# 535 search results for "boxplot"

## WifRA: a quick walkthrough

June 26, 2016
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After the quick overview, here is a quick walkthrough to some categorical analysis. Open the app: Here 1. 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

## R for Publication by Page Piccinini: Lesson 6, Part 1 – Linear Mixed Effects Models

June 26, 2016
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In today’s lesson we’ll learn about linear mixed effects models (LMEM), which give us the power to account for multiple types of effects in a single model. This is Part 1 of a two part lesson. I’ll be taking for granted some of the set-up steps from Lesson 1, so if you haven’t done that Lesson 6, Part...

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

June 20, 2016
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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...

## Venn Diagram Comparison of Boruta, FSelectorRcpp and GLMnet Algorithms

June 18, 2016
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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....

## A web interface for regression analysis: Walkthrough

June 18, 2016
By

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:

## 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...

## Scraping and Plotting Minneapolis Property Prices | RSelenium, ggmap, ggplots

June 8, 2016
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I recall having once scraped data from a Malaysian property site so that I may be able to plot the monthly rental rates for a specific neighborhood in Selangor. This time I thought it might be interesting to try and … Continue reading →

## 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...

## Using caret to compare models

June 2, 2016
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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...