323 search results for "ANOVA"

R for Publication by Page Piccinini: Lesson 6, Part 2 – Linear Mixed Effects Models

July 4, 2016
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R for Publication by Page Piccinini: Lesson 6, Part 2 – Linear Mixed Effects Models

In today’s lesson we’ll continue to 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 2 of a two part lesson. I’ll be taking for granted that you’ve completed Lesson 6, Part 1, so if you haven’t done that Lesson 6, Part...

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Simulation and power analysis of generalized linear mixed models

June 28, 2016
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Simulation and power analysis of generalized linear mixed models

Simulation and power analysis of generalized linear mixed models Brandon LeBeau University of Iowa Overview (G)LMMs Power simglm package Demo Shiny App! Linear Mixed Model (LMM) Power Power is the ability to statistica...

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

June 26, 2016
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R for Publication by Page Piccinini: Lesson 6, Part 1 – Linear Mixed Effects Models

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...

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

June 13, 2016
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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...

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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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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...

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R profiling

June 5, 2016
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R profiling

Profiling in R R has a built in performance and memory profiling facility: Rprof. Type  into your console to learn more. The way the profiler works is as follows: you start the profiler by calling Rprof, providing a filename where the profiling data should be stored you call the R functions that you want to analyse you The post

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Absence of evidence is not evidence of absence: Testing for equivalence

May 20, 2016
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Absence of evidence is not evidence of absence: Testing for equivalence

When you find p > 0.05, you did not observe surprising data, assuming there is no true effect. You can often read in the literature how p > 0.05 is interpreted as ‘no effect’ but due to a lack of power the data might not be surprising if there was...

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Bike Rental Demand Estimation with Microsoft R Server

May 10, 2016
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Bike Rental Demand Estimation with Microsoft R Server

by Katherine Zhao, Hong Lu, Zhongmou Li, Data Scientists at Microsoft Bicycle rental has become popular as a convenient and environmentally friendly transportation option. Accurate estimation of bike demand at different locations and different times would help bicycle-sharing systems better meet rental demand and allocate bikes to locations. In this blog post, we walk through how to use Microsoft...

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Introduction to R for Data Science :: Session 1

April 30, 2016
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Introduction to R for Data Science :: Session 1

Welcome to Introduction to R for Data Science Session 1! The course is co-organized by Data Science Serbia and Startit. You will find all course material (R scripts, data sets, SlideShare presentations, readings) on these pages. Lecturers dipl. ing Branko Kovač, Data Analyst at CUBE, Data Science Mentor at Springboard, 

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The five element ninjas approach to teaching design matrices

April 25, 2016
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The five element ninjas approach to teaching design matrices

Design matrices unite seemingly disparate statistical methods, including linear regression, ANOVA, multiple regression, ANCOVA, and generalized linear modeling. As part of a hierarchical Bayesian modeling course that we offered this semester, we wanted our students to learn about design matrices to facilitate model specification and parameter interpretation. Naively, I thought that I could spend a few minutes in class reviewing matrix...

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