# 2042 search results for "regression"

## A Shiny App for Passing Bablok and Deming Regression

Background Back in 2011 I was not aware of any tool in R for Passing Bablok (PB) regression, a form of robust regression described in a series of three papers in Clinical Chemistry and Laboratory Medicine (then J Clin Chem and Biochem) available here, here and here. For reasons that are not entirely clear to … Continue...

## Performing Principal Components Regression (PCR) in R

July 20, 2016
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Principal components regression (PCR) is a regression method based on Principal Component Analysis: discover how to perform this Data Mining technique in R The post Performing Principal Components Regression (PCR) in R appeared first on MilanoR.

## Introduction to R for Data Science :: Session 8 [Intro to Text Mining in R, ML Estimation + Binomial Logistic Regression]

June 21, 2016
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Welcome to Introduction to R for Data Science, Session 8: Intro to Text Mining in R, ML Estimation + Binomial Logistic Regression [Web-scraping with tm.plugin.webmining. The tm package corpora structures: assessing document metadata and content. Typical corpus transformations and Term-Document Matrix production. A simple binomial regression model with tf-idf scores as features and its shortcommings due to sparse data....

## R for Publication by Page Piccinini: Lesson 4 – Multiple Regression

June 13, 2016
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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...

## Introduction to R for Data Science :: Session 7 [Multiple Linear Regression Model in R  + Categorical Predictors, Partial and Part Correlation]

June 9, 2016
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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...

## Why you should read Nina Zumel’s 3 part series on principal components analysis and regression

June 9, 2016
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Short form: Win-Vector LLC’s Dr. Nina Zumel has a three part series on Principal Components Regression that we think is well worth your time. Part 1: the proper preparation of data (including scaling) and use of principal components analysis (particularly for supervised learning or regression). Part 2: the introduction of y-aware scaling to direct the … Continue reading...

## R for Publication by Page Piccinini: Lesson 3 – Logistic Regression

June 9, 2016
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Today we’ll be moving from linear regression to logistic regression. This lesson also introduces a lot of new dplyr verbs for data cleaning and summarizing that we haven’t used before. Once again, I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done that yet be sure to go Lesson 3: Logistic...

## Introduction to R for Data Science :: Session 6 [Linear Regression Model in R  + EDA, and Normality Tests]

June 6, 2016
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Welcome to Introduction to R for Data Science Session 6: Linear Regression + EDA, and Normality tests The course is co-organized by Data...

## R for Publication by Page Piccinini: Lesson 2 – Linear Regression

June 2, 2016
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This is our first lesson where we actually learn and use a new statistic in R. For today’s lesson we’ll be focusing on linear regression. I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done that yet be sure to go back and do it. By the Lesson 2: Linear...

## Principal Components Regression in R: Part 3

May 31, 2016
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by John Mount Ph. D. Data Scientist at Win-Vector LLC In her series on principal components analysis for regression in R, Win-Vector LLC's Dr. Nina Zumel broke the demonstration down into the following pieces: Part 1: the proper preparation of data and use of principal components analysis (particularly for supervised learning or regression). Part 2: the introduction of y-aware...