# 2262 search results for "regression"

## A Shiny App for Passing Bablok and Deming Regression

August 15, 2016
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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...

## Learning Club 05-07: Starting to love rmarkdown (Naive Bayes, Clustering, Linear Regression)

July 27, 2016
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I remember when I had an R course at university I was really not a fan of rmarkdown and knitr. But since I participate in a Learning Club, where people are encouraged to document and present their code, data and results, I started to love it. Prior to that I’ve always documented my assignments at the university either … Continue...

## Yet Another Post on Logistic Regression

July 21, 2016
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Everyday statisticians, analysts and data enthusiasts perform data analysis for different purposes. But when it comes to presenting analyses to wider audience, the good work is not the complex one with big words. It is the one that highlights interesting relations, answers business questions or predict outcomes, and explain all that in the simplest way through data visualization or...

## 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....

## A web interface for regression analysis: Walkthrough

June 18, 2016
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After the quick overview, here is a quick walkthrough to some categorical analysis.Open the app: Here1. Import the data:Here are some homemade data, done with the following R code: set.seed(3467)x=1:400+rnorm(400,0,1)y1=x*2.5+40+rnorm(400,0,50)y2=x*4.5+80+rnorm(400,0,50)group=rep(c('G1','G2'),each=400)x=c(x,x)y=c(y1,y2)DF=data.frame(x=x,y=y,group=group)write.csv(DF,'DF.csv')Click on import data, select your data and set rownames to first column. You should then get a quick overview of the data:

## A web interface for regression analysis: Overview

June 18, 2016
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A Web interface for regression analysis (aka WIfRA) 1.What is it ?Firstly, it was supposed to be a project to learn Shiny and quickly turn into a real project. I wanted to bring data visualisation, regression analysis technique and data engineering to everybody and for no-cost. Basically, this is a point and click UI to do some advanced linear regression...

## 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...