Linear Mixed-Effects Models with R

Learn how to specify, fit, interpret, evaluate and compare estimated parameters with linear mixed-effects models in R.

4.05 (247 reviews)
Udemy
platform
English
language
Math
category
Linear Mixed-Effects Models with R
2,374
students
10.5 hours
content
Aug 2020
last update
$49.99
regular price

What you will learn

Specify an appropriate linear mixed-effects model structure with their own data.

Compare alternative modeling structures and choose the best specification.

Represent, fit, and choose among different, competing correlational structures appropriate to both temporal and spatial pseudo-replicated models.

Validate the "goodness" of the model and the model assumptions.

Represent, estimate, interpret and report on linear mixed-effects model parameters using R software.

Why take this course?

Linear Mixed-Effects Models with R is a 7-session course that teaches the requisite knowledge and skills necessary to fit, interpret and evaluate the estimated parameters of linear mixed-effects models using R software. Alternatively referred to as nested, hierarchical, longitudinal, repeated measures, or temporal and spatial pseudo-replications, linear mixed-effects models are a form of least-squares model-fitting procedures. They are typically characterized by two (or more) sources of variance, and thus have multiple correlational structures among the predictor independent variables, which affect their estimated effects, or relationships, with the predicted dependent variables. These multiple sources of variance and correlational structures must be taken into account in estimating the "fit" and parameters for linear mixed-effects models.

The structure of mixed-effects models may be additive, or non-linear, or exponential or binomial, or assume various other ‘families’ of modeling relationships with the predicted variables. However, in this "hands-on" course, coverage is restricted to linear mixed-effects models, and especially, how to: (1) choose an appropriate linear model; (2) represent that model in R; (3) estimate the model; (4) compare (if needed), interpret and report the results; and (5) validate the model and the model assumptions. Additionally, the course explains the fitting of different correlational structures to both temporal, and spatial, pseudo-replicated models to appropriately adjust for the lack of independence among the error terms. The course does address the relevant statistical concepts, but mainly focuses on implementing mixed-effects models in R with ample R scripts, ‘real’ data sets, and live demonstrations. No prior experience with R is necessary to successfully complete the course as the first entire course section consists of a "hands-on" primer for executing statistical commands and scripts using R.

Screenshots

Linear Mixed-Effects Models with R - Screenshot_01Linear Mixed-Effects Models with R - Screenshot_02Linear Mixed-Effects Models with R - Screenshot_03Linear Mixed-Effects Models with R - Screenshot_04

Reviews

Stefano
December 28, 2023
the professor is good but wastes a lot of time on easy concepts and doesn't stay long enough on more difficult ones. plus there is no conclusive scheme to the course with some pindaric flights
Wayne
October 14, 2022
Course was clearly recorded for some other audience. Content was not properly organized. Lectures jump all over the place. Some needed r packages (sp) were not found in the scripts.
Bathelt
October 13, 2022
Poorly organised. A lot of time is dedicated to reviewing basic usage of R, which should be assumed at the level of the course.
Benjamin
September 27, 2022
This Course is very messy, there is no script so the presenter is jumping around a lot and clearly just reading out and slightly changing the R comments. Either read them out or don't, slightly modifying just make it hard to follow. The presenter almost doesn't seem to know what is coming next in the R script and regularly comments 'oh that isn't necessary' or 'not sure why that is done' which is not reassuring. Many of the same points (mostly about use of R) are made in every video which is really jarring.
Montserrat
August 15, 2022
The instructor does not provide any kind of help related to the course videos. You just watch the videos and be able to understand everything. Lessons are not very clear
Greg
June 1, 2022
No - didn't explain mixed effects well. - I think they just read some code they had gathered over the web at the audience. If you want to find out about mixed effects just skip this course
Chad
May 31, 2022
Ive actually learned a lot from this series. Decent explanations so would like to give it 5 starts. Unfortunately the course is from 2012 the AED package it relies on wont install manually because it seems to need R 3.0. Trying to downgrade to 3.0 fails because of dependencies. Ofc I could probably install them manually but that would be a headache and a pain. Just know you will have some hoops to jump through if you want to actually follow along with the code Upgrade to 3.5 stars because I have really learned quite a lot and have since bought other classes from him
Gary
April 8, 2021
It is a useful course, but the instructor could be better prepared to allow clearer explanation of the concepts. It often appears he is trying to "wing-it"
Denise
January 20, 2021
The course materials are randomly put by the instructor. It is hard to follow through. The instructor could make some efforts to organize the materials properly and make them understandable for students.
Błażej
October 30, 2016
After 50% of course: too much detail on very basic R (R-Studio), too little on LME theory and advanced scripts used along with no explanation. Instructor is following scripts from other books and it feels like something is missing here (at least for someone that didn't read those books)
Joseph
August 22, 2016
Instuctor is good but jumps around. He needs a TA to go through and clean up tbe course. A bit disjointed in places.
David
June 8, 2016
I absolutely love that the instructor provided this content. It's very difficult to get coverage of mixed models in an explanatory video that isn't extremely cursory. I am only rating 3 stars though because of the lack of presentation quality in some sections. This is apparently a recording of a live seminar and the instructor gets a little sloppy with the alignment between what he is showing on screen and what he is saying to the point that it hinders the learner. A little quality control would go a long way. With the timber dataset, he refers to a model that is testing loads as a function of slippage, which seems as though it should be the other way around but the lack of explanation leaves one guessing at the interpretation of results. Overall, I picked up some useful tips for model building and some much needed insight into the interpretation of output.
Nicholas
May 20, 2016
A bit of a slow start for a class on mixed effects models. I guess I would have assumed that anyone taking this course should already have some proficiency in the language basics - or that they'd decide to take a primer course beforehand. The material is useful for someone converting to R, but for those with some experience, the first group of videos can be safely skipped.
Rob
March 25, 2016
Some useful material but waffly and poorly structured. Many sessions are clearly just recordings of lectures given to classes, including answering questions from the students. There are some errors, some material is repeated and a lot of the material is taken from Zuur or from Crawley, including copyright violations in the form of sections of text taken from these books and put in the scripts (e.g. the hierarchical variance components description is just lifted from Crawley). Overall rather disappointing and you'd probably do better to just buy a copy of Zuur.
Eugene
February 28, 2016
This course is a good companion to Dr Alain mixed model in Rbook..but its still at a pretty high level. For gentler introduction, you may want to consider reading his introduction to GLM/GLLMM in R prior to starting this course..

Charts

Price

Linear Mixed-Effects Models with R - Price chart

Rating

Linear Mixed-Effects Models with R - Ratings chart

Enrollment distribution

Linear Mixed-Effects Models with R - Distribution chart

Related Topics

591852
udemy ID
8/24/2015
course created date
11/22/2019
course indexed date
Bot
course submited by