2047 search results for "Regression"

A Shiny App for Passing Bablok and Deming 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...

Read more »

Performing Principal Components Regression (PCR) in R

July 20, 2016
By
Performing Principal Components Regression (PCR) in R

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.

Read more »

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

June 21, 2016
By
Introduction to R for Data Science :: Session 8 [Intro to Text Mining in R, ML Estimation + Binomial Logistic Regression]

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....

Read more »

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

June 13, 2016
By
R for Publication by Page Piccinini: Lesson 4 – Multiple Regression

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...

Read more »

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

June 9, 2016
By
Introduction to R for Data Science :: Session 7 [Multiple Linear Regression Model in R  + Categorical Predictors, Partial and Part Correlation]

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...

Read more »

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

June 9, 2016
By
Why you should read Nina Zumel’s 3 part series on principal components analysis and regression

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...

Read more »

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

June 9, 2016
By
R for Publication by Page Piccinini: Lesson 3 – Logistic Regression

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...

Read more »

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

June 6, 2016
By
Introduction to R for Data Science :: Session 6 [Linear Regression Model in R  + EDA, and Normality Tests]

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

Read more »

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

June 2, 2016
By
R for Publication by Page Piccinini: Lesson 2 – Linear Regression

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...

Read more »

Principal Components Regression in R: Part 3

May 31, 2016
By
Principal Components Regression in R: Part 3

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...

Read more »

Sponsors

Mango solutions



RStudio homepage



Zero Inflated Models and Generalized Linear Mixed Models with R

Quantide: statistical consulting and training

datasociety

http://www.eoda.de





ODSC

ODSC

CRC R books series





Six Sigma Online Training









Contact us if you wish to help support R-bloggers, and place your banner here.

Never miss an update!
Subscribe to R-bloggers to receive
e-mails with the latest R posts.
(You will not see this message again.)

Click here to close (This popup will not appear again)