Introduction This blog is about two new functions, Model_factors and garrett_ranking that have been added to the Dyn4cast package. The two functions provides means for gaining deeper insights into the meaning behind Likert-type variables collected from respondents. Garrett ranking provides the ranks of the observations of the variables based on the level of seriousness attached to it by the respondents. On the other hand, Model factors determines and retrieve the latent factors inherent in such data which now becomes continuous data.

Introduction The linear model still remains a reference point towards advanced modeling of some datasets as foundation for Machine Learning, Data Science and Artificial Intelligence in spite of some of her weaknesses. The major task in modeling is to compare various models before a selection is made for one or for advanced modeling. Often, some trial and error methods are used to decide which model to select. This is where this function is unique.

Introduction Often economic and other Machine Learning data are of different units or sizes making either estimation, interpretation or visualization difficult. The solution to these issues can be handled if the data can be transformed to unitless or data of similar magnitude. When the need to transform thus arises, then one finds it difficult to get handy function to achieve that.
In this blog, I share with you a function data_transform from Dyn4cast package that can easily transform your data.

Introduction In statistics, Mallows's Cp, named for Colin Lingwood Mallows, an English statistician, is used to assess the fit of a regression model that has been estimated using ordinary least squares. Models with a Mallows' Cp value near P+1 (i.e. the number of explanatory variables + 1) have a low bias. If every potential model has a high value for Mallows' Cp, this indicates that some important predictor variables are likely missing from each model.

Time-varying dynamic forecast, machine learning metrics, linear systems transformation, Mallow's Cp of economic data for national development.

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