fibtele

datasets

genpathmox provides a very interesting solution for handling
    segmentation variables in complex statistical methodology. It contains en
    extended version of the PATHMOX algorithm in the context of partial least
    square path modeling (Sanchez, 2009) including the F-block test (to detect
    the responsible latent endogenous equations of the difference), the
    F-coefficient (to detect the path coefficients responsible of the
    difference) and the invariance test (to realize a comparison between the
    sub-models' latent variables). Furthermore, the package contains a
    generalized version of the PATHMOX algorithm to approach different
    methodologies: linear regression and least absolute regression models.

Giuseppe Lamberti

genpathmox

Generalized PATHMOX Algorithm for PLS-PM, LS and LAD Regression

fibtele function

A data frame with 147 observations on the following 35
variables. The first ten variables are segmentation variables.
The rest of the variables refer to five latent concepts: 1) <code>Image</code>=Image, 2)
  <code>Qual.spec</code>=Specific Quality, 3) <code>Qual.gen</code>=Generic Quality, 4)
<code>Value</code>=Value, 5) <code>Satis</code>=Satisfaction.
 Variables description
<ul><li><code>Image</code></li></ul>: Generic students perception of ICT schools: (internationally recognized,
     ranges of courses, leader in research).
<item><code>Qual.spec</code></item>: Perception about the achieved quality on the specific skills in the school.
<item><code>Qual.gen</code></item>: Perception about achieved quality on the  generic skills in
the school (abilities in solving problem, communication skills).
<item><code>Value</code></item>: The advantage or profit that the alumni may draw from the school
degree (well paid job, motivated job, prospectives in improvement and promotion).
<item><code>Satis</code></item>: Degree of alumni satisfaction about the formation in school respect to
their actual work conditions.

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itemize

<ul><li><code>ima2</code></li><li><code>ima3</code></li><li><code>ima4</code></li><li><code>ima5</code></li><li><code>ima6</code></li><li><code>ima7</code></li><li><code>ima8</code></li><li><code>quaf1</code></li><li><code>quaf2</code></li><li><code>quaf3</code></li><li><code>qutr1</code></li><li><code>qutr2</code></li><li><code>qutr3</code></li><li><code>qutr4</code></li><li><code>qutr5</code></li><li><code>val1</code></li><li><code>val2</code></li><li><code>val3</code></li><li><code>val4</code></li><li><code>sat1</code></li><li><code>sat2</code></li><li><code>sat3</code></li><li><code>sat4</code></li><li><code>Gender</code></li><li><code>Age</code></li><li><code>Studying</code></li><li><code>Contract</code></li><li><code>Salary</code></li><li><code>Firmtype</code></li><li><code>Accgrade</code></li><li><code>Grade</code></li><li><code>Startwork</code></li></ul>

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Laboratory of Information Analysis and Modeling (LIAM).
   Facultat de Informatica de Barcelona, Universitat Politecnica de Catalunya.

fibtele: Fibtele

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