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lmerTest Package: Tests in Linear Mixed Effects Models

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TLDR
The lmerTest package extends the 'lmerMod' class of the lme4 package, by overloading the anova and summary functions by providing p values for tests for fixed effects, and implementing the Satterthwaite's method for approximating degrees of freedom for the t and F tests.
Abstract
One of the frequent questions by users of the mixed model function lmer of the lme4 package has been: How can I get p values for the F and t tests for objects returned by lmer? The lmerTest package extends the 'lmerMod' class of the lme4 package, by overloading the anova and summary functions by providing p values for tests for fixed effects. We have implemented the Satterthwaite's method for approximating degrees of freedom for the t and F tests. We have also implemented the construction of Type I - III ANOVA tables. Furthermore, one may also obtain the summary as well as the anova table using the Kenward-Roger approximation for denominator degrees of freedom (based on the KRmodcomp function from the pbkrtest package). Some other convenient mixed model analysis tools such as a step method, that performs backward elimination of nonsignificant effects - both random and fixed, calculation of population means and multiple comparison tests together with plot facilities are provided by the package as well.

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TGFβ drives immune evasion in genetically reconstituted colon cancer metastasis

TL;DR: Increased TGFβ in the tumour microenvironment represents a primary mechanism of immune evasion that promotes T-cell exclusion and blocks acquisition of the TH1-effector phenotype, and immunotherapies directed against TGF β signalling may have broad applications in treating patients with advanced colorectal cancer.
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Evaluating significance in linear mixed-effects models in R

TL;DR: Results of simulations show that the two most common methods for evaluating significance, using likelihood ratio tests and applying the z distribution to the Wald t values from the model output (t-as-z), are somewhat anti-conservative, especially for smaller sample sizes.
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References
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Journal ArticleDOI

Fitting Linear Mixed-Effects Models Using lme4

TL;DR: In this article, a model is described in an lmer call by a formula, in this case including both fixed-and random-effects terms, and the formula and data together determine a numerical representation of the model from which the profiled deviance or the profeatured REML criterion can be evaluated as a function of some of model parameters.
Book

ggplot2: Elegant Graphics for Data Analysis

TL;DR: This book describes ggplot2, a new data visualization package for R that uses the insights from Leland Wilkisons Grammar of Graphics to create a powerful and flexible system for creating data graphics.
Book

An R Companion to Applied Regression

Sanford Weisberg, +1 more
TL;DR: This tutorial jumps right in to the power of R without dragging you through the basic concepts of the programming language.
Journal ArticleDOI

Least-Squares Means: The R Package lsmeans

TL;DR: The lsmeans package (Lenth 2016) provides a simple way of obtaining least-squares means and contrasts thereof and supports many models fitted by R (R Core Team 2015) core packages that fit linear or mixed models.
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