434 search results for "boxplot"

R for Publication by Page Piccinini: Lesson 5 – Analysis of Variance (ANOVA)

June 20, 2016
By
R for Publication by Page Piccinini: Lesson 5 – Analysis of Variance (ANOVA)

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...

Read more »

Venn Diagram Comparison of Boruta, FSelectorRcpp and GLMnet Algorithms

June 18, 2016
By
Venn Diagram Comparison of Boruta, FSelectorRcpp and GLMnet Algorithms

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....

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 Β + Categorical Predictors, Partial and Part Correlation]

June 9, 2016
By
Introduction to R for Data Science :: Session 7 [Multiple Linear Regression Model in R Β + 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 »

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

June 6, 2016
By
Introduction to R for Data Science :: Session 6 [Linear Regression Model in R Β + 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 »

Using caret to compare models

June 2, 2016
By
Using caret to compare models

by Joseph Rickert The model table on the caret package website lists more that 200 variations of predictive analytics models that are available withing the caret framework. All of these models may be prepared, tuned, fit and evaluated with a common set of caret functions. All on its own, the table is an impressive testament to the utility and...

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 »

R for Publication by Page Piccinini: Lesson 1 – R Basics

May 29, 2016
By
R for Publication by Page Piccinini: Lesson 1 – R Basics

Before starting this lesson you should have completed all of the steps in Lesson 0. If you have not, go back and do the lesson now. By the end of this lesson you will be able to: Make an R Project. Commit to Git. Push to Bitbucket. Read in and manipulate data. Make a figure Lesson 1: R...

Read more »

Re-introducing cricketr! : An R package to analyze performances of cricketers

May 14, 2016
By
Re-introducing cricketr! : An R package to analyze performances of cricketers

In this post I re-introduce R package cricketr. I have added 8 new functions to my R package cricketr, available from version cricketr_0.0.13 namely batsmanCumulativeAverageRuns batsmanCumulativeStrikeRate bowlerCumulativeAvgEconRate bowlerCumulativeAvgWicketRate relativeBatsmanCumulativeAvgRuns relativeBatsmanCumulativeStrikeRate relativeBowlerCumulativeAvgWickets relativeBowlerCumulativeAvgEconRate This post updates my earlier post Introducing cricketr:An R package for analyzing performances of cricketrs Yet all experience is an arch wherethro’ Gleams

Read more »

Plotting App for ggplot2 (Part 2)

May 11, 2016
By
Plotting App for ggplot2 (Part 2)

Through this post, I would like to provide an update to my plotting app, which I first blogged about here. The app is available as part of my package RtutoR, which is published on CRAN.(The app is also hosted at shinyapps.io. However, unlike the package version, you would not be able to use your own Plotting App for...

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)