Introduction This blog is a new function, treatment_model that have been added to the Dyn4cast package. The function provides means for enhanced estimation of propensity score and treatments effects from randomized controlled designed experiments.
Observational study involves the evaluation of outcomes of participants not randomly assigned treatments or exposures. To be able to assess the effects of the outcome, the participants are matched using propensity scores (PSM). This then enables the determination of the effects of the treatments on those treated against those who were not treated.

Introduction This blog is about world happiness ladder using the world happiness report data sets (Helliwell et. al., 2024). The basic objective is to demonstrate the use of panel data which is quite distinct from cross-sectional or time series data.
Global happiness ladder Cross-sectional happiness ladder for 2023 Fixed time, it is cross-sectional
Times series vs. panel data visualisation Each line is a timeseries but together, it is panel data

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.

Introduction In the 933 days since the first COVID-19 case was reported on February 29, 2020 in Nigeria, about 267,511 cases have been recorded with 1.18% fatalities. Except for Kogi State which Government refused monitoring laboratories to be setup in her health facilities for confirming and managing COVID-19, cases have been recorded on regular basis in the other 35 states and Abuja, the Federal Capital. However, the frequency of cases vary from state to state.

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

Load library and the data The data is scrapped from the website of the Nigerian Centre for Disease Control (NCDC) i.e (NCDC 2020). The scrapping was done with some bits of tricks. Please see my post on that. The BREAKS were established from the visual inspection of the data (see (Nmadu, Yisa, and Mohammed 2009))
library(tidyverse) library(splines) library(Metrics) library(scales) library(readxl) library(patchwork) library(Dyn4cast) BREAKS <- c(70, 131, 173, 228, 274, 326) z.

Introduction The advent of the COVID-19 pandemic really put everyone in confusion and as the days, weeks went-by, everyone was trying to understand the trend and the direction of the incidence. While the medicals were in their labs trying to understand the anatomy of the various, various statisticians and data scientists were trying to model the trend so as to guide future actions and preparations. One of the early models was done for Australia by (Krispin and Byrnes 2020).

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