar for unconditional weights: 1.000000|
|Fit of subject to hypothesis <P0> — S (Z, X) = 0.820667|
Optimal multidimensional scaling results using PINDIS
There are various multidimensional scaling techniques available but PINDIS is one of the highly sophisticated procedures that applies a series of variations. This is because it increases flexibility in each iteration in order to obtain optimal results. Although this the last test of the Hamlet II software, there are more tools or software for similar analyses. Therefore the next article will review other text-based quantitative analysis software.
Latest posts by Avishek Majumder (see all)
- Serological and molecular marker analysis in infectious disease identification - January 14, 2019
- Health insurance policies play an important role in managing allergy - January 9, 2019
- Genetic biomarker of peripheral arterial disease (PAD) - January 7, 2019