Linear Mixed-Effects Models with R
Linear Mixed-Effects Models with R. Learn how to specify, fit, interpret, evaluate and compare estimated parameters with linear mixed-effects models in R.
The name of this course is Linear Mixed-Effects Models with R. The knowledge you will get with this indescribable online course is astonishing. Learn how to specify, fit, interpret, evaluate and compare estimated parameters with linear mixed-effects models in R..
Not only will you be able to deeply internalize the concepts, but also their application in different fields won’t ever be a problem. The instructor is Geoffrey Hubona, Ph.D., one of the very best experts in this field.
Description of this course: Linear Mixed-Effects Models with R
Course Description 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.
Requirements of this course: Linear Mixed-Effects Models with R
What are the requirements? Students will need to install the no-cost R console and the no-cost RStudio application (instructions and provided).
What will you learn in this course: Linear Mixed-Effects Models with R?
What am I going to get from this course? 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.
Target audience of this course: Linear Mixed-Effects Models with R
Who is the target audience? Students do NOT need to be knowledgeable and/or experienced with R software to successfully complete this course. This course is useful for graduate students in business, the social sciences, education fields, statistics, mathematics and other disciplines who would like to learn about and become proficient estimating and interpreting linear mixed-effects model parameters and values. This course is useful to practicing quantitative analysis professionals, such as research scientists and other data analytic professionals who use linear modeling techniques on the job.
Geoffrey Hubona, Ph.D.