We recently had a dilemma for an OSI publication about the design for the graphs. There will be dozens of these graphs showing the mean score on a given variable for nearly 11000 parents from 10 countries. This example is for household wealt...

We recently had a dilemma for an OSI publication about the design for the graphs. There will be dozens of these graphs showing the mean score on a given variable for nearly 11000 parents from 10 countries. This example is for household wealth which has values ranging from 0 to 16. These are the three

In today’s lesson we’ll learn about linear mixed effects models (LMEM), which give us the power to account for multiple types of effects in a single model. This is Part 1 of a two part lesson. I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done that Lesson 6, Part...

In today’s lesson we’ll take care of the baseline issue we had in the last lesson when we have a linear model with an interaction. To do that we’ll be learning about analysis of variance or ANOVA. We’ll also be going over how to make barplots with error bars, but not without hearing my reasons Lesson 5: Analysis...

Feature selection is a process of extracting valuable features that have significant influence on dependent variable. This is still an active field of research and machine wandering. In this post I compare few feature selection algorithms: traditional GLM with regularization, computationally demanding Boruta and entropy based filter from FSelectorRcpp (free of Java/Weka) package....

Introduction Today we’ll see what happens when you have not one, but two variables in your model. We will also continue to use some old and new dplyr calls, as well as another parameter for our ggplot2 figure. I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done Lesson 4: Multiple...

Welcome to Introduction to R for Data Science Session 7: Multiple Regression + Dummy Coding, Partial and Part Correlations [Multiple Linear Regression in R. Dummy coding: various ways to do it in R. Factors. Inspecting the multiple regression model: regression coefficients and their interpretation, confidence intervals, predictions. Introducing {lattice} plots + ggplot2. Assumptions: multicolinearity and testing it from the...

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