The rms package offers a variety of tools to build and evaluate regression models in R. Originally named ‘Design’, the package accompanies the book “Regression Modeling Strategies” by Frank Harrell, which is essential reading for anyone who works in the ‘data science’ space. Over the past year or so, I have transitioned my personal modeling … Continue reading Introduction to the RMS Package
Given a data frame with multiple columns which contain time series data, let’s say that we are interested in executing an automatic forecasting algorithm on a number of columns. Furthermore, we want to train the model on a particular number of observations and assess how well they forecast future values. Based upon those testing procedures, … Continue reading Batch Forecasting in R
For those of us who received statistical training outside of statistics departments, it often emphasized procedures over principles. This entailed that we learned about various statistical techniques and how to perform analysis in a particular statistical software, but glossed over the mechanisms and mathematical statistics underlying these practices. While that training methodology (hereby referred to … Continue reading Statistical Reading Rainbow
Lets say that we estimated a linear regression model on time series data with lagged predictors. The goal is to estimate sales as a function of inventory, search volume, and media spend from two months ago. After using the lm function to perform linear regression, we predict sales using values from two month ago. If … Continue reading Weekly R-Tips: Visualizing Predictions
Number 1: Importing Multiple Packages Anyone who has used R for some time has written code that required the use of multiple packages. In most cases, this will be done by using the library or require function to bring in the appropriate extensions. That’s nice and gets the desired result, but can’t we just import … Continue reading Weekly R-Tips: Importing Packages and User Inputs
This is part two of the ‘applied statistical theory’ series that will cover the bare essentials of various statistical techniques. As analysts, we need to know enough about what we’re doing to be dangerous and explain approaches to others. It’s not enough to say “I used X because the misclassification rate was low.” Standard linear … Continue reading Applied Statistical Theory: Quantile Regression
Applied statistical theory is a new series that will cover the basic methodology and framework behind various statistical procedures. As analysts, we need to know enough about what we’re doing to be dangerous and explain approaches to others. It’s not enough to say “I used X because the misclassification rate was low.” At the same … Continue reading Applied Statistical Theory: Belief Networks
Forecasting refers to the process of using statistical procedures to predict future values of a time series based on historical trends. For businesses, being able gauge expected outcomes for a given time period is essential for managing marketing, planning, and finances. For example, an advertising agency may want to utilizes sales forecasts to identify which … Continue reading Basic Forecasting
Using R Functions in Python
My previous post covered the basics of logistic regression. We must now examine the model to understand how well it fits the data and generalizes to other observations. The evaluation process involves the assessment of three distinct areas – goodness of fit, tests of individual predictors, and validation of predicted values – in order to … Continue reading Logistic Regression in R – Part Two
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